Loyalty mission. Retail banking faces a challenge: that of building lasting and trusting relationships with its customers. The proliferation of digital competitors, which are smarter and cheaper than traditional players, has created a scenario in which the more informed and digitally savvy customer is increasingly aware of alternatives and is willing to change banks to find services better suited to their characteristics as a customer, saver or small investor. Already in the “World Retail Banking Report 2016“, it was highlighted that only 55% of customers were certain to keep their bank in the next 6 months. This figure gives a pretty clear idea of the “churn risk” that Bnakis have to face daily.
AI custom software for Banking
At Premoneo we develop tools based on Artificial Intelligence to help Banks in this crucial business challenge. Indeed, it is the data science that is the key to dealing with this scenario of uncertainty.
It is one of the levers with which, so far, Fintech companies have managed to attract more and more customers than traditional banks. A survey conducted by Salesforce showed that 55% of millennials prefer to make a payment through the services of a fintech rather than use a similar service offered by banks. The same research found that 13% of millennials have changed their bank to a digital one in the last 5 years.
The areas of development where we are working to bring Artificial Intelligence into the management processes of traditional banks are two: pricing and marketing.
When it comes to product pricing, banks are often constrained by limited margins and regulatory constraints. Therefore, it is crucial to assess the net profitability dynamically. It is important to keep in mind, however, that customers need not be attracted only by low prices or discounts. Many factors can influence this decision: demand, the availability of a similar level of service in the market and the urgency of the service. Add to that the ability to track pricing levels and competitor offers in real or near real-time, and you have a scenario where the traditional bank has a data set at its fingertips at all times that allows it to build better pricing models for the dynamic market. Only a pricing platform designed with an understanding of the decision-making processes of the prices applied by banks, capable of providing forecasting tools and the study of the elasticity of demand, can allow pricing analysts to modernise and speed up their price effectiveness control and repricing actions.
The impact of AI in customer relationship management
Relationships with current and prospective customers can also evolve, thanks to Artificial Intelligence applied to marketing.
With the right tools, banks may be able to periodically update the profile of their customers and calculate an estimate of their ‘potential value’. A customer acquired at t=0 thanks certain products with certain prices, could have changed, at t+1, his situation acquiring a different appeal for the bank itself. To be able to start a process of continuous enrichment of one’s database, also thanks to the acquisition of external data, it is necessary to have a technological environment capable of always receiving new information on the customers already in one’s portfolio: this would allow one to identify the precise phase of the customer’s life to which specific needs and preferences are associated, to be able to propose the most suitable products and services at any given time.
Machine Learning, personalised offers and rewards for customer loyalty
To process information about their customers, banks should have the tools to implement machine-learning-based analysis to create increasingly specific clusters based on consumer behaviour and preferences. Only based on this work should banks send offers and rewards to customers to engage and inform them frequently. The introduction of reward systems leads customers to take advantage of offers by purchasing services they would have postponed or avoided. Increasing loyalty through the creation of advantageous offers or the addition of ‘premium’ services allows the customer to feel more satisfied and thus decrease the churn rate. Sprinkling these activities on territorial clusters, as is often the case, is relatively ineffective and risks hurting off-target customers.
Data science and associated AI represent the present and the future of the banking sector, as it is now accessible to banks of all sizes. Only these tools, developed ad hoc and implemented in the banks’ IT ecosystem, will allow them to make up the ground lost to fintech companies and become competitive and appealing to both traditional and ‘digital’ customers.
