Customers today are spoiled when it comes to choices. They choose the products and services most relevant to them. Now, in order to stay relevant, banks need to get closer to their customers and understand them better than before, compared to their peers. Only then, through personalized and relevant offers, can banks significantly improve the success rates of their marketing campaigns.
In marketing, segmentation has been a fundamental building block of understanding customers’ behavior. As a concept, the advertising model core to today’s large search engines, social media networks, and e-commerce players is well-known to marketers at banks. However, they continue with traditional approaches based on demography, geography, and socio-economic classifications. Yet approach to segmentation has remained largely one or two dimensional. Getting reports and cross tab analysis on customers has been a struggle for most bank marketers for a long time until recently.
Micro-segmentation utilizes multiple dimensions to identify a set of customers, potentially pivoting around a business outcome – in this case the probability of the customers to accept an offer or buy a product.
Those of us who’ve had the opportunity to work in line functions, as well as technology at different times over our careers, know how technology has changed and become easier for even the untrained but curious user to undertake in terms of in-depth analyses versus what could be done 10 years ago. It has become very easy for the average business user to slice, dice and visualize data without almost writing a single line of code. It is not difficult for the curious users to come up with a matrix showing “people who have bought this, also bought that” using pre-built formula in spreadsheets. Hence, one may wonder why it is that marketers in retail banks are not delving deeper inside their traditional monolithic customer segments to uncover micro segments that will help them understand and serve their customers better.
The most common reasons provided by marketers about why they are not ‘micro-segmenting’ their customer base are as follows:
- Yes, we know about it and understand it, but it is too complicated for us to implement.
Users today are slicing and dicing their data starting from cross-tabs and pivot tables using spreadsheets to having an in-house team of “data scientists” or people with the knowledge of sophisticated statistical and mathematical modelling techniques. Most banks are somewhere in-between. In such cases it is worthwhile to start with something simple and small. For example, start with two or three attributes to segment the customer base before moving onto using more attributes to identify customers by needs that your organization’s products or services can uniquely address. This essentially breaks down a large problem into smaller bits.
2. We cannot conceptualize how it will work with our data in our business context in a meaningful way to impact our business goals
This is an interesting view. As technology specialists, we have the access and know-how of the tools that make data analysis fast, efficient, and effortless. While we may possibly stumble upon interesting insights based on a bank’s data, the best way possibly would be to enable the business users with similar capabilities related to data analysis based on technology and tools. It goes like this: it is extremely difficult to create a stone sculpture using a toothpick rather than using an iron chisel. However, it is still the sculptor who make the fine piece of art and not the blacksmith who made that chisel. Technology specialists can demonstrate all that is possible based on their views of the bank’s business and operations and perhaps work together as a team to help the bank in this pursuit.
3. We do not have enough and meaningful data
This is still one of the common reasons cited by business users. This is relatively true given that organizations, however big or small, (apart from internet search giants, social media companies and the like whose entire business model is built on leveraging user data) do not have data consolidated and aggregated in a single place. Even large banks face this problem. With more and more transactions and interactions channels, this problem is further accentuated given a customer may have used an ATM, called the call center, browsed the mobile app, and dropped by the branch to inquire about a new product. Finally, from a data perspective, there is usually a gap in terms of capturing the unstructured interactions and conversations that customers have with their relationship managers or the bank channels. If decision makers do believe that using three attributes are better than two attributes in understanding and defining their customers better (e.g. overall assets with the bank, monthly aggregate transactions, most recent interaction with the bank), it is a significant step towards a micro-segmentation mindset. With the business users’ requirement clearly stated to their IT, i.e. getting all transactions, interactions, profile and expectation data of their customers in a single place, the key ingredients will now be available for a journey in micro-segmentation.
Banks possess a treasure trove of transaction data that is by far a more reliable source to understand customer needs compared to the casual posts and chatter on social media or the browsing and search patterns on websites. With the three basic perspectives outlined above, banks can significantly become more aware of their customers’ needs and offer relevant choices to them as a result. This will noticeably improve the outcome of their marketing campaigns.
The article was originally published on BankNews (July 2016) and is re-posted here by permission.