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.
- Enrich your customer segmentation approach by blending machine learning and existing techniques, deepening product expertise, and marketing effectiveness.
- Business-friendly explainable AI allows your team to quickly create and then immediately understand the results of machine learning-based segmentation, without complex development.
- Powerful visual analytics clearly reveals customer segmentation trends over time, ensuring your past, current, and potential future customer mix is effectively understood by all teams.
- Instantly actionable insights allow your marketing specialists to instantly understand revenue share, product mix, and much more, all through prebuilt cross-sell, tier, and segment analysis dashboards.
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.
- Requires Dataiku v9+
- Prior to installation, your Dataiku instance Admin will need to install plugins. The full list of requirements can be found here.
- This plug-and-play solution can be installed and used right away in one of two ways:
- On your Dataiku instance click + New Project > Industry Solutions > Search for Customer Segmentation for Banking
- Download the .zip project file for your Dataiku version and import it directly to your Dataiku instance
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.
Powerful visual perspectives
Explore and better understand not only your current customer portfolio but also historical trends, allowing for more effective planning and marketing strategies.
Business-interpretable model insights
Ensure effective interpretation of the model’s findings through quick and insightful analyses of key drivers.
Plug and Play Application
Using the step-by-step Application, load your existing customer data into Dataiku quickly and easily, getting immediate and actionable results that can be presented directly to business users and management.
Versatile and effective customer analytics
Perform as much of your existing and future customer analytics within Dataiku as you wish, using our pre-built dashboards (or your own custom creations) to view new machine learning driven insights integrated alongside existing analytics, including Cross Sell and Product Mix.