Bridging gap between Data science and Marketing/Sales
In today’s times bridging gap between data science and sales & Mktg teams has become an important reality for business as never in the past there was explosion of marketing analytics platforms and AI (Artificial Intelligence) for Sales and Marketing, these two groups are very different groups, and working together far more often than ever before. This trend is only going to grow more in the coming years, so it’s critical for modern businesses to develop a common language. But the question is who shall interpret?
“Data Scientist from Mars, Marketing & Sales from Venus”
We all have experienced differences of opinion and strategy rifts between marketing and sales; however, this gap is even bigger between data scientists and marketing & Sales. These groups have different motivations and goals with understanding of different language.
In my present role I play the role of a bridge by listening to problems sales & marketing have, help developing analytical products, package these into actionable solutions and eventually implement these within our customer flows.
How to make Data science accessible to B2B Marketing & Sales.
If you ask a data scientist to explain the predictive models, they would be able to speak about it for hours with terms like regression, confusion matrices, decision trees and dimensionality reduction, etc. Predictive models are complex techniques to process data into outputs which should be easy to understand for Sales and marketing or else their attention would drift away quickly. Sales and Marketing people worry about business impact of these so-called predictive models, they only worry about which companies should we target or which lead shall be converted into an account and why.
What is the problem?
If you have a question for a data scientist, we all know what we hear either “it depends” or “we need more data”, actually this is true as data science is about a combination of business context, relevant data, objective clarity and executable output, the trickiest part of the output which the data science team produces and how business partners are able to consume the same and implement it. I have also seen business heads telling data science team to do what they think is right and also share with them the way to achieve the same. This results in a frustrated data science team and bad predictive model.
How to go about it?
The best solution is to share the business problem with the data science team and give them the freedom to provide solutions using judgement, skill and creativity. There needs to be resources who are between the business community like sales and marketing and data scientist who can bridge this gap.
I have been working with both, Sales and Marketing folks and Data science teams. My role has been to build a bridge with time and patience, it also requires focus from both sides to understand each other and communicate in what they want/need.
This is a skill which is unique and requires years of knowledge in similar areas of problem solving and can be arranged like a symphony in an orchestra to enable facilitate actionable out of for business.