In our previous article, we saw how Artificial Intelligence (AI) and Machine Learning (ML) can empower businesses to build personalized customer experiences and target customers at scale. Another key use case for AI and ML is Customer Relationship Management (CRM), which is a vital part of the sales lifecycle. CRM is a technology used by sales teams to manage all their customer relationships and sales interactions. CRM systems are valuable resources for businesses: They capture vast amounts of data on customers and provide sales teams with data on every sales interaction with customers and prospects. AI and ML can be used to enhance the CRM process in several ways:

Sales teams must deal with several labor-intensive routines and repetitive tasks such as data entry, scheduling sales calls and meetings, administrative paperwork, logging sales activities (calls, meetings, emails, etc.), customer documentation, and so on. AI-powered automation can complete these essential, albeit tedious, tasks with a high degree of accuracy and consistency, which can free up sales teams to focus on developing sales opportunities, building customer relationships, and closing sales.

The data in CRM databases exist in a variety of formats and with varying levels of quality. The effectiveness of any analysis depends on the accuracy of the data being used. AI tools can help ensure accuracy and completeness by cleaning the data to detect and address errors, inconsistencies, duplicates, missing data, and other potential issues.

AI can expedite the lead qualification process by vetting customers based on certain criteria, and identifying prospects with the highest likelihood of purchasing products and services. AI can analyze vast amounts of historical and real-time sales data to determine customers’ buying readiness.

Building trust with customers is a critical part of the sales process. A significant number of sales interactions occur virtually (online, via messaging, email, calls, etc.). However, it can be challenging to determine a customer’s emotional state and build trust when sales representatives cannot always see their customers face to face. Using sentiment analysis, AI-powered tools can analyze communications and evaluate a customer’s emotional response.

Sales teams can use machine learning to analyze sales data and determine how different factors contribute to sales wins or losses, predict the possibility of successfully closing sales for a given customer, and recommend any necessary adjustments to sales strategy. Further, ML tools can be applied to sales forecasting, customer behavior prediction, real-time sales pipeline analysis, tracking sales trends, identifying cross-selling opportunities, sales performance management, and much more. Overall, AI-driven sales intelligence gives sales teams insight into the entire sales lifecycle, allowing them to adapt to customer demand more effectively and close more sales.