November 2024

18
Nov
Sales Forecasting Methods Process, Tools, and Factors
B2B Sales  ·  Sales
How Edtech Companies Are Doing Highly Accurate Sales Forecasting

Sales forecasting is one of the most critical tools that can help you understand the future revenue trajectory of your Edtech business. As the demand for educational technology continues to grow, predicting how many courses will be sold, when students will enrol, and how much revenue will be generated is more important than ever. 

With advanced technology at your disposal, including AI-powered tools, Edtech companies can now achieve highly accurate sales forecasting, enabling better planning, decision-making, and growth.

But how exactly are Edtech companies nailing this crucial task? 

53% of sales professionals who implemented AI use it for sales forecasting specifically, according to HubSpot research. With predictive analytics, sales teams can assess future revenue with 96% accuracy. These stats show how transformative accurate forecasting can be for your Edtech business.

In this blog, we’ll break down the strategies Edtech is using and showcase case studies to help you understand the tangible benefits. Let’s get started!

The Importance of Accurate Sales Forecasting in Edtech

Accurate sales forecasting is crucial for Edtech companies, given their unique sales cycles that align with academic calendars and enrollment periods. These cycles bring fluctuating demand, often influenced by factors like exam seasons, academic deadlines, and institutional purchasing schedules. Without precise forecasting, Edtech companies risk overestimating growth—leading to resource waste—or underestimating demand, resulting in missed revenue opportunities.

Did you know that, according to a recent study by Gartner, businesses that use predictive analytics can improve their sales forecast accuracy by up to 50%? For Edtech companies, this means avoiding costly miscalculations and ensuring that resources are allocated to meet the actual demand.

Accurate forecasting enables Edtech companies to streamline operations and capitalise on peak sales periods, such as back-to-school seasons or major exam preparation windows. 

Here are some effective methods for enhancing sales forecasting in the Edtech sector:

  • Leveraging Historical Data: Analysing past sales data can reveal patterns in demand fluctuations tied to academic cycles, helping predict future sales more accurately.
  • Harnessing AI for Forecasting: AI-driven forecasting tools can analyse complex data and predict demand with greater accuracy, adapting to changes in the market and academic landscape.

By integrating these approaches, Edtech companies can improve decision-making, optimise resource allocation, and better anticipate the needs of their customer base.

Related: What best Edtech counsellors do on sales calls? Zipteams analyses thousands of education counselling calls daily and provides sales teams with the insights to make their calls learner-friendly.  Check out our LinkedIn post to learn 5 things the best education counsellors do on their sales calls.

Leveraging Historical Data to Improve Forecast Accuracy

If you’re looking for a reliable starting point, historical data is your best friend. Past enrollment trends, purchasing patterns, and customer behaviour are all treasure troves of information that can significantly improve your sales forecast accuracy.

Key Considerations for Using Historical Data:

  • Seasonal Trends: Analyse previous enrollment or purchasing peaks during academic periods.
  • Customer Behaviour: Identify patterns in student or institutional purchases over time.
  • Enrollment Windows: Predict demand based on academic deadlines or application cycles.

AI and Machine Learning for Predictive Analytics

According to HubSpot research,  with predictive analytics, sales teams can assess future revenue with 96% accuracy. Artificial Intelligence (AI) and machine learning have revolutionised sales forecasting by enabling Edtech companies to analyse vast amounts of data and generate predictive insights. 

These tools allow organisations to forecast future revenue more accurately by identifying patterns that may not be immediately obvious through traditional methods.

Benefits of AI in Sales Forecasting:

  • Real-Time Insights: AI tools continuously learn from ongoing sales activities, enabling Edtech companies to adjust their forecasts in real time.
  • Lead Scoring: Machine learning algorithms help prioritise leads by identifying which prospects are more likely to convert based on historical data.
  • Data Integration: AI models can aggregate data from multiple sources such as CRM systems and marketing platforms, providing a holistic view of the sales funnel.

With machine learning algorithms, you can automatically score leads, prioritise sales efforts, and adjust forecasts based on the latest data.

For example, Infinity Learn leveraged Zipteams’ AI-powered conversational intelligence tools to identify high-propensity leads. By analysing customer interactions and predicting which leads were more likely to convert, their sales teams could follow up more effectively, improving overall sales outcomes and the accuracy of their forecasts.

Managing Sales Pipelines for Short-Term Forecasting

While long-term forecasting is critical for strategic planning, Edtech companies also need to manage their sales pipelines effectively to generate accurate short-term forecasts. Sales pipeline management involves tracking the progress of deals and estimating when they are likely to close.

Key Steps for Effective Pipeline Management:

  • Deal Stages: Monitor each opportunity’s progression through the sales funnel, from prospecting to closing.
  • Win Probability: Assign probabilities to deals based on historical success rates at each stage.
  • Lead Qualification: Use tools to identify high-quality leads that are more likely to close.

You probably already know that tracking deal progression is key. But did you know that companies with well-managed sales pipelines can see a 28% boost in revenue growth as per Harvard Business Review?

By monitoring stages like deal initiation, qualification, and negotiation, your Edtech company can better predict when and how revenue will come in.

Also Read: Importance of CRM System for Business Growth in Edtech/Online Education Sales

Combining Qualitative Insights with Quantitative Data

While data-driven insights are vital for forecasting, qualitative inputs from sales teams, executives, and market experts can add an important layer of context. Many Edtech companies combine qualitative forecasting methods, such as expert opinions or focus groups, with quantitative data to refine their predictions.

How Qualitative Methods Help:

  • Market Trends: Gain insights into emerging trends that may not yet be reflected in the data.
  • Expert Opinions: Sales teams often have on-the-ground knowledge of prospects and can provide input that enhances data-driven models.
  • Competitor Behaviour: Use qualitative insights to predict how competitor actions might impact demand.

It’s easy to focus on numbers and analytics, but don’t forget the value of qualitative insights. Sometimes, the best forecast improvements come from the ground by listening to your sales team, gathering market sentiment, and understanding the nuances of customer behaviour. By combining qualitative insights with your quantitative data, you get a more comprehensive view of your market.

The Benefits of Accurate Sales Forecasting in Edtech

Now that you know how to improve your sales forecasting, let’s talk about why it’s so important. Accurate forecasting isn’t just a “nice-to-have”. It’s essential for making informed decisions that lead to growth.

Key Benefits:

  • Optimised Resource Allocation: Knowing exactly how many sales you can expect allows you to allocate staff, marketing budgets, and other resources more effectively.
  • Improved Customer Experience: You can better serve your students or institutional clients by anticipating demand and ensuring your resources are aligned with their needs.
  • Stronger Financial Planning: Accurate forecasting allows you to plan your finances with confidence, helping you secure investments, budget for growth, and mitigate risks.

Zipteams’ Impact on Edtech Sales Forecasting

Zipteams has become a trusted partner for several leading Edtech companies, helping them boost their sales forecasting accuracy and lead conversion rates. Let’s look at how companies like Coding Ninjas, Infinity Learn, and Lead School leveraged Zipteams’ AI-powered conversational tools.

  • Coding Ninjas is India’s most recommended code learning platform with 80,000+ alumni working in 95/108 Unicorns in India. With Zipteams, they improved their lead conversion by using data to anticipate when students were most likely to enrol, helping them focus on high-demand periods.
  • Infinity Learn, an education platform focusing on AI-driven learning, used Zipteams’ lead scoring system to prioritise high-potential leads. This allowed their sales teams to concentrate on the most promising prospects, increasing conversion rates and the accuracy of their sales forecasts
  • LEAD School, India’s largest and fastest-growing School Edtech company, tapped into Zipteams’ real-time lead qualification, enabling them to fine-tune their sales forecasts and better manage their sales funnel​.

Zipteams offers a cutting-edge solution for Edtech companies looking to improve their sales forecasting accuracy. By combining AI-powered tools, real-time lead qualification, and meeting rooms with conversational intelligence, Zipteams helps you streamline your sales processes and make data-driven decisions. Whether it’s prioritising high-conversion leads, tracking customer interactions, or optimising your sales pipeline, Zipteams empowers you to forecast with confidence.

Explore how Zipteams’ AI-powered solutions can transform your sales operations and drive growth in your Edtech company. Visit Zipteams to learn more and get started today!

by Aakriti


18
Nov
A Guide to Improving Forecast Accuracy with AI
Generative AI
A Guide to Improving Forecast Accuracy with AI

Forecasting accuracy is essential in any business that values strategic planning and data-driven decisions. Despite its importance, traditional forecasting models often lack the precision needed in today’s complex market.  

Using AI-driven predictive analysis improves forecast accuracy significantly by uncovering hidden data patterns and allowing real-time adjustments.

In this guide, we’ll explore the essential steps to harnessing AI for accurate forecasting, from data preparation to tracking key metrics. By implementing these strategies, you’ll enhance your ability to predict demand accurately and make smarter business decisions.

Forecast Accuracy and Its Importance

Improving forecast accuracy means aligning your predictions as closely as possible to actual outcomes. Research from McKinsey shows that companies that use data-backed forecasting improve their ROI by 15-20% and are more profitable than competitors​. Accurate forecasts are crucial for optimising resources, improving inventory management, and enhancing overall sales strategy.

Why Accurate Forecasts Matter:

  • Resource Planning: Accurate forecasts enable better allocation of inventory and staffing resources.
  • Strategic Alignment: With precise forecasts, you can align your sales goals and marketing strategies effectively.
  • Revenue Management: Well-predicted sales volumes support more stable cash flow and prevent stock shortages.

Pro Tip: Set a benchmark accuracy rate to gauge your forecasting progress. Many mature companies target forecasts within a 5% margin of error​.

Common Forecasting Accuracy Challenges

Even with structured forecasting methods, many businesses encounter challenges that impact accuracy. Recognising and addressing these issues can help improve the reliability of forecasts.

  1. Reliance on Assumptions and Gut Feelings

Forecasts often suffer when they are built on assumptions or personal intuitions rather than data. Decisions based on “gut feelings” may overlook key factors or introduce bias, leading to inaccurate predictions. While intuition can be helpful in some contexts, relying solely on it in forecasting can cause significant deviations from actual outcomes.

  1. Data Integrity Issues from Disconnected Tools

Disconnected systems such as separate CRM, ERP, and analytics tools often result in data inconsistencies. When data is not consolidated, it creates gaps or duplicates, making it difficult to produce a clear, unified view of trends and customer behaviours. 

This lack of data integrity undermines forecasting accuracy, as incomplete or conflicting data points compromise the final prediction.

Solution: Integrate your AI forecasting tools with core systems like CRM and ERP to establish a single source of truth. This integration prevents data duplication and inconsistency, improving forecast accuracy.

  1. Overlooking Historical Data for Predictions

Historical data is a valuable asset in forecasting, as it reveals trends and seasonality that inform future sales predictions.

However, many organisations overlook or underutilise past data when predicting future sales, especially if they rely on basic or outdated models. Ignoring this valuable resource results in forecasts that lack depth and may miss predictable fluctuations or patterns.

How AI Can Address These Challenges

AI offers a comprehensive solution to these forecasting challenges. By analysing historical data, consolidating information from multiple systems, and generating data-driven insights, AI helps eliminate reliance on assumptions and enhances data integrity. 

AI-powered forecasting tools can adapt to dynamic market conditions, providing accurate, real-time forecasts that improve decision-making and support long-term planning.

Pro Tip: Use AI models that are specifically designed for historical trend analysis, such as time-series models, to incorporate valuable past data into your forecasts. This ensures your predictions are grounded in real patterns rather than speculative projections.

AI Techniques for Improving Forecast Accuracy

AI offers a suite of powerful tools to enhance forecast accuracy. Each technique has distinct advantages that suit various business needs.

  • Machine Learning Algorithms: Machine learning models like time-series analysis and regression are particularly effective for sales forecasting. These models continuously learn from historical data to improve their predictions over time.
  • Predictive Analytics: Predictive analytics tools analyse vast amounts of data to project future trends, allowing for real-time adjustments. AI models also monitor market factors and alert you to any changes, enabling proactive decision-making.
  • Natural Language Processing (NLP): With NLP, AI can capture and analyse customer feedback, sentiment, and preferences. This is particularly valuable for industries where customer sentiment is a leading indicator of demand changes.

Pro Tip: Incorporate Zipteams in your sales call to automatically capture and analyse customer sentiment, enhancing forecasting accuracy. 

Integrating AI into the Sales Process

Incorporating AI into your sales process helps ensure your forecasts are not only accurate but also actionable.

