On Wednesday, June 3rd, @ben_p (Data Science Manager, MandM Direct) presented his DSS project to predict the likelihood that a customer will return to a website.
The presentation was followed by an intervention from Leo Treguer, Data Scientist at Dataiku, who shared learnings from his experience developing customer churn models.
Ben explained how his team joined data together from various sources into BigQuery, then used DSS to perform data preparation. This resulted in a machine learning algorithm to assess the probability of a customer returning to the website days after their visit.
Key takeaways from Ben's presentation:
Have a clear goal and put a lot of time/thought into defining the question you are aiming to answer,
Think about the final action early on in the process,
Spend a lot of time working on features and... let DSS do the hard work!
Your input data: check, recheck and check again!
Best practices and pitfalls from Leo's experience:
Focusing on a precise context, e.g. only new customers’ churn in their first 2 weeks of usage
Creating relevant features on customers
Explaining analyses to non-data scientists to maximize impact
Not checking input data enough
Expecting machines to understand business
Including too many outliers
Not involving other stakeholders enough
Ben has been working with data and analytics in the online retail space for 4 years. He leads a team at MandM Direct tasked with extracting maximum value from big data and using it to drive actions that benefit both the business and its customers. Ben has a diverse background, having studied illustration at university and practiced this for a number of years, followed by time in sales and marketing, before applying his passions for data and customer experience to his current role.
What's your experience with Customer Predictive Analytics? Any best practices to share, or thoughts on Ben's and Leo's perspectives?