There are many ways an e-retailer can enhance the shopping experience of a customer — through its communications on social media, by email, SMS, offline, etc. but also (and in a larger proportion) by personalizing what the customer sees on its website. It could be the products, filtered or sorted by the potential tastes of customers, or the recommendations pushed while checking out of the store. How do you put in place such personalization of the customer journey? By knowing your customer!
E-retailers have the chance to capture a lot of information about the purchasing habits of their customers. At an individual level, they are usually able to say when they visited the website, when their last purchase was, what category of items they usually buy, and many other behavioral events. With the mass of customers, counting thousands if not tens or hundreds of thousands of them, strictly individual personalization starts to be very challenging. It is not possible to create one specific marketing and selling strategy for each individual customer. That is the reason why retailers use customer segmentations. It is the best way to rationalize action by identifying different segments of customers, sorted by their similar behaviors. But what kind of segmentation are we talking about?
How Should I Slice My Customer Base?
There are infinite criteria on which to base a segmentation model and, the more information that is stored about their behavior, the more specific it can be. Most importantly, it is the future actions we want to take thanks to the model that will drive the selection of the criteria. For instance, a segmentation model based on demographics like gender or age might be useful to orient marketing to target the right people (especially relevant for clothing stores). There could be criteria on customer status, shopping behavior, or even psychographic elements (like beliefs or personality traits).
But the usual thing retailers are interested in when considering what marketing strategy to use is the potential for sales. Are we speaking about a customer that is on a buying spree and has high chances to buy again or a customer that buys less and less frequently, with smaller and smaller basket totals? These two different segments might need very different marketing incentives. That’s what RFM segmentation is best at: identifying the customer that bears the most value. Let’s deep dive into this segmentation method.
Quickly detailing out this acronym, here’s what we find:
- R for Recency: When was the last time the customer purchased something?
- F for Frequency: What is the number of total purchases made by the customer?
- M for Monetary: What is the average amount spent when purchasing something?
This type of segmentation will help answer many questions a digital marketer might ask his/herself about who composes the customer base: Who are the customers that spend the most? Who are the most loyal ones? Who are the customers about to churn? Answering these questions will later help to activate the customer base efficiently.
Entirely based on the purchasing history of customers and disregarding other criteria like demographics, this point of view on customers is uniquely driven by buying behaviors. It’s possible to add in more criteria of course (i.e., if the goal is to study the behavior of “HVCs” aka High Value Customers).
Highly used by digital marketers or customer growth departments, RFM segmentations are a foundational step in understanding customer behaviors. This is why the Business Solution team, when deciding to create a pre-packaged project to segment customers, focused on this type of model. Let’s look at what you might find in this solution exactly, but first a reminder of what a Business Solution actually is.
How Can Dataiku's Business Solutions Help You Reach Full Potential?
Business Solutions are Dataiku add-ons accelerating the way to achieve advanced or foundational industry-specific use cases within your organization. They are an operational shortcut to achieve real-world business value. Taking advantage of Dataiku’s core features, they are built to be fully customizable and entirely editable.
They come with:
- A user-friendly interface that enables fine tuning to match specific business requirements
- Ready-to-use dashboards that can be customized
- Documentation and training materials
Dataiku industry specialists develop solutions for every vertical, among which:
- Retail & CPG: Distribution spatial footprint, market basket analysis, RFM-enhanced customer lifetime value, product recommendation
- Financial services and insurance: Interactive document intelligence for ESG, modern insurance pricing, AML alerts triage
- Health and pharmaceuticals: Omnichannel marketing, accelerating drug repurposing
- Manufacturing and energy: Predictive maintenance, outliers detection
As a result, business professionals experience a boost in AI productivity and can rationalize their resources.
How Does It Work in Practice?
The RFM Segmentation solution provides a reusable project wireframe to accelerate the development of analytics tailored to your data and business structure.
Learn more about Dataiku's RFM segmentation solution in the video above.
With this solution, marketing analysts, CRM specialists, customer growth experts, or marketing directors are able to:
- Assess consumer RFM using a segmentation model using the period of reference of their choice and have a look at propagation (evolution over a certain period of time).
- Visualize the results through a webapp composed of various tables with different points of focus: exploring segments, finding active/inactive customers, etc.
From a user perspective, the solution is made of the following easy-to-use components:
1. Analyze Your Results on the Dynamic Webapp:
At the heart of the solution’s main differentiator is the webapp that enables users to interact dynamically with the results of the segmentation.
2. Compare Past and Future Periods
See how customers propagate from one to the other, which is useful for spotting any evolutions of your customers from one segment to another.
3. Explore the Different Recency Scores
Understand the distribution of the segments, and do the same with frequency and monetary value.
4. Analyze the Total Revenue
Explore the revenue you generate from your customers depending on their RF segment.
Start implementing your RFM segmentation model right now, with these simple requirements:
- Historic data on the past transactions
- Dataiku version: 9.0 or later