Why Does Your Sales Team Need a Robust Data-Based Prioritized List of Customers to Contact?
If your sales team doesn’t have access to a data-based prioritized list of customers or leads to contact, they are operating at a big disadvantage compared to the competition. Even if your organization is using some simple data-based prioritization methods, these are often based on what people assume is the best customer to call, not what the data proves is actually the best customer to call; thus, there could be a big opportunity to consider a model-based method of prioritizing customers.
Predictive and prescriptive data science models can learn from historical sales and sales interaction data to help sales teams understand with which customers an additional sales call would have the greatest incremental impact on their sales. In the end, they can get an interactive dashboard that suggests exactly who they should visit, call, and e-mail that day with an optimized driving route! Imagine a salesperson calling a huge list of prospective buyers in no particular order and getting 1% of them to buy. It is not uncommon for a predictive model to be able to identify a group of 10% of customers on the list that convert at 5 times the average rate; so, if your sales team only had time to contact 10% of customers on the list, their productivity goes up 5x as they go from converting 1% customers to 5% customers!
If your analytics team isn’t providing this kind of prioritization to your sales team today, it’s possible they lack the data science knowledge to build and implement something like this. Whether you’d like your analytics team to learn the data science techniques required to do this themselves or would prefer for a consultant to build the data science model for you, Value Driven Analytics can help! Watch the video above to learn more about how to build a robust customer prioritization model for your sales force.
