There was an interesting article in the New York Times about why medicine, in a world of fixed protocols and extensive research, needs stories and vignettes as well. Watching my daughter at medical school, I agree with the author. On the other hand, in the world of sales, the opposite is true: Sales is in desperate need for data science, yet has plenty of stories.
Today Sales management, planning, and forecasting largely depend on motivational techniques, case studies, intuition, and rules of thumb that are often myths. I am going to focus on sales forecasting today.
It is not uncommon for sales executives to say if we get greater than X% pipeline coverage at the beginning of the quarter, we should make the quarterly sales target while paying little attention to the quality of the pipeline. It’s time for sales to use data science and some rigor alongside the techniques in the paragraph above.
Data science enables salespeople to prioritize and focus on accounts that have a greater likelihood of closing. Typically businesses with higher priced line items and a longer sales cycle focus on the top 10 deals by value. However this top 10 ranking often includes deals that are unlikely to close and quantitative analysis will identify worthwhile opportunities that are more likely to close within the quarter.
Many firms struggle to determine the risk in attaining their sales targets and “guidance” they have given investors for the quarter. A data science approach provides probabilistic information about how likely the firm can meet its targets at the aggregate level. Of course, the data science probabilities cascade up and each deal has a probability.
Sales forecasting is a predictive process with many biases built into it. Some forecasters are overly optimistic and others try to be heroes and beat their number by submitting a pessimistic forecast. Data science provides an unbiased approach to compile a truly data-driven forecast.
Of course, most people in businesses other than high volume e-commerce will continue to utilize human forecasts for many many years into the future. Data science driven forecasts are typically based on history, while the human forecast may continue to include insights that are not yet included in the data science model.
Applying data science to sales forecasting enables sales management and other stakeholders in the process to ask the right questions about the internal (human) forecast.
The combination of the two approaches provides far greater confidence in the internal forecast and easier attainment of sales targets.