Over the past month numerous companies in industries as diverse as airlines, banking, and retail have issued guidance lowering growth expectations for the remainder of the year. The slowing economy will put pressure on sales organizations to deliver near-term growth.
Advanced analytical techniques can re-focus sales efforts and assure that the right products are offered to the right customers at the right time for the right price. We’ve seen double digit growth from applying predictive analytics to help companies (1) decrease customer churn, (2) increase organic revenue growth, and (3) strengthen their sales force by identifying the best job candidates, determining what motivates them, and retaining the high performers.
In terms of reducing churn, leveraging sales analytics can determine customer-buying behaviors and predict the probability of sales success in order to predict “at risk” customers before they even start looking for alternatives. For one company, we integrated Salesforce data with customer profile data and transaction data to get a holistic picture. The analysis revealed that the client was losing more than $42 million annually due to preventable customer churn and missed sales opportunities.
From this, we were able to create an “intervention playbook” for the sales force which would continually:
- Evaluate customer orders continuously to identify high priority and at-risk customers;
- Analyze the risk profile of each customer to determine the optimal account strategy;
- Prioritize sales calls by integrating priority attributes with scheduling logic;
- Recommend intervention tactics related to product and price to the customer rep via a dashboard;
- Define an escalation process to evaluate and refine engagement with customers to ensure success.
Sales analytics also can help companies overcome the potentially profit-reducing habit of sales reps giving too many discounts. Rigorous analysis of customer response to price allowed Revenue Analytics to provide guidance on which customers should be approached at what time, with what product, and at what price. Specific promotion & rebate programs could be accurately targeted to incent the appropriate customer response.
Similarly, an analysis of the employees in the sales force was also eye-opening. Analytics can uncover a connection between customer buying behavior and the sales force hiring criteria. In one case, we saw that our client hired people based on a “positive” personality. The data, however, showed that the most successful salespeople were those with an “assertive” personality.
A new hiring profile was developed that identified the optimal attributes best suited for selling the most products at the highest margins.
By combining these strategies – all stemming from sales analytics – a company can develop a dynamic and analytically driven sales strategy. One Revenue Analytics’ client increased customer growth in the first year by 27 percent – breaking a three-year cycle of stagnant growth.
Using advanced analytics to proactively address these issues now can prevent end-of-year angst.