Data Science Process: Communicating results

In our last theoretical post about Data Science Process.  We reached our final station after having an amazing machine learning model that can predict, with high accuracy, how likely a prospective customer is to buy X Inc’s product. But how do we convey its awesomeness to our client, the VP of Sales? How do we present our results to her in a form that she can use?

Communication is one of the most underrated skills a data scientist can have.

While some of our colleagues (engineers, for example) can get away with being siloed in their technical bubbles, data scientists must be able to communicate with other teams and effectively translate their work for maximum impact. This set of skills is often called ‘data storytelling.’

So what kind of story can we tell based on all what we’ve done so far? Our story will include important conclusions that we can draw based on your exploratory analysis phase and the predictive model we’ve built. Crucially, we want the story to answer the questions that are most important to our client!

First and foremost, we take the data on the current prospects that the sales team is pursuing, run it through our model, and rank them in a spreadsheet in the order of most to least likely to convert. We provide the spreadsheet to your VP of Sales.

Next, let us highlight a couple of our most relevant results:

• Age: We’re selling a lot more to prospects in their early 30s, rather than those in their mid-20s. This is unexpected since our product is targets people in their mid-20s!

• Marketing methods: We use social media marketing to target people in their 20s, but email campaigns to people in their 30s. This appears to be a significant factor behind the difference in conversion rates.

The following week, we meet with her and walk her through our conclusions. She’s ecstatic about the results we’ve given her! But then she asks ,

“How can we best use these findings?”

Technically, our job as a data scientist is about analyzing the data and showing what’s happening. But as part of our role as the interpreter of data, we’ll be often called upon to make recommendations about how others should use your results.

In response to the VP’s question, let us think for a moment and say, “Well, first, I’d recommend using the spreadsheet with prospect predictions for the next week or two to focus on the most likely targets and see how well that performs". That’ll make our sales team more productive right away, and tell us if the predictive model needs more fine-tuning.

Second, we should also look into what’s happening with our marketing and figure out whether we should be targeting the 20s crowd with email campaigns, or making our social media campaigns more effective.”

The VP of Sales nods enthusiastically in agreement and immediately sets you up in a meeting with the VP of Marketing so we can demonstrate our results to him. Moreover, she asks us to send a couple of slides summarizing our results and recommendations so she can present them at the board meeting.

Boom! We’ve had an amazing impact on our first project!

Next we will discuss this process in more technical details using real data project