Client’s Problem: “We really just want to know what the future holds for sales. That knowledge impacts everything we do.”

This problem is so common that there are thousands of off-the-shelf solutions. All the big companies (Salesforce, Tableau, SAP, MicroStrategy, et al.) have robust forecasting tools baked into their desktop or cloud software that only require the press of a button. During scoping, we pressed the Client hard on the validity of their “build versus buy” decision. We offered our insights and assessment as well. Still, they had several excellent strategic reasons for wanting to create their solution.

As with any problem, we searched the Client’s data and their industry data for patterns. We found no shortage of those: temporal, spatial, segment, category, and everything. What we found were too many patterns. Something didn’t add up: we were looking at a single data set despite the data sets being independent. The multitude of patterns was the signature of a coupled system with a driver. So, we unearthed the small number of driving forces in the system.

Once we knew the underlying drivers, we modeled them using traditional statistical techniques and modern machine-learning models. The models accurately reflected the Client’s desire for a time horizon relevant to their decisions. Mission accomplished! However, was it?

Using the knowledge we gained about the demand patterns in their industry, we saw a unique opportunity for the Client to acquire, collect, and curate novel data associated with their industry. As the Client’s liaison, we worked with third parties to design several strategic initiatives to tackle data acquisition. The new data was later folded into the demand modeling as market share features and further improved the model’s utility as a strategic tool.