How banks can leverage AI: Segmentation as one-two-three!

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. 🙂 

Recommender Systems and Banks: Precious Recommendation

The client’s path, from conceiving an idea to making it a project in the bank, used to be clear, but unpredictable. It involved a potentially noticed ad or the client’s own idea, a visit to the bank counter in person and the more or less successful deal with the bank. It was very time consuming, with very little control from the bank, and even less efficiency. It was very hard to accept this method in an industry that is proud of the motto “time is money”.

Online communication of a bank and a client seems even more chaotic at first sight. Random clicks on the websites in the search for the information, answers to the key questions, wandering around, looking for the needed service.
However, things do not have to look that way. Human behaviour in attempts to communicate with banks is usually all but chaotic.

First Step – Creating a System

The best cure for chaos is – implementing the order. In the online communication between clients and banks, that means identifying the options to get the best possible outputs from the system, relying on the inputs.
“Recommender Systems are used to provide the best recommendation of our product that would interest the client most (system output), based on the user data (system input)”, Things Solver expert for development and implementation of Recommender Systems Strahinja Demic explains, using the company’s definition.

These systems can be classified according to the system input, system output or according to the algorithms that operate in the background and create the recommendation. First two classifications are created in Things Solver and are based on the practical experience.
“In the classification based on system input, we can identify inputs from online (user visits to the website, for example), inputs from offline (user data kept in the bank’s database), or the mixture of both kinds of data. In the classification based on the system output, the recommender can propose products the user already experienced or those that he might be interested at, but still does not have them; or the mixture of both kinds”, Demic describes the systems.

The New Approach for the Banks

Recommender Systems made by Things Solver make their way into the banking systems through the online sessions data.
“An online session is defined as a client’s visit to the website and its entire activity on the website – for example, the path through the website, time spent at certain pages, choice of links. Based on that data, we try to describe what client actually wants – the client’s visit to the webpage for loans or his attempt to make a calculation can send us a signal that there is some interest for taking a loan”, Demic explains.

The outcome of the process is a package of five products the client analysed and five products he might be interested at, but had no direct experience with it so far – only the algorithm noticed the potential interest for the product.
“We can notice the interest even before they show it at one of the banks for at least 10 percent of clients, and that is not a small number. The percentage would grow if those clients would be contacted in order to maintain their interest”, and this is the moment when, as Demic explains, the managerial structures of banks became more interested for the Recommender Systems.

But this results do not make only the management happy. The other employees are satisfied as well – without many changes in the procedures, since people rarely welcome big changes, the results and efficiency are improved. This recommendation then definitely deserves the adjective “precious” in its description.

Big Data and Banking: How Our Data Protects Us

Some of the main tasks for the bankers are, among others, keeping, preserving, deriving more from less, taking the best out of what the clients deposit to them, with great confidence and trust. Almost the same definition could be applied to the Data Scientists dealing with the big data. They also deal with keeping, preserving, deriving more conclusions from a little information, getting the best out of the existing databases which they handle with great care and confidentiality. It is maybe because of this similarity in definitions, or maybe because of the fact that the databases in the banks are an ideal example on which the detailed analysis could be performed and the conclusions can be used to benefit both clients and banks that the financial industry is among the top users of Data Scientists services, according to the credible surveys.
One of the leading banks in the Serbian market, Banca Intesa has a client base of around 1.5 million. For Banca Intesa COO Aleksandar Stojadinovic, it is obvious that those databases should be treated as an important asset. When used properly, it could bring notable benefits for both clients and the companies.
As a part of the worldwide group whose headquarters is in Italy, Stojadinovic says that the use of big data in Serbia has never been closer to the world trends. “In the world of globalization, we have at our disposal all tools that are available to the most powerful companies in the world. The only thing that limits us is the lack of talents and specialists, as well as the size of the market and average living standards of the population.”
Banca Intesa decided to make a combination of their strong internal team and the experts of Things Solver in order to start a joint search for the solutions that can be applied right away.

Short Sprints Lead to Results

Stojadinovic describes the cooperation of data scientists and banking experts as an explosion of energy and ideas. “On one hand, we have Data Science experts. On the other, we have very experienced experts in banking, and they thoroughly know every issue we are trying to solve.” The last issue the team tackled was how to improve the user experience in the social networks. Findings on clients, their desires and needs in the communication with the bank, services they find important and the ways how they found them – it is all already in the big databases. The answers just need to be properly derived.

Intesa’s COO describes the process of looking for the answers with sports vocabulary. “We start from the long-term vision and we try to follow this vision through short sprints that last 2 to 4 weeks, providing solutions for parts of the problem or improving the existing solutions.”

Data as Our Shield

If we decide to stay within the sports vocabulary, it is important for the banks to remain loyal to the rules of fair-play, stay in the track of respecting rules, laws, and safety of the data. Intesa claims to be fully aware that the mutual trust is in the center of the profession. “There is an ethical, professional and legal limit – and that is good. On the other hand, it is clear that the limit is imposed only over the initiatives that can cause harm to the clients, while for everything else – the sky is the limit”, Stojadinovic concludes.

Because of the fact that there is no limit, the winner is the one that uses this ethically, professionally and legally limited space in a more creative and innovative way. “We use the data that we have to prevent frauds, to analyze and improve security, for risk assessment and analyzing the loan potential”, says Stojadinovic.
This is the way how the data that we willingly share, in the times when everybody is worried about how they are treated, is becoming our shield from frauds. This is how the data scientists and bankers get back to their original mission of preserving trust.