  • CRM and ERP Integration: Integrate AI with your CRM and ERP systems to create a unified data flow that reflects all sales interactions and inventory updates in real-time. This ensures your forecasts are based on the latest information.
  • Pipeline Analysis: Use AI tools to analyse your sales pipeline and assign probability scores to deals. This helps your team predict potential revenue more accurately and focus on high-priority leads.
  • Customer Segmentation: AI can segment your customers by buying behaviours, regions, or profiles. These segments provide valuable insights into demand, helping you create tailored forecasts for different customer groups.

Related: Why Generative AI Adoption is one of the Must B2B CRM Strategies for Business Growth

Steps to Implement AI for Forecast Accuracy

Achieving accuracy with AI involves several tactical steps. Follow these guidelines to ensure a smooth and effective implementation.

Step 1: Build a High-Quality Data Foundation

Collect data from reliable sources such as CRM, ERP, and POS systems, and cleanse it to ensure consistency. According to BoostUp, the better your data quality, the more precise your forecast.

Action: Schedule regular data cleansing sessions to remove duplicates and inaccuracies. This builds a solid foundation for accurate AI forecasting.

Tip: Use automated data-cleansing tools to speed up the process and maintain data integrity across departments.

Step 2: Choose the Right AI Model for Your Needs

AI offers multiple forecasting models, from time-series analysis to neural networks. Select one based on the complexity and specific goals of your business.

Example: If your data includes seasonal trends, a time-series model with seasonality adjustments may work best. For more complex data, consider using a neural network.

Step 3: Enable Real-Time Predictive Analytics

AI models can process real-time data, continuously adjusting forecasts to reflect current trends. Predictive analytics enables your team to react to changes instantly rather than waiting for periodic updates.

Action: Implement real-time predictive tools that monitor and notify your team of significant forecast changes.

Pro Tip: Automate alerts for forecast deviations. This ensures your team is always aware of shifts and can take immediate action to adjust strategies.

Step 4: Segment Forecasts for Enhanced Accuracy

Dividing your forecast by segments, whether by region, product line, or customer group, allows you to create more tailored and accurate projections.

Action: Develop individual KPIs for each segment to monitor specific performance trends. This makes it easier to refine your strategy based on real-time results.

Step 5: Monitor and Track Forecast Accuracy Metrics

Tracking metrics such as Mean Absolute Percentage Error (MAPE) and forecast bias can help you measure the accuracy of your forecasts.

Pro Tip: Visualise accuracy metrics with dashboards, which provide an at-a-glance view of performance and allow you to identify any recurring issues.

Best Practices for Enhancing Forecasting Accuracy

Implementing best practices ensures that your AI forecasting process is both efficient and reliable.

  • Cultivate a Data-Driven Culture: Encourage your team to prioritise data integrity and understand the importance of accurate forecasting.
  • Regularly Retrain Models: AI models need periodic retraining to maintain accuracy. This is especially important as your business and market conditions evolve.​
  • Scenario-Based Forecasting: Use AI to model different scenarios (e.g., best-case, worst-case). This helps you plan for various outcomes and adjust your strategy accordingly.

Tip: Validate your models frequently to ensure your AI remains accurate and effective. Schedule model checks every quarter to account for seasonal trends and market shifts.

Real-World Applications of AI in Forecasting

Many industry leaders, including Walmart, Amazon, and Unilever, have turned to AI-driven forecasting to achieve remarkable improvements in their ability to predict demand accurately. By leveraging AI’s advanced analytics and real-time data processing, these companies can make more informed decisions and stay ahead of market shifts.

1. Walmart: Enhancing Inventory and Demand Forecasting

Walmart, a leader in retail innovation, has adopted AI to improve the accuracy of its demand forecasting and inventory management. 

By using machine learning models to analyse data from its point-of-sale (POS) systems, Walmart can make precise predictions for individual stores and regions. This capability reduces the chances of stockouts and minimises excess inventory, leading to a more efficient supply chain and better customer service.

2. Amazon: Personalised Demand Forecasting for Customer-Centric Operations

Amazon uses AI and machine learning to forecast demand at a highly granular level. By analysing customer behaviour, browsing history, and social media trends, Amazon’s forecasting models can predict demand for specific products by customer segment.

This personalisation enables Amazon to keep in-demand products stocked, ensuring customer satisfaction and streamlined inventory management.

3. Unilever: Using AI for Data-Driven Sales and Demand Forecasting

Unilever applies AI to enhance its demand forecasting and supply chain planning across a diverse product portfolio. Its AI models analyse various data sources—such as historical sales, weather, and economic indicators—to predict demand accurately, particularly for seasonal products. For instance, Unilever adjusts forecasts for products like ice cream by analysing temperature and weather trends, optimising its production and distribution.

These examples highlight how AI-driven forecasting allows companies to move beyond basic predictions to real-time, data-informed decisions that enhance their operations. By implementing AI, Walmart, Amazon, and Unilever have not only improved forecast accuracy but also optimised their inventory management, reduced costs, and provided a better customer experience. 

Like every new tool, AI comes with its own challenges. Identifying the challenges and overcoming them will be an advantage against your competitors.

Overcoming Challenges in AI-Driven Forecasting

Even with AI, forecasting isn’t without its challenges. Here’s how to tackle common obstacles:

  • Data Bias: Bias in training data can lead to skewed results. Use diverse, representative datasets to ensure fair and unbiased predictions.
  • Integration Issues: Integrating AI with existing systems can be complex. Work closely with IT to ensure smooth integration with CRM and ERP systems​. 
  • Skill Development: Upskill your team to help them understand AI outputs and make data-driven decisions based on these insights.

AI implementation is not successful unless your team is able to extract its potential. Some basic tools and resources will help your team to get AI in the right direction.

Key Tools for AI-Driven Forecast Accuracy

Choosing the right tools is essential for successful AI-driven forecasting. Consider platforms that specialise in forecast accuracy.

  • AI Platforms: Solutions like Zipteams are designed for predictive analytics and can integrate with CRM for enhanced accuracy​
  • Visualisation Tools: Dashboards like Tableau or Power BI make it easy to track forecast metrics and share insights with teams.

Pro Tip: Start with a small-scale AI tool and expand as your team gains confidence and experience with the platform.

Future Trends in AI for Forecast Accuracy

AI continues to evolve, offering even more advanced capabilities for forecasting:

  • Generative AI for Scenario Planning: Generative AI can create multiple forecast scenarios, allowing you to prepare for diverse market conditions.
  • Enhanced Sentiment Analysis: Advanced sentiment analysis will let AI capture deeper insights into customer intent, improving demand forecasts.

How Zipteams Drives Forecasting Accuracy

Sales forecasting is only as good as the data that powers it. Zipteams‘ AI-driven platform transforms everyday sales conversations into predictive insights, enhancing forecast accuracy through real-time data capture and intelligent analysis.

Three core capabilities make Zipteams particularly valuable for sales forecasting:

  • Intent Recognition & Scoring: The platform analyses conversation patterns to quantify buying intent, helping teams predict deal outcomes with greater precision. By scoring prospect interactions, sales leaders can better estimate which opportunities are likely to close, reducing forecast variance.
  • BANT Integration: Through automated capture of Budget, Authority, Need, and Timeline signals during calls, Zipteams standardises qualification data. This structured approach to opportunity assessment leads to more reliable pipeline forecasts by ensuring consistent evaluation criteria across all deals.
  • Sentiment-Based Analytics: Moving beyond traditional metrics, Zipteams factors in customer sentiment to predict deal health. This emotional intelligence layer helps teams identify at-risk opportunities early, allowing for more accurate pipeline adjustments and reduced forecast surprises.

Supporting these core features is a robust reporting infrastructure that translates conversational insights into actionable forecasting data, enabling sales leaders to make informed decisions about their pipeline and future revenue projections.

Integrate Zipteams to your sales forecast today and expand your sales forecast accuracy.

by Aakriti


18
Nov
How AI Tools Improve Productivity in the Workplace
Generative AI
How AI Tools Improve Productivity in the Workplace

AI is shaking things up at work, and many wonder what’s next. Maybe you’ve heard whispers around the office about jobs disappearing or seen headlines about robots taking over. It’s enough to make anyone a little nervous.

But here’s the thing: AI isn’t just about replacing humans. It’s changing the game in some pretty cool ways. Think of it like getting a super-smart assistant who can handle the boring stuff, freeing you up to focus on what you do best. From helping you breeze through your to-do list to sparking new ideas you never knew you had, AI tools are transforming how we work. 

A recent survey by The Conference Board revealed that over half of employees already use AI. Of those AI-savvy workers, 63% reported that generative AI has increased their productivity. AI isn’t just a futuristic concept anymore. It’s a powerful ally in our daily work lives, helping us do more in less time and with less stress.

Adopting AI is key to staying ahead, not something to sit on the sidelines for. In this blog, we’ll explore how AI tools are transforming the workplace while increasing productivity, diving into practical examples and real-world strategies for using AI. 

The Game-Changing Benefits of AI in the Workplace

AI isn’t just a buzzword anymore. It’s transforming how we work. Let’s explore the key ways AI is making our work lives more productive and enjoyable.

1. Supercharging Productivity

AI is like having a tireless assistant who never needs a coffee break. It takes care of repetitive tasks, freeing up your time for more important work. The result? You get more done without burning the midnight oil.

✔ Automates routine tasks

✔ Speeds up data processing

✔ Reduces human error

2. Improved Decision-Making

AI-powered analytics tools process vast amounts of data quickly, providing insights that support informed decision-making. They crunch numbers faster than any human could, giving you insights to stay ahead of the curve.

3. Customer Engagement 

AI is revolutionising how businesses connect with customers. From chatbots that never sleep to personalised recommendations that feel like mind-reading, AI is helping create experiences that keep customers coming back for more.

4. Employee Experience and Well-Being

By taking the struggle out of daily tasks, AI is making work more enjoyable. When employees can focus on meaningful work, job satisfaction goes up, and increases productivity.

Now that we’ve covered the perks, let’s zoom in on how AI is shaking things up in our actual workspaces.

AI in the Workspace

AI is now an essential part of many workplaces, reshaping how tasks are handled and how teams collaborate. From daily operations to long-term strategic planning, AI is transforming various aspects of the work environment.

  1. Automation: Repetitive tasks like scheduling meetings and note-taking! Consider them handled. AI-driven automation is tackling everything from data entry to report generation, giving you back hours in your day.
  2. Bridging the Remote Work Gap: In our new world of hybrid work, AI is the glue holding teams together. Smart scheduling, virtual assistants, and collaborative tools are making distance irrelevant when it comes to teamwork.
  3. Workflow Optimisation: AI doesn’t just do tasks. It helps you work more efficiently. By analysing work patterns, AI can suggest ways to streamline your day, making you more productive without extra stress.

Let’s get personal for a moment. How is AI touching our day-to-day work lives? More than you might think.

A LinkedIn collaborative articles collection on Struggling to integrate AI into your workflow? features expert comments from the LinkedIn community. It is an informative post worth checking out if you are to integrate AI into your workspace.

AI in Daily Tasks

Beyond the workplace, AI has found practical applications in everyday tasks, making daily life more manageable.

Administrative Tasks 

Remember the last time you had to schedule a meeting with multiple people? Or sort through hundreds of emails? AI tools are taking these small but time-consuming tasks off our plates.

AI can handle simple but time-consuming tasks like scheduling appointments, sending reminders, and even organising files, all while reducing human error.

Editing and Drafting

Whether you’re crafting an important email or putting together a presentation, AI writing tools are like having a top-notch editor on standby. They’ll catch your typos, suggest better phrasing, and even help you strike the right tone, making it easier to produce polished content quickly.

Brainstorming

Stuck on a problem? AI brainstorming tools are like having a room full of creative geniuses at your fingertips. They can help you explore new angles, make unexpected connections, and spark ideas you might never have thought of on your own.

Since its inception, AI has marked its presence in many business sectors. The sales sector is the leading playground where AI is revolutionising the way sales teams sell and close deals. Let’s explore the common use case of AI integration in sales.

Industry-Based Use Case: AI in Sales

AI is transforming sales processes, offering new ways to understand and engage with customers. AI has found many applications in sales, where it helps drive efficiency and improve outcomes. Here are three key use cases:

Predictive Analytics

What if you know which leads are hot and which are not before you even pick up the phone? AI analyses data from various sources to predict which prospects are most likely to convert, helping you focus your energy where it counts.

✔ AI-powered systems analyse customer data to predict future buying behaviours.

✔ Helps sales teams target the right prospects at the right time.

✔  Right prediction improves lead conversion rates and helps optimise marketing efforts.

The success of Netflix with Predictive Analytics 

Netflix, the leading entertainment company, transformed the ways for media consumption for their multimillion customers around the world. By strategically implementing predictive analytics of AI, Netflix improved viewer experiences.

