Another important metric is customer lifetime value (CLV). There are several ways to calculate this metric, but the result allows sales managers to better understand how much a company can earn from a specific customer over the entire relationship.
Understanding CLV can help managers prioritize in-person customer visits. For example, predictive analytics can help identify new customers who are similar to high CLV customers. These customers may require more in-person meetings or other resources from the start.
Additionally, using predictive analytics, you can actively norway mobile database target industries, organizations, or job roles that have previously generated higher CLV for your business.
8. Sales department plan
This KPI is almost certainly already part of your sales data analysis. It’s important to understand which salespeople are meeting their targets. And if they’re not, why? It’s possible that your sales targets simply don’t reflect the reality of the territory.
Analyzing sales quotas helps managers identify workload imbalances that need to be addressed in their territories. For example, a manager who consistently falls to the bottom of the leaderboard may not have enough opportunities in their territory. On the other hand, someone who consistently comes out on top may have too many leads. Using sales analytics to allocate resources allows you to increase overall revenue while treating each manager fairly.