In the financial sector of Accra, two banks have the same customers and are experiencing two distinct realities. The first bank sees customers line up for hours to perform a task, only to be frustrated by the long process and generic services. Customer dissatisfaction persists, complaints grow, and the bank struggles with decreasing profits.
Across the street is the second bank. They welcome the customer by name, provide personalized service, and create a ‘frictionless’ experience that anticipates the customer’s needs. The difference between them does not stem from their ownership or construction. It is their mastery of data.
This story demonstrates an incredible banking revolution taking place around the world regarding customer relationships and revenue growth. In Ghana and across West Africa, banks are discovering that customer retention and profitability depend on their ability to collect, store, analyze, and act upon vast amounts of data (not all banks).
The growth has been sparked by the countless digital touchpoints in every customer’s journey. Whether through mobile money transactions, deposit preferences, or investment decisions, every digital transaction creates data. This data can then be used as a foundation for creating even greater loyalty and revenue effectiveness.
According to the Market Research Future Analysis, the Big Data Analytics in Banking Market is expected to reach USD 10.56 million by 2025 and USD 29.87 million by 2030, achieving 23.11% CAGR! This rapid growth is indicative of a substantive transformation from transactional interactions to a strategic, integrated partnership with the client that is based on a unique understanding and personalized value creation.
For Ghana’s banking sector, the implications are profound. As digital financial services continue to expand, with new fintech competitors nearly every week, traditional banks must adapt or become extinct.
Transforming Banking: The Data Revolution
Banking has undergone a profound change from product-led business models to customer-led business models utilizing data analytics. This approach is a fundamental re-engineering of how banks understand customers, interact with customers, service customers and retain customers.
In banking, scalable data means collecting, processing and analyzing vast quantities of customer data in real-time and leveraging that data to deliver personalized experiences across all customer interactions.
For Ghana banks, the data revolution comes at a critical point in time. The banking marketplace has undergone considerable consolidation in recent years, compounded by competition from local and international competitors and the growing and aggressive fintech sector.
In 2025, commercial banks that make data analytics a priority will have a more efficient operation that will help them drive profitability and enhance their customers’ ability to receive insights to make informed decisions in increasingly complex environments. Banks that employ advanced forms of data analytics report significantly higher customer satisfaction and engagement measures, but importantly, lower attrition and higher cross-selling opportunities, than the banks that follow the more traditional approach.
The revenue impact of data-driven banking
Importantly, the financial implications of scalable data analytics systems go beyond just cost savings from operational efficiency. Banks that leverage customer data successfully have the capability to create multiple revenue streams while simultaneously reducing costs, thus triggering a compounding effect of profitability that contributes to a competitive advantage.
Cross-selling and up-selling represent direct revenue opportunities. Traditional banks have time-tested methods to achieve 5%-15% cross-selling with the use of broad demographic segmentation and generic campaigns. Banks that leverage analytic insight consistently achieve cross-sell success rates between 30%-50% because analytic insights enable banks to identify the right product for the right customer, at the right time, by determining what action to take based on behavior, important life events, or transaction history.
With greater analytical marketing precision, the cost to acquire customers decreases significantly, and the overall quality of new customers increases. No longer relying on costly campaigns for mass consumption or worse, experiencing marketing fatigue, banks can utilize analytics and leverage their marketing dollars efficiently, discovering high-value prospect segments and specifically crafting messages for targeted segments. Costs will be reduced by as much as 20-40%, resulting in new customers who are far more likely to become profitable, long-term, low-risk relationships.
The analysis suggests that if banking productivity can increase by 20-30%, revenue for the banking system can increase by 6%.
Implementing Scalable Data Solutions
Implementing scalable data solutions must consider approaches to balance technology capability and organizational change management. Success will require banks to make significant changes in the way they collect, process, analyze, and act on information about customers.
The foundational responsibility will be data infrastructure and governance. They will need robust systems in place, collecting data from every customer contact point – mobile app, website, branch, call center, and third-party sources – that ultimately have accountability over a customer’s data. They will also need to make sure the data is standardized, cleaned, and stored in accessible locations where it can be analyzed and acted upon in almost real-time.
Governance of data will also need to be thoughtfully considered, as governance becomes crucial as the analytics capabilities scale. There will need to be clear policies covering how data is collected, stored, used, and shared, that allow for compliance with regulatory requirements, while at the same time maximizing the value of information about customers. In many of the markets in which Ghanaian banks operate, there is a different approach to local regulations on data protection, compared to how they would prepare themselves for international expectations on data protection.
Future Trends and Opportunities
The future is likely to be even more sophisticated through technology that further builds understanding about the customer relationship, as well as provides revenue generation opportunities.
The AI and machine learning technology is evolving rapidly to facilitate the processing of increasingly complex patterns of data relationships and assist with predicting customer behavior with a greater level of accuracy.