Netflix developed an advanced system for content recommendation, analysing user behaviour and preferences to provide customised suggestions. With machine learning models, Netflix was able to predict customer churns and create retention strategies. Predictive analytics enabled content investments, enhancing Netflix’s ability to produce and acquire hit shows and movies across the world

Hyper-personalisation

As per a report, 80% of business leaders find personalisation boosts customer spending with an average uptick of 38%. With generative and predictive capabilities, AI can hyper-personalise your customer engagement in real-time.

AI-powered CRM systems can analyse customer data to suggest the perfect time, channel, and message for each interaction. AI chatbots engage with website visitors in real-time, answering queries and providing personalised product recommendations, all while gathering valuable data about customer preferences.

Hyper-personalization for Car buying: A Honda Story

Honda, the leading car manufacturer, simplified customer onboarding through simple personalised videos. After purchasing a car, the customer will receive a video outlining important details of the agreement like a payment plan, monthly costs, and payment schedules. This hyper-personlisation enabled customers to skip reading manuals and long agreements.

Sales Forecasting 

AI models can analyse historical sales data to predict future performance trends, helping companies adjust their strategies accordingly. This enables more accurate forecasting, inventory planning, and budgeting.

While AI brings significant benefits, its adoption isn’t without challenges.

Challenges of Adopting AI in the Workplace

Understanding the challenges of AI can prepare you well for its implementation in your work. Mitigating these challenges ahead can strategically help you extract the potential of AI tools.

  • Cost of Implementation: AI solutions, especially those customised for specific business needs, can be expensive to deploy. Many organisations face financial constraints when trying to integrate AI.
  • Resistance to Change: Employees may resist the implementation of AI, fearing job loss or being replaced by machines. Overcoming this resistance often requires clear communication and training programs to demonstrate how AI complements, rather than replaces, human work.
  • Integration Complexity: Incorporating AI into existing technological infrastructure can be technically challenging. Consider AI tools with hassle-free integration to continue the workflow.
  • Data Privacy and Security: AI systems rely on vast amounts of data, raising concerns about how that data is stored, processed, and protected. Ensuring robust data security practices is essential when adopting AI technologies.

Zipteams: Boosting Productivity with Smart Sales Tools

Zipteams provides a suite of AI-driven tools designed to enhance productivity and sales performance.

Zipteams’ Meeting Scheduling tool stands out for its real-time availability sync with Google and Outlook calendars, making scheduling seamless and efficient. The round-robin scheduling feature ensures that the right team members are always available for client meetings. 

✔ The ability to embed meetings directly into digital platforms like websites and apps, 

✔ Combined with advanced lead routing and CRM integration

✔ Helps sales teams streamline client engagement and reduce manual coordination.

Zipteams’ Instant Meeting Rooms offer quick, voice or video-only meetings that can be embedded directly on websites or apps. With features like meeting note-takers and recordings, teams can manage inbound and outbound meetings efficiently.

Zipteams’ Conversational Intelligence provides key tools to increase productivity, such as auto-generated meeting summaries, real-time talking cues, and intent analytics to optimise sales conversations. 

✔  Automated next steps and CRM syncing, 

✔ Automated follow-ups and better engagement tracking

✔ Real-time call quality scores for instant feedback to improve performance.

By embracing AI as a tool to augment human capabilities rather than replace them, organisations can create more efficient, innovative, and productive work environments. The future of work is increasingly intertwined with AI, offering exciting possibilities for those ready to adapt and evolve.

Reach out now to see how Zipteams can fit your business needs.

by Aakriti


18
Nov
Understanding the Types and Use Cases for Enhanced Sales Performance
B2B Sales  ·  Sales
Sales Automation with AI: Understanding the Types and Use Cases for Enhanced Sales Performance

Managing a growing sales pipeline often involves many manual tasks, from data entry to following up with leads. These tasks take time away from building meaningful customer relationships and can slow the overall sales process. 

According to Salesforce, sales representatives currently spend only 28% of their week actively selling due to time-consuming tasks like lead prioritisation and data entry. By automating these repetitive activities, organisations can free up valuable time for sales teams to focus on building relationships and closing deals.

AI integration in sales transforms organisations’ operations, with significant benefits reported across various metrics. According to a McKinsey survey, 72% of organisations now leverage AI in multiple business functions, a notable increase from previous years, with marketing and sales being the most common areas of application. 

With AI transforming key aspects of the sales process, businesses are reaping the benefits of faster, smarter workflows. Now, let’s explore the specific AI tools driving this shift and look at real-world examples of how companies use them to enhance performance.

What is Sales Automation?

Sales automation definition involves using technology to perform repetitive, time-consuming tasks in the sales process, allowing sales teams to focus on higher-priority activities. These tasks include data entry, follow-up emails, lead scoring, and customer tracking. Sales automation tools handle the operational side of selling, reducing the manual work sales reps need to do.

Benefits of Sales Automation with AI

At its core, sales automation involves using technology to perform repetitive tasks in the sales process. AI takes this one step further by adding smart, data-driven decision-making. Rather than just automating emails or tracking customer data, AI can predict customer behaviour, forecast sales, and personalise interactions. The result? More time for your sales team to focus on high-value tasks like closing deals while AI handles the administrative and analytical work.

Here’s why AI-powered sales automation is essential for modern sales teams:

  • Improved Efficiency: AI handles large volumes of data much faster than any human could, allowing sales teams to prioritise leads and engage with prospects on time. This makes your sales process more streamlined and effective.
  • Personalisation at Scale: AI helps sales reps personalise their communications based on customer behaviour, preferences, and previous interactions. Whether it’s tailored email content or targeted product recommendations, AI ensures that every message resonates with the recipient, increasing the likelihood of conversions.
  • Accurate Sales Forecasting: AI tools are great at predicting future sales trends by analysing historical data and market conditions. This enables sales teams to set realistic goals, allocate resources efficiently, and avoid underperformance.
  • Better Lead Management: AI improves lead scoring by ranking prospects based on their potential to convert. This allows sales teams to focus on high-value leads, improving conversion rates and overall sales performance.

AI-powered sales automation keeps your business competitive by removing bottlenecks and enabling more strategic, personalised customer interactions. 

Also Read: The Ultimate Guide to AI-Powered Sales Automation in CRM

Types of AI Used in Sales Automation

You’ve probably heard about AI in sales, but what does it do? AI can take on various tasks, from personalising customer outreach to improving lead management. Here’s a closer look at the different AI tools that can change your day-to-day sales process.

Predictive Analytics

AI tools are like having a crystal ball but based on data. Predictive analytics reviews historical sales data and real-time market trends to help you make better decisions. Whether it’s forecasting demand or identifying hot leads, it helps ensure your sales strategies are on point.

When time is limited, knowing which leads to pursue first can save a lot of effort and increase the chances of success.

Lead Scoring

Lead scoring is where AI shines. It analyses customer behaviours and interactions to rank prospects based on their likelihood of converting. By highlighting the most promising leads, you can focus your energy where it’s most likely to pay off.

Chasing cold leads can drain your team’s morale. By focusing only on high-potential prospects, your team can work smarter and close deals faster.

Generative AI for Content Creation

Writing emails or proposals from scratch takes time. Generative AI helps by crafting personalised content. Whether it’s an email campaign or product recommendation, AI tools can tailor the content to each prospect. All you need to do is add a personal touch.

Customers are more likely to respond when they feel understood. AI-powered personalisation can make each interaction more meaningful, increasing your chances of a positive response.

Sales Forecasting

AI tools use historical sales data to generate accurate forecasts, allowing your team to better allocate resources and set realistic targets. This is more reliable than relying on guesswork or intuition alone.

Knowing what’s ahead means fewer surprises and more control over your pipeline, reducing the risk of falling short on targets.

For instance, Wipro uses AI for predictive analytics and sales forecasting by analysing complex datasets to identify trends and client behaviours. This approach enhances decision-making capabilities and drives revenue growth, enabling Wipro to simplify its sales processes while ensuring accurate forecasts.

As you have seen, AI automates your sales process. Many sales sectors have implemented AI in various use cases. Let’s explore the popular real-world application of AI in automating sales.

Real-World Applications of AI in Sales

The potential of AI extends beyond theory; it’s already delivering results in real-world settings. Here’s how companies are leveraging AI to boost their sales performance.

Automating Mundane Tasks

From follow-up emails to scheduling meetings, AI can take over repetitive administrative tasks. CRM systems now come with built-in AI features that streamline data entry and lead nurturing, giving your team more time to focus on closing deals.

Admin work can overwhelm your sales reps. Automating these tasks frees up their time to do what they do best: selling.

IKEA’s “Billie” AI: Automating Routine Tasks and Elevating Customer Support

Faced with a surge in customer inquiries, IKEA launched an AI chatbot named Billie in 2023 to streamline support. Billie efficiently handles generic customer questions, alleviating the burden on call centre agents. This shift allowed 8,500 IKEA employees to repurpose their time, focusing on higher-value activities like interior design training rather than routine support calls.

Boosting Customer Engagement

AI tools are great at personalising customer interactions. Chatbots can engage customers in real-time, helping answer questions or providing personalised product suggestions. This keeps prospects interested and engaged, even when your team is off the clock.

Customers appreciate timely and relevant responses. Engaging with prospects instantly makes them feel valued and speeds up decision-making.

Spotify’s AI-Powered Personalization: Transforming Customer Experience

Spotify’s use of AI takes music personalisation to the next level by analysing users’ listening habits and creating tailored recommendations. With a vast user base of over 433 million, Spotify’s AI DJ delivers individualised music selections, showcasing the potential of AI in customer-centric personalisation.

Enhancing Cross-Selling and Upselling

AI analyses customer behaviour to recommend relevant add-ons or upgrades, helping you maximise revenue from existing customers. This data-driven approach takes the guesswork out of upselling, allowing you to present the right offer at the right time.

Check the post to know more about how High-growth firms are getting the most from generative AI.

Challenges of Implementing AI

Challenges of Implementing AI

Like any new technology, AI in sales has its hurdles. Let’s address some common concerns:

  • Data Quality: AI tools rely on good data to work effectively. If your data is inaccurate or incomplete, the AI won’t deliver the results you expect. Make sure your systems are up-to-date and your data is well-maintained.
  • Employee Resistance: Some sales teams fear AI will make their roles obsolete. But AI isn’t here to replace people; it’s here to make their jobs easier by handling the heavy lifting. A well-trained team that understands the value of AI will be more willing to adopt the tools, increasing your chances of success.
  • Integration with Current Systems: It’s not always easy to implement new AI tools alongside existing platforms. The key is choosing tools that can integrate seamlessly with your CRM and sales platforms. Poor integration leads to inefficiencies. Choose AI tools that work well with your current system to minimise disruption.

Getting Started with AI in Sales

Ready to bring AI into your sales strategy? Here’s how you can start:

  • Identify Bottlenecks: Look for areas where AI can make the biggest impact—whether it’s automating follow-ups or improving lead scoring.
  • Train Your Team: Ensure that your sales reps understand how to use AI tools effectively. Ongoing training will help them stay up-to-date with the technology.
  • Start Small: You don’t need to overhaul your entire process overnight. Start with one or two AI tools and scale up as you see success.

Accelerate Your Sales Automation with Zipteams

Zipteams revolutionises sales automation with its AI-driven platform, specifically designed to enhance performance through targeted features. 

Zipteams’ Smart Meeting Rooms, powered by conversational analysis, are designed to empower inside sales and customer success teams with automation and real-time intelligence, enabling them to engage in highly contextual conversations.

  •  These meeting rooms automatically index all customer interactions, creating a comprehensive repository of discussions that eliminates the need for manual note-taking. 
  • With AI-powered nudges, users receive insights about customers during calls, enhancing engagement and understanding of their needs.
  • The platform also provides detailed conversational analytics, tracking customer sentiment and involvement, which leads to improved sales conversion rates and pipeline growth.

On the other hand, the Sales Acceleration App for CRM focuses on optimising the sales funnel by leveraging customer intent captured during calls. 

  • It enriches CRM systems with automated call summaries and BANT insights, ensuring that sales teams can act promptly on follow-up tasks. 
  • By setting up qualification questions, Zipteams captures customer responses directly into CRM fields, streamlining the qualification process and enhancing overall productivity. 

