More than 40% of marketers are looking at using AI for customer segmentation.  AI improves targeting, personalization, and engagement. It gives banks more time to interact with consumers at the time of need. AI-driven business strategy should be a top priority for banks & financial institutions that want to succeed in a hyper-competitive marketplace. For example, if you look at disruptors like Netflix or Amazon, their customer segmentation strategy is driven by AI so that the appropriate content or product recommendation can be made. They have created the benchmark of hyper personalization due to early adoption of AI. Banks have been tad slow in adopting AI and part of the reason for  their reluctance towards new technology is the lack of a global data repository. Fintechs meanwhile have embraced it and have been threatening to disrupt the banking landscape by taking the customers away from them by providing easier and efficient banking solutions.

Customer segmentation has become the Holy Grail for banks and for good reason. Pitching the right message to the right customer and at the right time has been the objective for all banks. Marketers are at it every day and trying every trick in the book. Now with AI & ML coming into play, Marketers can look at micro-segmentation and building multiple buyer personas. Micro-segmentation means better probability of conversion rates and better targeting. It gives marketers number of options compared to the existing customer segmentation methods.

Banks look at customer segmentation to gain insight, on how to decide on specific offers, improve customer service, and understand customer behaviour & more. The success or failure of a marketing campaign depends on how customers are segmented. Based on the customer segmentation, banks unleash product recommendations like saving plans, loans, wealth management, etc. on target customer groups. Pitching the wrong product can, not only lead to customer dissatisfaction but may also force them to look for alternative options. When the right messages are pushed out, relevance and context is created which can ensure better customer response. Most customer segmentations genera in use today are quite generic and tend to be focussed on larger filter criteria than specific. Age, location, marital status, place of living is some of the common segmentations that happen due to limited bandwidth or lack of insights.

In the post digital era, another bit of complexity has been added to the existing mix, which is the customer’s online profile and footprint. Information about what they do online and where are they get their information about new offers and how are they get influenced should be taken into consideration if you want to serve today’s tech-savvy customers. Now banks have to worry about not just what to offer but also which digital channel to offer and where. Manual process of analysing these data is not a viable option. This is where AI/ML plays a very important role. These algorithms can evolve and learn about customers digital profile and even predict their actions which will help in getting the right message across at the right time.

There are several tools that are currently available in the market, which helps us follow customers and prospects interest in the digital world. These tools detect offers they were interested in and the time spent on that offer page. AI/ML can, not only build the customer segmentation profiles but also follow up with appropriate actions. For example, if the customer did not make a purchase, an AI/ML solution can help in addressing that situation by taking a corrective action, which could help in influencing the customer to make a purchase. As sophistication develops, AI/ML could help in increasing the conversion rate in such scenarios. Now imagine, hundreds of similar scenarios where a customer leaves without making an action to purchase a product and AI/ML can make a significant impact towards conversions. Once the customer segmentation is perfected, better customer engagement, better online customer experience and ultimately brand loyalty follows.

One of the unique challenges traditional customer segmentation strategies fall short is how you can keep segmenting real time. They capture the segmentation at a particular point of time but not the customer journey itself as habits and needs keep changing and evolving over a period of time. For example, a customer can move from segment to another based on personal or professional changes. But with AI/ML in the picture, continuous customer segmentation is possible and offers for that customer can be changed appropriately on the fly.

All the above tricks are possible only if the bank has a data lake. AI/ML benefits can be reaped only if proper data hygiene is followed. Customer segmentations and recommendations are possible if data is stored in global repository for managing, storing, classifying and analysing the data. Before joining the AI/ML bandwagon banks needs to make sure they have a data lake as part of the data monetization strategy.  Banks that can derive value from all their data which can be made accessible to different business units across their organizations are well placed to deliver exceptional customer experiences with the help AI/ML.

By removing human bias on customer data, AI/ML algorithms can give objective insights. Finding hidden insights is a complex task under current process but AI/ML make it easy and real time. As the ML algorithm evolves, updating segments automatically and creating new micro-segments happens on the fly. This enables banks to provide ultra-personalization.

Customer segmentation challenges