Traditional customer segmentation no longer works in banks and other financial firms. The industry must adapt to provide clients more personalised services, rather than the broad client classifications of the past.
For example, wealth management services have historically segmented their clients on wealth and service models, and those segments have remained uniform. Yet demographic and socio-economic shifts are creating more non-traditional segments that do not fit the standard approach and find their needs unmet.
Wealth managers are losing potential client segments, such as female and younger investors, because their service models lack the required customisation, digital servicing methods, and transparent and competitive pricing schemes.
There is now significant untapped potential in targeting such segments with new and better products and services. You can access this potential easily if you better understand an asset you already have – your data. But you need advanced data science techniques to do it. Such methods can evolve your segmentation by identifying and understanding more similarities and differences across customers and analysing unmet needs and non-traditional segments.
Using your data to find new segments
You can discover unmet needs using an algorithm that analyses your client data alongside product, service and demographic information. This enables you to identify new segments whose needs relate more to new factors such as user experience and behavioural preferences, rather than simply wealth level or cost-to-serve.
This will give your relationship managers a clearer picture of different customer segments and how to tailor strategies. They can do this in conversations and in marketing campaigns to attract new investor groups based on more diverse factors.
These segment insights can also allow wealth managers to innovate the products and services they offer. For example, for tech-savvy millennials, you can develop a more transparent pricing scheme and more socially responsible investing products.
Data analytics technology can help you improve customer loyalty by turning raw data into values and insights about how clients like to engage with you and other companies.
It can also reveal opportunities for cross selling and additional revenue streams by identifying product or service offering gaps for new and existing customers. You can apply a wide range of methods to achieve this including predictive machine learning, and artificial intelligence (AI) based recommendation and reinforcement learning.
One method is to identify patterns in existing client purchases and what is missing to attract new clientele. This will be at a detailed level, allowing you to understand similarities between clients, how likely they are to purchase products and services, and what they think about when purchasing.
Data analytics can also visualise this data in a way that allows you to see easily which products, services and innovations are potentially profitable for each customer segment. From that, you can adopt a more comprehensive and targeted client-centric approach.
By Consulting and Solutions Analyst, Elizabeth Basusi and Data Science Consultant, Edward Adcock.
Get in touch
Want to see what such data analysis applications can do for you?
Delta Capita’s data science team provides a powerful range of options to help you advance your customer segmentation capability. The team focuses on clear explanation and real-life application of data science. It also offers specially developed, in-house services to support your model.
To find out more, contact Delta Capita today.
.jpg)
.jpg)