Plug and Play with your own Data
Use the Dataiku Application to easily upload your data and adjust the project to your needs.
Explore !This project offers a plug-and-play solution allowing retail analytics teams to compute RFM scores and conduct customer segmentation based on those scores. With easy tailoring of the project through a Dataiku Application and the capacity to adjust the data flow to their needs, the RFM Segmentation solution enables retail companies to segment customers based on their purchasing behavior and design more tailored engagement strategies. 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.
Business Overview
Personalization is a huge opportunity for Retail and CPG businesses: 80% of companies report seeing an uplift since implementing personalization, which includes engaging with consumers with the right content, offers, or services.
In order to do so, a key step is to identify purchasing patterns among consumers.
- When was the last time each of the consumers purchased a product/a service?
- How regularly do consumers complete a purchase?
- What is the average amount spent?
Those questions are strategic for brands in order to make the right decisions depending on each consumer’s purchasing behavior. While several techniques can be used to do so, one that has been tried and true is the RFM segmentation. It answers the 3 questions listed above by focusing on the Recency, the Frequency, and the Monetary value of the consumer purchases. Once every consumer RFM has been assessed against those 3 criteria, consumers are brought together into homogenous groups of users (segments): from the “hibernating” one to the “champions” every consumer belongs to one segment which can evolve over time depending on the purchases made. Brands are then able to better engage with consumers: at an optimal frequency, through the right channel, and with the right message. Doing so will be beneficial for both: the brand will foster loyalty and increase the consumer lifetime value, while consumers will have a better purchase experience while benefiting from a more personalized journey.
Highlights
- Input transaction data to generate consumer segments based on their historical purchases pattern (Recency, Frequency, Monetary Value)
- Define the solution settings through a user-friendly Dataiku App: transactions preprocessing, RFM computation (data filtering strategy, computation technique, etc.), RFM
- propagation to extend it to a larger period of time
- Consume the output and learn purchasing patterns through interactive dashboards aiming at exploring your consumer segments by value and making the right decisions
- Rerun the entire data pipeline on new data using a simple application
- Automate the pipeline to rebuild with new/fresh data
Data Driven Insights
Thanks to the flow, easily understand the project structure, navigate data transformation steps, and tailor
to your needs as needed.
Build the present, explore the past, activate the future
Build the segments from your current consumers and propagate them in the past to witness how consumers evolve from one segment to another over time.
Explore !
Interactive Dashboard For Consumer Segments
Extract your consumers' distribution across segments. With the treemap, get a view of the "Recency x Frequency" segments occurring in your dataset, depending on their respective monetary value.
Explore !
Automate the pipeline
Define your automation strategy and set up the full flow automation accordingly.
Explore !