See how Zipteams can simplify your sales process. Start making every conversation count with Smart Meeting Rooms and real-time insights. Don’t miss out. Elevate your sales game now! 

by Aakriti


18
Nov
Strategies to Boost Your Sales Rep Productivity and Maximise Outcomes Using Generative AI
B2B Sales  ·  Generative AI  ·  Sales
Strategies to Boost Your Sales Rep Productivity and Maximise Outcomes Using Generative AI

In the current high-velocity sales environment, the numbers tell a concerning story: sales representatives spend only 28% of their workweek actively selling, with the remaining 72% consumed by administrative tasks, data entry, and meeting preparations, according to recent Salesforce research. 

This productivity challenge is further amplified by evolving customer expectations, where B2B buyers now demand personalised interactions, immediate responses, and deep product knowledge across multiple touchpoints. As sales teams struggle to keep pace, the gap between customer demands and sales rep bandwidth continues to widen.

Generative AI has emerged as a transformative force in addressing these productivity bottlenecks. While AI has already demonstrated its prowess in automating routine tasks across industries, its application in sales operations is particularly promising. From automating email communications and generating personalised sales collateral to providing real-time customer insights and streamlining CRM updates, AI-powered tools are fundamentally reshaping how sales teams operate. 

As sales leaders seek to maintain competitive advantage in an increasingly complex marketplace, understanding and implementing AI-driven productivity strategies has become paramount. Let’s explore how AI can help your sales team harness the power of generative AI to dramatically boost productivity and drive better outcomes.

Automating Content Creation for Outreach and Follow-Ups

Crafting emails, proposals, and follow-up messages can be time-consuming, but it’s essential for building relationships with prospects. Generative AI helps automate this process by generating tailored emails, presentations, and proposal drafts based on customer data. AI tools integrated into CRMs analyse customer interactions, preferences, and previous purchases to create personalised messaging for each prospect.

This not only saves time but also improves the relevance of communication, making it more likely to resonate with recipients.

  • Example: Instead of crafting each follow-up email manually, AI can use stored data that acknowledges the prospect’s previous questions or concerns, making outreach feel personal and timely.

Summarising and Analysing Sales Calls in Real-Time

Source: 

Summarising and Analysing Sales Calls in Real-Time

Sales calls are pivotal in B2B selling, but they’re often challenging to capture and analyse fully. AI-driven conversation intelligence tools can transcribe and summarise calls in real-time, capturing key points, objections, and customer sentiments. For instance, tools like Zipteams not only transcribe calls but also identify critical insights like prospect buying intent and interested & disinterested reasons from ongoing conversations.

This feature saves sales reps from taking detailed notes and allows them to focus on engaging with the client during the call. Post-call summaries ensure that follow-up actions are precise and timely, improving the overall customer experience.

  • Key Benefits:
    • Increases engagement by allowing reps to focus on conversation, not note-taking.
    • Provides actionable insights immediately after each call, making follow-ups more effective.

Also Read: Why Generative AI Adoption is one of the Must B2B CRM Strategies for Business Growth.

Prioritising Leads and Opportunities with AI-Powered Scoring

One of the biggest challenges sales teams face is identifying which leads have the highest potential for conversion. AI-powered lead scoring helps by analysing factors like engagement frequency, interaction history, and demographic data and then ranking leads accordingly. This process helps sales reps focus their energy on leads that are most likely to convert, streamlining efforts and increasing productivity.

  • Example: AI tools can analyse past interactions to highlight leads showing high intent signals, such as frequent engagement with emails or website content. This enables reps to focus their outreach on leads who are further along in the buyer’s journey.

Providing Data-Driven Forecasting and Pipeline Management

Forecasting is essential for effective sales management, but it often involves a lot of guesswork. Generative AI can create accurate sales forecasts by analysing historical sales data, current deal pipelines, and market trends. It can also flag potential pipeline gaps and suggest corrective actions, ensuring that sales managers have a clear view of what’s working and where resources may be needed.

  • Impact:
    • Increases forecast accuracy, helping sales teams set realistic targets.
    • Offers proactive solutions to improve pipeline health, reducing the risk of missed quotas.

Also Read: How Generative AI Can Automatically Create and Develop Your Sales Enablement Strategy

On-Demand Training and Skill Development

Every sales rep has unique strengths and areas for improvement, and AI can now provide personalised training programs to address each rep’s specific needs. By analysing performance metrics, AI can create tailored training modules, helping reps improve in areas such as objection handling, negotiation, or even advanced account management.

Some platforms can even simulate real-world sales scenarios, allowing reps to practice in a controlled environment and refine their skills.

  • Example: AI can analyse a rep’s past sales calls and identify areas for improvement, such as response time to objections. Based on this data, it can recommend specific training modules to help the rep address those challenges more effectively.

For quick productivity use cases of AI, check the Salesforce+ stream video, 7 Sales AI Use Cases to Supercharge Sales Productivity.

Improving Customer Interaction Analysis for Better Engagement

Improving Customer Interaction Analysis for Better Engagement

Understanding a customer’s journey and sentiment is critical for personalised sales. AI can analyse customer behaviour across various channels to identify trends, preferences, and engagement patterns, helping reps tailor their approach for each interaction. By understanding customer sentiment, reps can proactively address any concerns, making interactions more relevant and improving the likelihood of conversion.

  • Example: AI tools like Zipteams’ sentiment analysis can gauge whether a prospect’s tone is positive or negative in emails or calls, giving sales reps cues on how best to follow up and continue the conversation.

As you have seen, AI is augmenting your sales team’s capabilities by automating many sales-related tasks. Since AI is evolving, your sales team expect more productivity hacks from integrating AI into your sales process.

Like every new technology you have integrated into your sales like CRM, AI also needs well-planned execution to extract the full potential for your sales rep. There are a few common best practices to integrate AI into your sales.

Best Practices for Implementing AI in the Sales Team

Introducing AI to a sales team can significantly boost productivity by streamlining workflows, enhancing lead prioritisation, and improving customer engagement. However, successful implementation requires strategic planning and a focus on the specific productivity needs of sales reps. Here are some best practices for seamlessly integrating AI into sales teams:

1. Start Small with Targeted Use Cases

  • Focus on High-Impact Areas: Begin by introducing AI in specific areas where it can provide immediate productivity benefits, such as lead scoring, follow-up automation, or sales forecasting. For example, if your team struggles with prioritising leads, implementing an AI-driven lead-scoring system can help them focus on high-potential prospects, reducing time spent on lower-priority contacts.
  • Benefit: Starting small allows the team to familiarise themselves with AI tools, observe tangible results, and adjust without feeling overwhelmed by extensive changes.

2. Ensure Data Quality and CRM Integration

  • Prioritise Clean and Accurate Data: AI tools rely heavily on quality data. Before rolling out AI, conduct a data audit to clean up outdated or duplicate entries in your CRM. Integrate AI solutions that work seamlessly with existing CRM systems to avoid the productivity drain of toggling between platforms.
  • Benefit: High-quality data ensures that AI recommendations (such as lead prioritisation and customer insights) are accurate and reliable, helping sales reps spend more time with the right prospects instead of managing administrative tasks.

3. Offer Tailored Training and Continuous Learning

  • Provide Onboarding and Role-Specific Training: Train sales reps on how to use AI features relevant to their daily tasks. For instance, if AI automates email personalisation, demonstrate how they can make quick adjustments to refine messages based on the AI-generated draft.
  • Encourage Ongoing Skill Development: AI tools evolve, and so should sales skills. Include periodic training sessions or workshops to help reps maximise the value of new features or upgrades in the AI system.
  • Benefit: Tailored, role-specific training empowers sales reps to use AI tools confidently, reducing resistance and improving adoption rates.

4. Define Clear KPIs and Monitor AI Performance Regularly

  • Set Productivity Benchmarks: Define specific metrics (like time saved on non-sales tasks, increase in high-quality lead interactions, or reduction in follow-up time) to gauge how AI impacts productivity. Use these benchmarks to refine AI applications over time.
  • Conduct Regular Reviews: Assess AI tool performance periodically to ensure they’re helping sales reps meet these benchmarks. Gather feedback from the sales team to identify any issues or areas for improvement.
  • Benefit: Clear KPIs help track the real impact of AI on productivity, ensuring the tools contribute positively to workflow and are adapted as necessary.

5. Balance AI Automation with Human Oversight

  • Automate Repetitive Tasks, Not Relationship-Building: AI can handle repetitive and time-intensive tasks like data entry, meeting scheduling, and follow-up reminders, freeing up reps to focus on relationship-building and strategic outreach. However, emphasise that AI suggestions (e.g., lead scoring) are guidelines rather than final decisions.
  • Encourage Reps to Personalise AI Outputs: While AI can generate templates for outreach, it’s essential that sales reps personalise these messages based on real-time customer cues and needs.
  • Benefit: This balance ensures that AI enhances productivity without compromising the personalised human touch that is essential to effective sales.

6. Foster a Collaborative Culture of Continuous Improvement

  • Involve Sales Teams in AI Integration: Encourage reps to share feedback on AI’s effectiveness, noting specific areas where it improves or disrupts workflow. Use this input to refine processes and adapt AI tools to fit the team’s evolving needs.
  • Encourage Collaboration with IT and Data Teams: Regular collaboration between sales and IT teams can help troubleshoot integration issues, improve data management, and streamline AI updates or modifications.
  • Benefit: A collaborative approach fosters adaptability and ensures that AI remains a supportive tool for sales reps rather than an imposed technology.

Implementing AI in sales teams is most effective when approached with a clear focus on productivity and team support. This approach not only maximises the productivity benefits of AI but also cultivates a sales environment where technology and human skills work in harmony to drive success.

Zipteams: Elevating Sales Productivity with Automated Sales Processes

Are your sales reps drowning in administrative tasks like call scheduling, note-taking, and customer interaction analysis? These time-consuming activities can quickly sap their productivity, leaving them with less time to engage with prospects and close deals. Fortunately, there’s a game-changing solution that can help: Zipteams.

Zipteams is an AI-powered platform that automates these tedious sales operations, freeing up your team to focus on high-impact activities. Zipteams brings a suite of productivity-boosting features to the table. Its intelligent scheduling algorithms handle the back-and-forth of setting up calls, while advanced transcription and analysis capabilities provide detailed insights into customer conversations, no more frantically jotting down notes or struggling to recall key discussion points. 

To top it off, Zipteams can generate comprehensive call summaries, ensuring that critical information like lead score, parameters and metrics are captured and shared across your organisation without your reps having to lift a finger. 

By partnering with Zipteams, you can unlock a world of opportunities to boost productivity, enhance customer experience, and drive sustainable revenue growth – a win-win scenario that every sales leader should explore.

Schedule a demo today with Zipteams for super-efficient sales teams.

by Aakriti


18
Nov
Using AI to Simplify the Steps in the CRM Process
CRM  ·  Generative AI
Using AI to Simplify the Steps in the CRM Process

The global CRM market is rapidly evolving, and Artificial Intelligence (AI) is now at the core of its transformation. According to reports, the CRM market is expected to surpass $145 billion by 2029, largely driven by the increasing adoption of AI-powered features that streamline customer engagement and enhance sales processes. 

For businesses, integrating AI into CRM isn’t just a trend; it’s essential for future-proofing. As more companies adopt AI-driven CRMs, those without AI risk falling behind, struggling to keep up with data demands and personalised customer expectations. 

If you’re considering adopting AI, it’s best to start with the basics. Experiment with integrating AI into simpler stages of the CRM process—like lead qualification or automated follow-ups—and gradually explore how AI can improve personalisation and insights across your entire customer journey. This gradual approach allows you to understand AI’s capabilities in a practical, hands-on way.

In this blog, we’ll walk through each stage of the CRM process, showcasing how AI optimises operations, enhances customer interactions, and enables data-driven decisions. 

CRM and CRM Process

Customer Relationship Management (CRM) is a strategy and toolset used by businesses to manage and analyse customer interactions across their lifecycle. 

At its core, CRM helps companies understand, nurture, and engage with their customers more effectively, ultimately aiming to improve satisfaction and drive sales growth.

The CRM process refers to a series of structured steps companies use to build strong, lasting relationships with customers. This process includes

  1. Reaching new prospects
  2. Qualifying leads, 
  3. Converting them to customers
  4. Providing support
  5. Retaining them over time. 

Since each step in the CRM process aligns closely with the sales cycle, CRM enables sales teams to streamline workflows, prioritise leads, and focus on high-impact tasks, ensuring smoother transitions from one stage to the next.

The Role of AI in Optimising the CRM Process

With advancements in Artificial Intelligence (AI), CRM systems are undergoing remarkable changes in terms of functionalities, results and possibilities. 

As per statistics of G2, nearly 4 out of 5 companies see AI as a critical part of their business plans and sectors like marketing and sales focus 40% more on AI and machine learning than other departments.

