Segmentation is a widely known term, especially within the marketing sector. It is an insightful, effective and actionable approach to better understanding a customer or client base.
What is so attractive about leveraging AI specifically within the banking sector is the amount and variety of data. Segmentation per se is not the final outcome of the analytics process. If is often used as the initial step or auxiliary step in the process of “customer 360”. Insights about customer segments can be used to get closer to the tailored marketing strategy and personalization.
RFM segmentation is one of the most popular approaches used by marketing and business analytics teams, in order to group customers with similar descriptive features – recency (R), frequency (F) and monetary value (M).
Years before machine learning has seen the light of the day, RFM segmentation has been performed by binning and ranking the customers based on these three features. Namely, each feature is tiered into four or five bins, where the bins are assigned ranks from 0 (lowest/worst) to N(highest/best) in terms of the feature being analyzed.
In regards to all said: the higher the final segment rank – the better. Impressive! I really like the simplicity and mightiness this approach possesses!
Nevertheless, there are some drawbacks. We are obliged to always use fixed bins, and assign ranks that could result in rough separation between segments, and also, what if we have two pretty similar clients that have fallen into different segments due to the slight difference between their recencies (e.g. customer with final rank 455 and customer with final rank 555)?
There is a solid ground for this from the business perspective – clients are best identified by their spending habits. If we know how much they spend and how active they are, we can anticipate whether there is a fertile ground for building strong and loyal relationships.
RFMT is one of our first and most commonly used methods for performing a specific customer segmentation based on the activity level and spending habits. “T” in RFMT stands for tenure, the duration of the client’s lifecycle. We have introduced one additional modification more, in regards to the standard RFM model. We’re not binning the data and using ranks – but we use clustering techniques based on machine learning algorithms.
One of the things I’m especially proud of within our platform is the perseverance of simplicity – all our modules can be easily objectified, understood and utilized by non-tech users.
The only thing we need is the anonymized data – and all the magic happens within our platform – no manual work or additional involvement of the client is needed! (unless the opposite is especially requested by the client). A brief overview of what you can expect from our “Customer importance” module is depicted below.
You may as well track client’s transitions through the segments over time – and thus model the customer journey and define triggers for marketing automation when some specific event happens, e.g. falling from “champions” to “sleepy” segment.
Within our Solver AI Suite – segmentation is an integral part of Solver Personalize pillar (which besides includes recommender system and customer lifetime value estimation). By combining outputs from these three modules, we support marketing automation through audience creation and campaigning (campaigning will have its shining moment in our future posts).
What about the results? – you might ask. We’ve got 30% higher conversion rates by targeting a specific segment of customers, which we have identified as potentially loyal, by a tailored offer. On the other hand, we have proven that the so-called “champions” will come either way, and that they don’t require a special discount or often targeting. This further implies the increase of sales and reduction of unnecessary costs.
So, let me ask you a question – what are you waiting for? Drop us an email, and we’ll help you out. 🙂