Linda, the VP of marketing has been asked by the President and the CFO to help with predicting the order volume for the next month. They want to ensure that the sales forecast for next month is cross validated with an alternate estimate of what next month might look like. The executive leadership wants to ensure that if the forecast for next month does not meet the plan, they are ready to deploy countermeasures now. Marketing tends to have robust data on customer interactions that might provide a leading indicator to the order volume in the future. Linda has some ideas on which customer interactions could be potential leading indicators. She decides to engage Julie, a skilled data analyst, to help her with the analysis.

Julie decides to build a predictive model based on existing data to evaluate whether she sees any relationships between potential customer interaction variables and order volume. Let’s see what they discover.

Goal

Customers engage digitally with your website through their buying process via many touchpoints. Some of the touchpoints might have predictive information on the number of orders a product receives—especially if the product is high volume and transactional. Our goal is to identify the variables, and after determination of the predictive nature, use them to forecast—and in some cases, increase (by optimizing the touchpoints)—the number of orders for the product.

Also, marketing spend helps bring out the product value proposition in front of customers. The other goal of this exercise is to understand how marketing spend drives orders.

Pain Points

• My lead volume has gone down. How will this impact my orders in the future?
• How does my marketing spend drive orders? My manager is asking me to predict order impact for a higher spend.
• We have discount programs planned, and we are being asked about the impact on orders.

Idea

In B2B marketing, as we try to understand which customer touchpoints influence the number of orders for high-volume products, we have the following hypotheses:

• Hypothesis 1: When customers come to a web page organically (through SEO), obtain pricing, have a price discount program, etc., they are inclined to purchase.
• Hypothesis 2: Marketing spend also favorably impacts orders.

Decision to be Made

Predict number of orders based on the leading indicators described above.

Learn more about how to prove or disprove this hypothesis in our course Using Data Analytics for Decision-Making in B2B Marketing.