Automation is a built-in feature for modern CRMs, and as AI is evolving, CRMs are pushing their limits to near-complete automation for most of the CRM tasks. From lead management to customer retention, CRMs are using AI to optimise the CRM functionality, driving the future of business.

As AI tools are integrated into CRMs, the long cycle of the CRM process is becoming more optimised and efficient. Each stage and step of the CRM process is either automated or simplified by AI tools. 

Are you yet to integrate the AI in CRM? And doubtful about AI’s advantages in your CRM? Here’s how AI is reshaping each stage of the CRM process with practical tools, applications, and implementation steps for successful AI integration

Customer Acquisition with AI

AI-powered CRMs enhance the acquisition phase by automatically identifying and targeting ideal customer segments. Machine learning algorithms analyse large datasets to find patterns, enabling companies to customise marketing campaigns that reach the right audience with precision.

Practical Applications:

  • Predictive Analytics: AI tools analyse demographic and behavioural data to predict which audiences are more likely to engage, enhancing the efficiency of acquisition campaigns.
  • AI Chatbots: Chatbots engage website visitors in real-time, answer initial questions, and gather lead information, ensuring potential customers are reached even outside business hours.

Steps for Implementing AI in Customer Acquisition

To effectively integrate AI into the customer acquisition phase of your CRM process, follow these steps to maximise impact:

  1. Identify Key AI Tools
    Begin by selecting AI-driven tools that fit your acquisition strategy. For instance:

        • Chatbots: For 24/7 lead capture and instant responses.
        • Meeting Schedulers: To automate scheduling directly from customer interactions, reducing response time. For example, Zipteams’s website meeting scheduler is a ready-to-embed smart meeting scheduling widget with Round-robin scheduling for a collaborative team.
        • Predictive Analytics: To identify and target high-potential prospects based on behavioural and demographic data.
  2. Data Collection and Analysis
    Collect and consolidate data from multiple sources (e.g., website interactions, social media, and past purchases) to create a detailed prospect profile. AI algorithms can analyse this data to recognise patterns and generate insights on customer behaviour, preferences, and engagement triggers.
  3. AI-Powered Personalization
    Leverage AI to personalise recommendations and interactions. Use data insights to provide tailored product suggestions, personalised content, or even customised service offerings to match individual prospect needs.
  4. Automate Customer Engagement
    Set up AI-powered tools for seamless, automated communication:

        • AI Chatbots: Engage visitors, capture essential lead information, and provide real-time answers to basic inquiries.
        • Automated Email Systems: Schedule follow-ups and nurture emails based on customer actions and preferences, creating a smooth and personalised journey from the first touchpoint. Tools like Mailchimp can automate and create personalised email marketing campaigns for any business. AI tools like this help businesses save time from administrative tasks.
  5. Continuous Optimization
    Regularly monitor performance metrics and analyse AI-driven interactions. Refine strategies based on engagement rates, conversion metrics, and customer feedback. AI algorithms will also adapt and learn over time, improving the effectiveness of targeting, personalisation, and engagement.

Streamlining Lead Qualification and Nurturing

Lead management is a lengthy step in any CRM process. This step involves major human intervention, especially in nurturing the leads and moving them to the sales funnel. AI-powered tools are designed to automate major portions of this CRM process. With data analytics, AI tools can process vast amounts of data to get insights on buyers’ persona, lead scoring, lead qualifying, and recommendations for nurturing.

As per the HubSpot survey, 64% of marketers use AI/automation and 38% of those who don’t will start using it in 2024.

AI optimises lead qualification by scoring leads based on their engagement level, behavioural patterns, and past interactions. This allows sales teams to prioritise high-quality leads and nurture them with relevant content.

Practical Applications:

  • AI-Driven Lead Scoring: Tools like HubSpot and Salesforce Einstein automatically score and rank leads based on factors like email opens, web activity, and demographic information.
  • Automated Follow-Up Campaigns: AI can personalise and automate follow-up emails based on lead behaviour, ensuring that prospects receive timely, relevant content.

Steps to Implement AI in Lead Management

Most modern CRM are offering automated Lead management features for their users. Since the intervention of AI, AI-powered lead management is becoming more efficient and effective. Follow the steps to successfully implement AI tools for Lead management in your CRM process.

  1. Identify Lead Scoring AI Tools: Choose AI tools that specialise in lead scoring. These tools analyse past behaviour, engagement, and demographic data to score leads based on conversion potential. For Example, Zipteams offers conversational intelligence for sales calls and meetings that automate capturing custom qualification parameters, pre-trained BANTs, and prospect buying intent with interested and disinterested reasons.
  2. Data Enrichment and Integration: Ensure lead data is continuously updated in your CRM to provide AI with accurate and comprehensive information. Integrate data from all lead touchpoints (e.g., website visits and email engagement) to improve scoring accuracy.
  3. Automate Personalized Nurturing Campaigns: Use AI-driven platforms like Marketo or ActiveCampaign to send tailored follow-ups based on lead scores and behaviour. Set triggers for actions like opening an email or visiting a webpage, which prompts automated responses or content delivery.
  4. Monitor and Refine Lead Scoring: Regularly check the accuracy of lead scoring models by tracking conversion rates and adjusting scoring criteria based on observed performance.

Enhancing Conversion with AI-Powered Customer Insights

As per the Mckinsey report, 70% of the customer’s journey is based on how the customer feels they are being treated and As per a Zendesk survey, 6 in 10 customer service agents say a lack of consumer data often causes negative experiences.

AI offers deeper insights into each lead, enabling sales reps to tailor their pitches and product offerings according to customer needs. Predictive sales tools analyse past purchase data, engagement history, and customer sentiment to guide sales reps in closing deals more effectively.

Practical Applications

  • Sales Recommendations: AI suggests personalised product recommendations based on a lead’s preferences, increasing the likelihood of conversion.
  • Real-Time Call Analytics: AI-driven tools like Zipteams transcribe sales calls and highlight key takeaways, allowing reps to focus on understanding customer needs.

Steps to Implement AI in Customer Conversion

AI tools can be integrated into the customer conversion process for better results, as these powerhouses can predict and recommend the best strategies to convert a customer based on deep insights. 

  1. Deploy Predictive Analytics for Conversion Optimization: Implement AI tools that can analyse customer behaviours and predict purchasing likelihood. 
  2. Utilise AI for Real-Time Insights During Sales Interactions: Tools like Zipteams use AI to analyse live conversations, highlighting key insights and areas for improvement, allowing reps to pivot their approach in real-time.
  3. Personalise Recommendations Using Customer Data: Use AI algorithms to recommend tailored products or services to leads based on previous purchases, browsing history, and preferences, which increases the chance of conversion.
  4. Integrate AI-driven content and Messaging Personalization: Use AI to suggest language, offers, and content in real-time to ensure relevance. CRM systems integrated with AI offer personalised recommendations for effective call-to-action phrasing and content that resonates with prospects.

Also Read: Why Generative AI Adoption is one of the Must B2B CRM Strategies for Business Growth.

Customer Support and Onboarding Automation

After conversion, AI-powered CRM solutions streamline customer onboarding and support by automating interactions and providing real-time answers. AI chatbots and virtual assistants handle basic queries, freeing up human agents for more complex issues.

Practical Applications

  • AI Chatbots for Support: Integrated with CRM, AI chatbots offer 24/7 support, guiding new customers through onboarding and handling routine inquiries.
  • Sentiment Analysis: AI tools analyse customer feedback, emails, and chats to understand customer sentiment, enabling teams to respond proactively.

Steps to Implement AI in Customer Support 

  1. Implement AI-Powered Chatbots for Support: Set up AI chatbots like Drift or Intercom to engage customers immediately, handling frequently asked questions, and guiding them through the onboarding process.
  2. Automate Onboarding Sequences: Use AI to automate onboarding workflows, delivering tutorials, guides, and follow-up content based on user behaviour and milestones reached in the onboarding journey. 
  3. Use Sentiment Analysis for Proactive Support: Integrate AI-driven sentiment analysis tools like Zipteams to monitor customer feedback and interactions. This helps support teams proactively address issues before they escalate, ensuring a smoother onboarding experience.
  4. Monitor and Optimise Support Automation: Track chatbot response times, resolution rates, and customer satisfaction scores, refining chatbot responses and automation workflows as needed.

Retaining and Engaging Customers with Predictive AI

AI helps businesses retain and engage customers by identifying those at risk of churn and delivering personalised retention efforts. Machine learning models analyse behaviour trends and satisfaction scores to provide retention recommendations.

Practical Applications

  • Predictive Churn Analysis: AI identifies patterns that indicate dissatisfaction or potential churn, allowing teams to initiate retention strategies proactively.
  • Personalised Loyalty Programs: AI recommends rewards and incentives tailored to individual customer preferences, increasing loyalty and long-term engagement.

Steps to Implement AI in Retaining Customers 

Retaining customers is a crucial part of any business strategy looking for upselling. In the dynamic market, 89% of consumers are more likely to make another purchase after a positive customer service experience, as per the Salesforce survey.

  • Implement Predictive Churn Analysis: Choose AI tools, like Gainsight or CustomerGauge, that use customer interaction data to predict churn risk. These tools analyse engagement history, satisfaction surveys, and behavioural patterns to flag potential churn cases.
  • Deploy AI-Driven Loyalty Programs: Use AI to create and manage loyalty programs, providing customised rewards based on each customer’s behaviour, preferences, and purchase history.
  • Automate Proactive Retention Campaigns: Set up automated retention campaigns triggered by churn indicators. For example, if AI predicts a customer is at risk, it can initiate special offers or personalised outreach to re-engage them.
  • Continuously Monitor Retention Metrics and Refine Strategies: Track key retention metrics such as churn rate, loyalty program participation, and engagement levels to assess the impact of AI-driven retention strategies, adjusting them based on real-time feedback and AI insights.

With AI supercharging your CRM process, there are a number of best practices you need to consider while introducing AI to your organisation. These practices ensure your CRM gets the full potential of AI tools when integrated.

Best Practices for Integrating AI in CRM

Successfully integrating AI into your CRM system requires careful planning, data management, and team collaboration. Here are some best practices to ensure smooth implementation and optimal results:

Start with Clear Objectives and Key Use Cases

  • Define Specific Goals: Identify which CRM processes you want to enhance with AI, such as lead scoring, customer support automation, or personalised marketing.
  • Focus on High-Impact Use Cases First: Begin with AI applications that provide quick, measurable benefits, such as automating lead scoring or personalising email outreach, to demonstrate value early on.

Ensure Data Quality and Accessibility

  • Clean and Organise CRM Data: AI relies heavily on accurate data to generate insights. Ensure that customer data in the CRM is complete, up-to-date, and free of duplicates or errors.
  • Centralize Data Sources: Integrate your CRM with other data sources (e.g., customer service logs, and purchase history) to give AI a comprehensive view of customer interactions, enhancing predictive capabilities.

Invest in Employee Training and Continuous Support

  • Provide Hands-On Training: Equip sales reps and managers with the knowledge to use AI tools effectively. Offer workshops or onboarding sessions that demonstrate how AI features work within the CRM.
  • Encourage Continuous Learning: As AI capabilities evolve, keep your team updated on new features and encourage a culture of continuous learning to maximise AI’s potential.

Also Read: Completely Automate Your Sales Enablement Training: Benefits and Best Practices

Monitor Performance and Adjust Regularly

  • Track Key Performance Indicators (KPIs): Establish KPIs (such as conversion rate, response time, or customer satisfaction) to measure AI’s impact on CRM processes.
  • Regularly Assess AI Outputs: Regularly review AI-generated insights and recommendations to ensure they align with your business goals. Use feedback to refine AI models and adapt the CRM strategy as needed.

Maintain a Balance Between Automation and Human Oversight

  • Automate Routine Tasks, Not Relationship Building: While AI can streamline repetitive tasks, ensure that sales reps maintain control over high-touch aspects like relationship-building and closing deals.
  • Encourage Sales Reps to Add a Personal Touch: AI can provide templates or recommendations but encourage reps to personalise communications, as genuine human interaction remains crucial in B2B sales.

By following these best practices, businesses can harness AI’s potential to optimise the CRM process, reduce operational friction, and support sales teams in building lasting customer relationships.

Zipteams: AI Tools for Automated CRM Process

If your team struggles with managing customer relationships manually or keeping up with sales leads, Zipteams offers a stack of solutions. Designed for sales and customer service optimisation, Zipteams leverages AI to automate key CRM processes, from lead scoring to customer engagement. Features like real-time meeting scheduling, Smart Meeting Rooms and AI-driven coaching insights streamline workflows, allowing sales teams to focus on building relationships and closing deals.

