No sales team would pass up an opportunity to improve its overall productivity and profitability. Most also realize that big data and analytics are game changers. The question isn’t whether to harness the power of big data and analytics. It’s deciding what provides the most value to individual salespeople and the entire organization. Companies that consistently post record sales quarter after quarter have learned how to close the gap between customer relationship management (CRM) and the real-time performance of sales teams. One reason for this is that big data gives management the visibility to see what’s going on with the entire sales team.
Practical Applications of Big Data
Sales platforms driven by big data give managers more information to work with when it comes to coaching their teams. The ability to see sales performance in real time allows them to step in and give constructive feedback so a pattern of underperformance doesn’t continue. According to a 2016 Corporate Executive Board study, it takes just three hours of one-on-one coaching per month for poorly performing sales personnel to increase their close rate by up to 70 percent. This also helps management identify and reward top performers more easily.
Success in sales happens most often when the decision-making process of the buyer and the sales process align closely. One way to achieve this is by equipping sales professionals with pre-loaded conversations they can access from any location. This eliminates the need for salespeople to rely on gut instincts when meeting with a prospect and trying to close a deal. Another useful application of a big data platform is the ability to record conversations during the sales meeting. Later, managers can go over these conversations to analyze what worked and what didn’t work.
Analyzing Big Data for Maximum Benefit
Big data is only part of the picture. It means nothing without the ability to analyze it effectively. Sales organizations should aim to implement algorithms that uncover the following benefits:
• Improved accuracy in forecasting
• Improved ability to identify opportunities for cross-selling
• Ability to separate customers into segments such as those looking to buy high-dollar items, long-term customers, and those that need the most convincing to close a deal
• Highlight ways to improve marketing efforts
While it can take some trial and error to get the algorithms just right, a 2015 Strands Retail report indicates that sales teams can expect a return on investment of up to 20 percent.
Pipeline Analysis for Better Sales Performance
The quarterly business review (QBR) is an ideal time to review the strength of a salesperson’s pipeline. After three months of trying to close deals, it can be easy for sales reps to lose sight of the importance of a healthy pipeline. Some areas a manager and representative can explore together include:
• Whether the pipeline contains the right mix of prospects
• Whether deals forecasted for future quarters can be moved up
• Deciding how to move deals along that appear stuck in a certain stage of the sales process
• Whether the quota for the pipeline is realistic
• Realign the typical customer persona
Discussing pipeline health doesn’t have to wait until the QBR. Ideally, discussing it should be part of every one-on-one meeting between managers and sales reps. These are just a few ways sales organizations can make big data and analytics work for them. They really are nothing to fear.
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