Compliment existing expertise with ML
Develop comprehensive insight into your customer base by aligning your existing tiering and cross-sell analysis with data-driven clusters derived via machine learning.Explore !
The goal of this plug-and-play solution is to enable marketing teams to understand how Dataiku can be used to leverage customer insights within a robust and full-featured data science platform, while easily incorporating machine learning to better understand the customer mix. More details on the specifics of the solution can be found on the knowledge base. This solution is available on installed instances and Dataiku Online.
Insightful customer segmentation is a cornerstone of effective business management, marketing, and product development within consumer banking. Many firms have developed deep business knowledge which is applied to their customer pools using business rules logic, slicing the overall customer base into subgroups based on actual or potential revenues, product mix, digital engagement, and much more.
These existing customer analytics provide powerful insight and are often driven by qualitative insights or historical practice. Yet 82% of bank executives say their teams have difficulties identifying relevant customer segments, which can drive up acquisition costs and reduce retention rates.
Leveraging a purely data-driven approach to segmentation introduces the possibility of new perspectives, complementing rather than replacing existing expertise.
By creating a unified space where existing business knowledge and analytics (for example, on Cross Sell and Tiering) are presented alongside new and easily generated Machine Learning segmentation, business teams can immediately benefit from the incremental value of an ML approach while preserving continuity with established methodologies and analytics.