Zipteams also integrates seamlessly with CRM systems, ensuring that sales reps have all customer data at their fingertips without spending hours on data entry or follow-up tracking. By simplifying each stage of the CRM process, Zipteams empowers sales teams to increase productivity and deliver a more personalised customer experience.

For more on how Zipteams can optimise your CRM process, Book a demo here.

by Aakriti


14
Nov
AI-Driven Sales Management Process
B2B Sales  ·  Generative AI  ·  Sales
AI-Driven Sales Management Process: Understanding the Basics in 4 Essential Stages

The sales management process can feel overwhelming at times, with so many moving parts, such as finding the right leads, nurturing prospects, closing deals, and maintaining customer relationships.

AI offers a fresh way to simplify and supercharge your sales efforts. Think of AI as your new sales sidekick. It steps in to handle the repetitive tasks, like sorting through leads and analysing customer interactions, freeing you up to focus on building connections and closing deal.

According to McKinsey report, 14% of businesses that use AI for their sales and marketing efforts have already seen a noticeable boost in lead generation and conversion rates. This shows the power of AI when integrated effectively into the sales process. It’s not just about speed; it’s about being smarter in how you engage with customers.

In this blog, we’ll explore how AI can boost your sales process across four key stages: lead generation, conversion, deal management, and post-sale relationships. By understanding these basics, you’ll see how AI can help you work smarter, close more deals, and keep customers coming back for more.

Stage One: Lead Generation and Qualification with AI

Lead generation is the backbone of the sales management process. With diverse lead channels and vast data, identifying the right prospects can be challenging and time-consuming. 

Managing these diverse leads with manual power will lead to missing out potential leads through cracks and customising the lead generation will also wind up using generic communications. AI can automate these tedious task and provide you with insightful information to manage the lead into nurturing.

AI tools are equipped with unprecedented capabilities to generate and process vast amounts of data from various sources. From website visits to sales calls, AI makes this easier by analysing vast datasets, including customer behaviors, demographics, and market trends, to identify high-potential leads. 

With the AI-powered meetings tools, you can customise the capturing parameters for specific business requirements, from customer qualification captures to pre-trained BANT captures.

AI Integration:

  • Meeting Scheduling Assistance: AI-powered tools like meeting scheduling assistance analyse initial conversations to assess lead quality. This includes monitoring customer interest levels, identifying keywords, and recognising patterns in customer queries.
  • Dynamic Audience Targeting: AI can segment leads based on specific behaviours and preferences, ensuring your sales team targets prospects most likely to convert. AI-driven meeting tools can identify nuanced customer signals during interactions, providing valuable insights that inform qualification strategies.

Benefits:

  • Improved Lead Scoring: AI systems can score leads based on engagement and interaction patterns, helping prioritise which leads to pursue.
  • Efficient Qualification: By identifying key signals during customer interactions, AI ensures you focus only on leads with the highest potential, saving time and increasing efficiency in your sales management process.

For example Zipme.at provides seamless coordination of scheduling meetings between sales teams and prospects. Zipme.at provides easy-to-embed meeting rooms available from your websites, landing pages, CRMs, or calendars. And meeting rooms are powered with AI to identify the touch points of the customer in real time.

Stage Two: Lead Conversion Using AI

Once leads are qualified, converting them into customers becomes the primary focus. This stage of the sales management process requires personalised engagement, follow-ups, and strategic communication, all areas where AI can assist.

From content generation to automation of communication, AI can serve the lead conversion stage with precision. AI-powered sales tools can improve the lead qualifying campaigns and increase conversion by providing exclusive Artificial Intelligence features.

With smart meeting rooms, AI equips your sales team with key information like customer sentiment, prospect buying intent scores, and purchasing history with real-time feedback to both sales teams and managers. AI can create tailored content like emails, presentations, and FAQs without errors, and sales teams can use them as per the customer’s requirements.

AI Integration:

  • Personalised Follow-ups: AI systems analyse past conversations and interactions to generate personalised follow-up content tailored to each prospect’s specific needs. Automated meeting summaries ensure that the next steps are clear and address the prospect’s concerns.
  • Sales Pipeline Optimisation: AI provides real-time insights into customer sentiments and engagement, guiding you toward the most promising opportunities in the pipeline. By tracking customer behavior during interactions, AI tools can suggest optimal next steps and strategies for conversion.

Benefits:

  • Increased Engagement: Personalised follow-ups keep prospects engaged and informed, increasing the likelihood of a successful conversion.
  • Enhanced Productivity: With AI handling routine tasks like follow-up emails and appointment scheduling, your team can focus on more strategic activities that drive conversions in the sales management process.

Also Read: Why Generative AI Adoption is one of the Must B2B CRM Strategies for Business Growth

Youtube

Wants to know how AI manages the sales call from scheduling to post-calls within minutes. Check the video to learn how to create AI-powered smart meeting rooms within just 2 minutes

Stage Three: Sales Management and Deal Closing

Closing deals effectively is a significant and optimal component of the sales management process.

Closing deals traditionally required a lengthy and tiresome process. With smart technologies like CRMs, businesses can optimise the process and improve closing rates. 

AI can help streamline this closing process that requires analysing customer interactions, handling objections, and providing tailored solutions. AI-driven insights can enhance this process by offering data-driven recommendations and support in real time over the meatings and sales calls.

AI Integration:

  • Conversation Intelligence: AI tools capture and analyse sales conversations in real time, identifying critical moments like objections, buying signals, and pain points. This helps the sales team adapt their strategies mid-conversation to address concerns effectively.
  • Sales Forecasting: AI uses historical data to provide accurate sales forecasts, helping teams anticipate deal outcomes. Predictive analytics offer insights into expected revenue, guiding managers to focus on deals with the highest potential for closure.

Benefits:

  • Targeted Strategy: By understanding customer responses and objections through AI analysis, sales reps can tailor their pitches and closing techniques to match customer needs.
  • Accurate Forecasting: AI-enhanced forecasting helps allocate resources efficiently, ensuring the sales management process remains on track toward meeting targets.

Zipteams

Check here to learn more about Zipteams’ tools to improve the product demos.

Stage Four: Enhancing Post-Sale Relationships with AI

The sales management process doesn’t end at closing a deal. Maintaining and enhancing post-sale relationships is essential for customer retention, upselling, and long-term business growth. 

AI plays a key role in personalising post-sale engagement. From round-the-clock chatbots and virtual assistants to tracking customer interactions and feedback,  AI-powered tools can be crucial in retaining loyal customers.

AI Integration:

  • Ongoing Customer Engagement: AI tools continuously analyse customer interactions post-sale to identify areas of satisfaction and potential issues. Real-time insights guide sales reps in providing targeted support and identifying opportunities for cross-selling or upselling.
  • Automated Follow-Ups: AI-generated meeting summaries and follow-up suggestions help sales teams maintain meaningful post-sale communication, ensuring customers feel valued and understood.

Benefits:

  • Improved Customer Satisfaction: By addressing specific customer needs based on AI-driven insights, you can enhance customer experiences, leading to higher retention rates.
  • Proactive Support: AI tools can monitor customer feedback and behaviors, allowing you to address concerns proactively and build long-term relationships.

As you wonder which AI-powered tools can optimise your sales management, we, at Zipteams, created a curated suite of tools customised to your business specific needs.

Zipteams: Your AI-Powered Sales Management Solution

Zipteams offers a suite of AI-powered tools meticulously designed to streamline every aspect of the sales management process, from initial lead generation to post-sale engagement. Here’s a breakdown of how its features tackle key sales activities:

AI Smart Meeting Rooms

With Zipteams’ AI Smart Meeting Rooms, sales reps get real-time insights during customer meetings. The AI assistant monitors conversations, tracks customer engagement signals, and identifies key decision-making cues. With the power conversational intelligence, Zipteams solutions can generate talking cues in real time to laser focus on prospects. 

Sales-Focused Conversation Intelligence

Zipteams uses advanced AI Conversation Intelligence to analyse sales calls. The system captures and highlights critical moments, such as objections, buying signals, and questions that indicate a prospect’s needs. It provides detailed insights into the effectiveness of sales pitches and interactions, helping sales teams refine their approach to closing deals.

After each meeting, Zipteams’ AI Meeting Assistant generates automated summaries, capturing action items, key discussion points, and customer sentiments. 

Real-Time Coaching Insights and Sales Performance Analytics

During live sales calls, Zipteams offers Real-Time Coaching Insights, guiding sales reps on how to address customer pain points and objections. This tool helps in increasing the chances of successful lead conversion by providing on-the-spot suggestions based on the flow of the conversation.

Zipteams provides an in-depth review of interactions and pipeline health. It allows sales managers to track the effectiveness of meetings, monitor customer engagement, and identify opportunities for improvement, leading to more informed decisions throughout the sales management process.

Ready to transform your sales process? Try Zipteams now and watch AI take your results to the next level.

by Aakriti


B2B Sales  ·  Generative AI  ·  Sales
Best AI-Driven Marketing and Sales Automation Tools to Use in 2024

Businesses today are flooded with data and customer expectations that are constantly changing. AI-powered marketing and sales tools help make sense of all that information by turning it into useful insights, automating tasks, and improving how companies interact with their customers. 

These tools are becoming a go-to solution for companies looking to make smarter decisions, streamline operations, and stay competitive in a fast-evolving market.

Marketers prioritise AI implementation above all other initiatives, with 75% either experimenting with or fully integrating AI into their operations. As per the same survey by Salesforce, High-performing teams are significantly more likely to have integrated AI compared to underperformers

With tools now more accessible and affordable, businesses of all sizes are embracing AI-driven solutions. AI is applied across tasks like content creation, personalised marketing, email automation, and data analysis. The result? Businesses are benefiting from automating repetitive processes, delivering tailored experiences at scale, and gaining deeper insights into customer behavior, all while working more efficiently.

Ready to step into the future of marketing and sales? We’ve curated a list of the top AI-driven tools for 2024 that address the most pressing challenges in the industry. Whether you’re looking to enhance customer segmentation, automate campaigns, or gain deeper insights into your audience, these tools have got you covered. Let’s explore how these innovative solutions can supercharge your marketing and sales efforts.

Best AI-Driven Marketing and Sales Automation  Tools to Use in 2024

Each tool brings something different to the table, from advanced lead scoring to personalised customer journeys. Below, we’ll compare key features and use cases of leading platforms, helping you determine which solution aligns best with your business needs and goals.

Zipteams

Zipteams

Zipteams is an AI-powered platform designed to accelerate sales and marketing campaigns. With AI-powered scheduling and meeting rooms, the platform helps sales teams qualify leads in real time, automate follow-ups and analysis, and provide conversational insights during customer interactions.

Key Features:

  • Meeting Scheduling

Zipteams Instant website conversion tools and meeting scheduler can connect your leads to sales teams in 20 seconds with Intelligent routing to available sales team members.

    • Acceleration Widgets: Offers meeting widgets for websites and marketing campaigns for better customer engagement.
    • Real-Time Calendar Synchronisation: Prevents double-booking and missed opportunities by ensuring that schedules are always up-to-date.
    • Advanced Routing Features: Helps in efficiently coordinating meetings, allowing teams to focus on building client relationships.
  • Smart Meeting Rooms

Smart Meeting Rooms are designed to give real-time intelligence to inside sales and customer success teams to have highly engaging and contextual conversations with customers.

    • Automated Note-Taking : Capures key moments of customer interaction like sentiments, objections, and feedback and identifies and identifies possibilities of upsell opportunities and retention risks in real-time calls.
    • Customer Conversational Analytics: Provides conversational analytics around customer involvement and their sentiment.

With customer buying intelligence, Zipteams’ tools are equipped to contextualise every sales call with automated intent scorings and CRM integrations.

    • Intent Scoring: Analyses customer interactions to gauge interest and readiness to engage.
    • Automated Next Steps: Suggest follow-up actions based on the conversation, ensuring timely engagement with prospects.
    • Robust Reporting: Offers detailed reports on sales interactions, aiding in forecasting and strategy adjustments.

Use Case: Ideal for businesses aiming to optimise real-time sales calls and interactions with customers, particularly those heavily reliant on voice and video calls.

Also Read: Why Generative AI adoption is one of the Must B2B CRM Strategies for Business Growth.

Zoho CRM

Zoho CRM

Zoho CRM integrates Zia AI, a virtual assistant that provides smart recommendations, anomaly detection, and predictive insights to help teams make informed decisions. Zia also automates workflows and assists with lead management, making Zoho a powerful tool for both small and large businesses.

Key Features: 

  • Anomaly Detection: Identifies unusual patterns in sales data and customer behavior for proactive decision-making.
  • Voice-activated Data Management: Enables hands-free data entry and retrieval through natural language processing.
  • Sentiment Analysis: Evaluates customer interactions to gauge satisfaction and identify potential issues.
  • Contact Time Optimisation: Determines the most effective times to engage with leads based on historical data.
  • Automated Workflow Suggestions: Recommends process improvements based on successful past interactions.

Use Case: Best for small to medium-sized businesses looking for a highly customisable and affordable CRM solution that integrates well with other Zoho applications.

Factors.AI with LeadSquared

Factors.AI with LeadSquared

Factors.AI of LeadSquared is a comprehensive sales execution platform that leverages AI to streamline the lead-to-revenue process and maximise conversion rates. Its strength lies in its ability to manage high lead volumes and streamline the sales process.

Key Features

  • Multi-channel Lead Capture: Automates lead acquisition from various digital touchpoints with AI-powered data enrichment.
  • Intelligent Lead Distribution: Utilises machine learning algorithms to match leads with the most suitable sales representatives.
  • Task Automation and Prioritisation: Generates and prioritises tasks for sales teams based on AI-driven insights.
  • Predictive Lead Scoring: Employs advanced algorithms to assess lead quality and likelihood of conversion.
  • Sales Process Optimisation: Analyses sales workflows to identify bottlenecks and suggest improvements

Use Case:  Ideal for sectors like BFSI, healthcare, and education, focusing on handling a high volume of leads and automating personalised follow-ups.

Freshworks CRM (Freddy AI)

Freshworks CRM (Freddy AI)

Freshworks CRM, powered by Freddy AI, helps businesses automate their sales processes by providing predictive insights, automated workflows, and customer sentiment analysis. Freddy AI helps sales teams focus on high-value tasks by automating follow-ups and managing sales sequences.

Key Features:

  • Intelligent Data Management: Automates contact deduplication and data cleansing processes.
  • Predictive Deal Insights: Calculates win probabilities and provides recommendations for deal progression.
  • AI-powered Lead Qualification: Automates lead scoring and routing based on predefined criteria and historical data.
  • Email Engagement Analysis: Tracks and scores email interactions to prioritise follow-ups.
  • Next Best Action Recommendations: Suggest optimal next steps for sales representatives based on AI-driven insights.

Use case:  Suited for businesses looking to automate repetitive sales tasks and follow-ups, particularly those needing to personalise customer interactions at scale​

Breeze Copilot of HubSpot

Breeze Copilot of HubSpot

HubSpot integrates AI capabilities across its marketing, sales, and service hubs to provide a unified, intelligent customer experience platform. The platform helps businesses automate marketing campaigns, manage CRM workflows, and optimise sales with real-time data insights.

Key Features: 

  • AI-driven Content Strategy: Recommends content topics and strategies based on market trends and user engagement data.
  • Predictive Lead Scoring: Utilises machine learning to identify high-potential leads and accounts.
  • Natural Language Processing Chatbots: Facilitates intelligent customer interactions for support and lead qualification.
  • Automated SEO Recommendations: Provides data-driven suggestions for content optimisation and keyword targeting.
  • AI-enhanced Sales Forecasting: Analyses pipeline data to generate accurate sales projections.

Use Case: Perfect for businesses focused on inbound marketing and needing a comprehensive CRM and marketing platform to scale their sales efforts.

Salesforce Einstein

Salesforce Einstein

Salesforce Einstein is an AI layer that enhances Salesforce’s suite of products with predictive and prescriptive capabilities, driving intelligent decision-making across sales, marketing, and service functions.

Key Features: 

  • Custom AI Model Creation: Enables the development of tailored predictive models without extensive data science expertise.
  • Opportunity and Account Insights: Provides AI-driven analysis of sales opportunities and account health.
  • Automated Activity Capture: Streamlines data entry by automatically logging customer interactions across channels.
  • Computer Vision for Social Media: Utilises image recognition to enhance social media monitoring and engagement.
  • AI-powered Product Recommendations: Generates personalised product suggestions for e-commerce applications.

Use Case: Ideal for larger organisations needing sophisticated analytics and automation capabilities.

Mailchimp with AI

Mailchimp with AI

Mailchimp utilises AI to enhance email marketing automation and customer segmentation, allowing businesses to send highly personalised emails. Its easy-to-use platform helps businesses create, send, and track campaigns with AI-optimising content for better engagement.

Key Features:

  • AI-assisted Design: Generates email designs based on brand guidelines and best practices.
  • Subject Line Optimisation: Utilises machine learning to predict and suggest high-performing email subject lines.
  • Predictive Segmentation: Automatically categorises subscribers based on behavior and attributes for targeted campaigns.
  • Send Time Optimisation: Determines optimal email delivery times based on individual recipient behavior.
  • Content Recommendations: Suggests relevant content to include in email campaigns based on subscriber preferences and engagement history.

Use Case: Great for businesses looking to optimise their email marketing efforts, especially small and medium businesses with a focus on customer engagement.

ActiveCampaign

ActiveCampaign

ActiveCampaign is a customer experience automation platform that combines email marketing, CRM, and machine learning to improve customer engagement. Its predictive sending feature ensures that emails are delivered at the optimal time to increase open rates.

Key Features:

  • Predictive Sending: Optimises email delivery timing based on individual recipient engagement patterns.
  • Machine Learning-powered Lead Scoring: Calculates lead scores and win probabilities using advanced algorithms.
  • AI-driven Content Recommendations: Suggests optimal content for email campaigns based on subscriber data and preferences.
  • Automated Segment Discovery: Identifies valuable customer segments for targeted marketing initiatives.
  • Predictive Engagement Scoring: Assesses customer engagement levels to identify at-risk accounts and opportunities for retention.

Use Case: suitable for marketers looking to enhance customer relationships through targeted communication.

Marketo Engage (Adobe)

Marketo Engage (Adobe)

Marketo Engage, an Adobe product, utilises AI to enhance B2B marketing automation and account-based marketing strategies. It uses AI to recommend content, optimise marketing campaigns, and predict customer behavior, making it a top choice for large-scale operations.

Key Features: 

  • AI-powered Audience Segmentation: Analyses customer data to create precise, targeted marketing segments.
  • Predictive Content Recommendations: Personalises web experiences with AI-driven content suggestions.
  • Automated Budget Allocation: Optimises marketing spend across channels based on performance data and AI insights.
  • Intelligent Account Profiling: Utilises AI to identify high-value accounts and detect purchasing intent signals.
  • Adaptive Nurture Paths: Optimises lead nurturing sequences based on engagement patterns and AI-driven insights.

Use Case:  Ideal for B2B companies needing robust marketing automation solutions.

Pipedrive with AI

Pipedrive with AI

Pipedrive is a sales CRM that uses AI to provide deal probability predictions, helping teams prioritise deals with the highest likelihood of success. The platform simplifies sales pipeline management with automated follow-ups and task scheduling.

Key Features:

  • AI-powered Sales Assistant: Provides personalised recommendations and reminders to sales representatives.
  • Automated Data Entry and Enrichment: Extracts and organises relevant information from email communications.
  • Deal Probability Predictions: Calculates the likelihood of deal closure based on historical data and deal characteristics.
  • AI-suggested Sales Activities: Recommends optimal follow-up actions and engagement strategies.
  • Intelligent Contact Management: Automates the detection and merging of duplicate contacts to maintain data integrity.

Use Case:  Ideal for small to mid-sized sales teams looking to optimise their sales pipeline and focus on the most promising leads.

As businesses continue to adopt AI-driven tools for marketing and sales, understanding the essential features these platforms offer becomes critical to maximising their value. However, with numerous options available in the market, it can be challenging to determine which tool best suits your specific needs.

To help businesses make informed choices, it’s important to focus on the features that will have the greatest impact on both marketing and sales operations. Below are the key features to look for when selecting an AI-driven marketing and sales automation tool, ensuring that it aligns with your business goals and helps drive efficiency and growth.

Key Features to Look for in AI Marketing and Sales Tools

When you’re on the hunt for AI-driven marketing and sales automation tools, keep an eye out for these essential features:

  • Intelligent Lead Management: Look for AI-powered lead scoring and prioritisation capabilities. These will help your team focus on high-potential opportunities and allocate resources more effectively.
  • Predictive Analytics: Choose a tool with advanced forecasting and trend analysis functionalities. This will give you the insights you need for strategic decision-making and smarter resource allocation.
  • Automated Personalisation: Opt for AI-driven content customisation and timing optimisation. This will boost your engagement across various communication channels.
  • Natural Language Processing (NLP): Select a tool with robust NLP capabilities. This will enhance customer interactions, streamline data analysis, and enable voice-activated commands.
  • Adaptive Sales Strategies: Pick a platform that dynamically adjusts sales approaches based on real-time data and historical performance. This will help optimise your conversion rates.
  • Integration Capabilities: Make sure to choose a tool that integrates seamlessly with your existing tech stack. This ensures a unified view of customer data and smoother workflows.
  • Ethical AI and Data Privacy: Opt for a solution with strong data protection measures and ethical AI practices. This will help maintain regulatory compliance and build customer trust.
  • Scalability: Select a tool that can handle increasing data volumes and complexity while maintaining performance. This is crucial for long-term value as your business grows.

By focusing on these key features, you’ll be well-equipped to select an AI-driven marketing and sales tool that offers comprehensive functionality, adaptability, and the potential to significantly impact your revenue generation and customer relationships.

Conclusion

As we’ve seen, AI is revolutionising the marketing and sales landscape, offering tools that can dramatically improve efficiency, insights, and customer engagement. While each platform we’ve explored has its strengths, Zipteams stands out as a comprehensive solution that addresses the core needs of modern sales teams.

Zipteams shines with its modern AI-powered meeting scheduling, hosting, and analysing tools that provide conversational intelligence and advanced meeting analytics to improve your sales conversations. With its customer buying intelligence including intent score and automated next step, Zipteams gives you a 360-degree view of your sales engagements, helping you make data-driven decisions that drive results.

Take the first step towards the future of sales calls and meetings with our comprehensive suite of tools. Book a demo with Zipteams and discover how our AI-powered platform can elevate your sales game to new heights. 

by Aakriti


14
Nov
How CRM Software Helps Salespeople Plan Better Sales Calls with AI-Powered Insights
CRM  ·  Generative AI
How CRM Software Helps Salespeople Plan Better Sales Calls with AI-Powered Insights

Sales success doesn’t just come from the number of calls your team makes. Success stems from how well those calls are planned. Poorly planned sales calls often result in wasted time, miscommunication, and missed opportunities. 

A study of Salesforce found that 70% of customers expect every sales rep should know customer’s purchase and issue history. Within the dynamic market, planning an effective sales call can make all the difference between closing a deal and losing a prospect. 

The key lies in preparation, where understanding customer needs, timing, and personalisation is crucial. AI-powered CRM automates this preparation and provides solid customer data. According to HubSpot’s 2023 State of AI in Sales survey, sales professionals save an average of 2 hours and 15 minutes daily by automating manual tasks through AI.

If you’ve ever felt that your team’s sales calls fall flat or fail to connect, it’s likely because they weren’t equipped with the right information or insights. This is where CRM (Customer Relationship Management) software steps in, providing essential tools that help sales teams plan better calls. CRM software can help salespeople plan better sales with the data and insights needed to approach each call with purpose and precision.

This guide will explain the importance of CRM and AI-powered tools for effectively planning your sales calls. From data collection to hyper-personalisation, this blog will cover the benefits that an AI-powered CRM can bring to your sales call.

Importance of Effective Call Planning 

Planning a sales call might seem simple, but it’s easy for salespeople to fall into the trap of generic pitches or poorly timed conversations. Without a clear plan, sales reps often miss the mark, wasting valuable time and opportunities.

Effective call planning is all about understanding the customer’s needs, knowing when to approach them, and delivering a personalised experience. When done right, it transforms a simple sales call into a meaningful conversation.

On the other hand, a well-planned sales call helps you connect with prospects in a more meaningful way. By anticipating customer needs and tailoring your pitch, you build trust and improve your chances of closing deals. 

CRM software plays a critical role in this process by providing a centralised hub for customer data, streamlining communication, and delivering real-time insights that enable you to move from a generic sales approach to a more personalised one.

The Role of CRM Software in Sales Call Planning

To streamline sales call planning, modern CRM systems offer a range of powerful features designed to improve efficiency and performance. Here’s how CRM tools help:

  • Centralised Data on Customers: CRM systems provide a one-stop shop for all customer information. Whether it’s past communications, purchase history, or notes from previous meetings, all relevant data is organised and easily accessible. This helps sales reps tailor their conversations and avoid redundant follow-ups.
  • Automation of Repetitive Tasks: Routine tasks like setting up meetings, sending reminders, and logging call notes can take up valuable time. CRM software automates these administrative tasks, freeing up salespeople to focus on building relationships with prospects and closing deals.
  • Personalisation for Customers: With CRM tools, salespeople can tailor each conversation based on customer behaviour and preferences, making the interaction more relevant and increasing the likelihood of conversion. The CRM provides real-time insights into customer needs, helping you pivot your pitch based on what resonates with the prospect.

By centralising customer data, automating routine tasks, and providing deeper insights into each prospect, CRMs make sure your sales team is always prepared for every conversation. With modern features, a solid CRM can help you overcome common sales challenges. 

Also Read: How Generative AI is Disrupting Call Center CRM Software and Automation

Overcoming Common Sales Call Planning Challenges with CRM

From fragmented data to time constraints, sales professionals often encounter various challenges when planning their calls. CRM software helps overcome these obstacles:

  1. Data Silos: When customer information is spread across different tools or departments, it’s hard for salespeople to access the full picture. CRM systems break down these silos by centralizing all customer data in one platform.
  2. Data Overload: CRM systems filter and organize customer data so your team can focus on actionable insights instead of getting lost in endless details.
  3. Data Collection: CRM systems simplify data collection by automatically logging customer interactions across channels (email, phone, social media), ensuring that nothing falls through the cracks.
  4. Sales Training: CRM platforms can guide newer sales reps through the call process with step-by-step scripts, reminders, and suggestions, making it easier for them to build confidence and improve their skills over time.

Zipteams

Read here on how Zipteams can help your sales teams with Gen AI productivity

With its unprecedented abilities in automation, predictive analytics, and conversational intelligence, AI has extensive roles in sales calls. Let’s explore the multiple hats AI can wear for your sales team.

AI’s Role in Sales Call Planning

AI is revolutionising sales call planning by automating complex tasks and offering predictive insights. AI-powered CRMs provide unprecedented possibilities for sales teams, allowing them to be more proactive and data-driven. Key roles AI plays include:

1. Enhanced Data Collection and Analysis

The beauty of CRM lies in its ability to gather and organise data across various touchpoints automatically. From email exchanges to website visits, your CRM captures it all.  AI can sift through vast amounts of customer data to uncover actionable insights. By analysing customer behaviours, purchase histories, and interaction patterns, AI empowers sales teams to make data-backed decisions when planning their calls.

  • Real-Time, Accurate Data: Stop worrying about outdated or incomplete customer info. AI tools ensure your sales teams always have access to up-to-date customer profiles.
  • Data-Driven Decisions: AI within CRM systems analyses customer behaviour and preferences, giving your team insights into what matters most to each prospect.
  • Predicting the Next Move: AI-driven CRMs can predict the likely outcome of a sales call, suggesting the best next steps based on patterns from past interactions.

2. Hyper-Personalisation

AI goes beyond basic personalisation by analysing customer preferences in real time, helping you craft hyper-personalised communication strategies. This could mean adjusting your script on the fly or customising follow-up actions based on the customer’s behaviour.

  • Tailored Communication: AI tools can generate personalised emails, dynamic call scripts, and custom follow-up actions. This means your team is always ready with the right message at the right time.
  • Building Stronger Connections: Personalised outreach doesn’t just improve sales. It strengthens relationships. Prospects are more likely to trust a salesperson who clearly understands their needs and preferences.
  • Adaptive Communication: CRM systems track changes in customer behaviour and sentiment, helping your team adjust their approach based on what’s happening in real-time.

3. Lead Scoring and Prioritisation for Calls

Not every lead is worth equal attention. AI-driven lead scoring ranks prospects based on their likelihood of converting, allowing you to prioritise the most valuable leads. This helps sales teams optimise their time and efforts, ensuring they focus on high-potential opportunities.

  • Focus on High-Potential Leads: With AI scoring, your team can see at a glance which leads are ready to buy and which ones need more nurturing.
  • Improved Call Prioritisation: By focusing on the most promising leads, your sales reps can structure their days more effectively, maximising their chances of closing deals.
  • Real-World Impact: Many companies have seen increased conversion rates and faster sales cycles after implementing AI-powered lead scoring through CRM.

4. Call Planning and Scheduling

AI tools analyse customer behavior to recommend the best times for outreach, helping your team schedule calls when prospects are most likely to be receptive. Plus, automated reminders and calendar syncing ensure that follow-ups are never missed.

  • Smart Scheduling: AI tools within CRM systems analyse customer behaviour to determine the best times for calls. This means your sales team reaches prospects when they’re most likely to engage.
  • Automatic Reminders and Syncing: CRM tools can send reminders to sales reps and sync with their calendars to ensure no opportunity slips through the cracks.
  • Relevant, Dynamic Scripts: With AI-generated scripts personalised for each prospect, your reps can confidently adjust their messaging mid-conversation based on how the call is going.

5. Predictive Sales Analytics for Strategic Call Planning

AI-driven predictive analytics allow sales teams to forecast sales trends and outcomes, helping them adjust their strategies in real-time. By identifying which deals are most likely to close, sales teams can focus on where they’ll have the biggest impact.

  • Sales Forecasting: With predictive analytics, your team can forecast which deals are most likely to close and plan their outreach accordingly.
  • Better Resource Allocation: Knowing which prospects are most likely to convert allows you to allocate your team’s time and energy where it will have the most impact.
  • Performance Insights: CRM systems generate reports that give you a detailed view of team performance, helping you tweak strategies and improve sales outcomes.

Zipteams’ AI Power Suites For Successful Sales Calls

Zipteams offers a compelling stack of AI-powered tools to your CRMs for successful sales calls. From smart meetings to sales call training, Zipteams features an exclusive suite to enhance your sales call. 

With the power of conversational AI, Zipteams can extract valuable insights like Custom Qualifications, Pre-trained BANT, prospect buying intent scores, and customer sentiments, all from live ongoing calls in real-time with automation.

In addition, Zipteams’ AI-powered tools support multiple languages, from English to Hindi. With their hassle-free interface, Zipteams AI tools can integrate seamlessly with popular CRMs in the market.

Take the next step and schedule a free demo with Zipteams today. Discover how AI-powered CRM software can help salespeople plan better sales calls and help you close more deals efficiently.

by Aakriti


14
Nov
Exotel Zoho CRM and Desk Integration
CRM
Exotel Zoho CRM and Desk Integration

In the current business landscape, seamless communication between teams and customers is crucial for success. Zoho CRM and Zoho Desk provide robust customer relationship management and support solutions, while Exotel offers a powerful cloud telephony platform. 

Integrating Exotel with Zoho CRM and Zoho Desk allows businesses to unify their communication channels, enabling efficient call management, improved customer experience, and enhanced productivity for sales and support teams. 

This article will guide you through the integration process, highlight its key features, and provide best practices for maximizing its benefits.

Exotel Benefits in Zoho Integration

The integration of Exotel with Zoho CRM and Desk offers several advantages:

  • Call Notifications: Users receive real-time notifications for incoming calls on their Zoho interface, ensuring they never miss important communications.
  • Click-to-Call Feature: Initiate outbound calls directly from Zoho, simplifying the calling process and saving time.
  • Task Management: Automatically create follow-up tasks or events after calls, enhancing workflow management.
  • Call Recordings: Call recordings are automatically linked to relevant contacts or tickets, providing valuable context for future interactions.
  • User Mapping: Easily map Exotel users to Zoho users, allowing for selective Click-to-Call functionality and bulk uploads to save time.

Prerequisites for Integration

Before setting up the integration, ensure the following:

  1. Exotel Account: Sign up for an Exotel account.
  2. Account Verification: Verify your account via phone or email.
  3. KYC Verification: Complete KYC verification for your account.
  4. ExoPhone Purchase: Acquire ExoPhones for agents to handle inbound and outbound calls.
  5. API Credentials: Note your Account SID, API Key, and API Token from the API section.

Keeping the prerequisites ready ensures a smooth setup process for Exotel integration

Setting Up Exotel Integration

To set up the integration, follow these steps:

Step 1: Create Co-workers and Groups in Exotel

  • Log in to your Exotel account.
  • Navigate to the ‘Co-workers and Groups’ section and invite co-workers

Step 2: Configure Zoho Plugin Call Flow

  • Access the ‘App Bazar’ section in Zoho.
  • Create a new app and define the call flow settings.

Step 3: Associate the Call Flow to ExoPhones

  • In the ExoPhones section, assign the created call flow to your ExoPhones for call routing.

Step 4: Enable Integration

  • Go to the integration interface provided by Exotel.
  • Enter your Exotel credentials and enable the integration with Zoho.

Step 5: Map Zoho Users to Exotel Agents

  • Map Zoho users to Exotel agents in the integration interface, enabling Click-to-Call as needed.

Handling Calls and Tasks

Once integrated, users can manage calls effectively:

  • Incoming Calls: Zoho displays a pop-up with caller information when a customer calls, allowing agents to provide personalized support.
  • Post-call Actions: Create follow-up tasks and events in Zoho CRM or Desk after each call.
  • Missed Calls: Automatically generate tickets for missed calls in Zoho Desk, ensuring timely follow-ups.
  • Call Recordings: Access and review call recordings directly from Zoho for monitoring and training purposes.
  • Task Management: Manage customer-related tasks directly within Zoho’s dashboard to streamline workflows.

Zipteams seamlessly integrates with Zoho and Exotel, and its AI-powered tools provide insightful data-driven analysis of sales calls using conversational intelligence.

Zipteams

Check out the LinkedIn post for more information on Zipteams and integration with popular CRMs.

Benefits of Integration for Sales and Support Teams

By integrating Exotel and Zoho, your team gets the benefits of enhanced collaboration between sales and support teams. Let’s explore the diverse benefits of exotel integration to your Zoho CRM.

  • Increased Productivity: The integration reduces manual data entry and task creation, allowing agents to focus on customer interactions.
  • Enhanced Customer Experience: Incoming call pop-ups provide context, enabling personalised and efficient communication with customers.
  • Unified Communication: Centralised access to call logs and communication history improves visibility and decision-making.
  • Improved Reporting: Generate reports integrating both call data and CRM/helpdesk information for actionable insights.

Best Practices for Using Exotel-Zoho Integration

Integration alone can not be beneficial unless you harness the features for strategic steps in your sales cycle. Following are the best practices you can implement to get best from the integration.

  • Regular Updates: Periodically update the integration settings in Exotel and Zoho CRM/Desk.
  • Call Recording: Use call recordings for quality monitoring and training.
  • Automatic Ticket Creation: Utilise automatic ticket creation for missed calls to ensure no query is overlooked.
  • Click-to-Call: Set up click-to-call functionality to enhance agent efficiency.

Known Limitations

Though integration can help you improve your business, it comes with hurdles. There are notable limitations to be aware of:

  • On-Call Events: Call events, such as calls answered or ringing, are not triggered in real-time. Users must manually select ‘Answered’ and perform subsequent actions after the call concludes.
  • Call Recording Upload: Call recordings are uploaded every five minutes. To view these recordings, users need to refresh their Zoho Desk screen after the specified interval.
  • Missed Call Details: Information about missed calls becomes available in the Zoho Dashboard only after a validation process, which typically incurs a delay of approximately five minutes.
  • Feature Restrictions: Some advanced features available in standalone versions of Exotel may not be fully accessible within Zoho integrations12.
  • User Limitations: Depending on your plan, there may be restrictions on the number of users who can access certain features like Click-to-Call.

These limitations can affect the efficiency of call handling and task management within the integrated platforms. 

Revoking Exotel Zoho Integration

If you need to revoke the integration:

  • Log into your Zoho account.
  • Navigate to the integration settings.
  • Select Exotel from your integrations list and choose the option to disable or remove it.

This action will stop all data sharing between Exotel and Zoho platforms, ensuring that no further interactions occur until re-enabled

Zipteams’ AI-powered Tools for Better Benefits

With Zipteams, you can make the most out of these integrations. AI-powered tools of Zipteams equip you to track and analyse the sales call in real time. With the Zipscore, your sales team can extract insights like customer sentiments and lead scoring. Zipscore also features sales training metrics to keep your team on the right track. With Zoho and Exotel integration, Zipteams’s AI tools can optimise each call for smarter and faster sales.

Conclusion

Integrating Exotel with Zoho CRM and Desk offers a unified communication platform that streamlines customer interactions, enhances agent productivity, and improves overall customer experience. 

By following the setup steps and utilising the features effectively, businesses can extract the power of cloud telephony and CRM integration to stay ahead in customer service and sales management.

Book a demo today to know more about how Zipteams can help you optimise your Exotel and Zoho integration.

by Aakriti


Page 3 of 4