By Victor AGBEVE
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 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 touch points 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 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.
Banks that recognize data as their most strategically valuable asset will have their transactional exchanges transformed into experiences that convert customers into lifelong advocates.
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. Each customer interaction leaves a data footprint that banks can use properly to design an optimized and sustainable customer relationship revenue opportunity.
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.
Scalable data also includes transactional data, behavioral data, preferences about communication, life events and social media activity. With access to scale, banks have the opportunity to conduct transactions with thousands of customers with the personalization and customization that has only previously been available to high-net-worth customers.
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.
Banks that can set themselves apart from any competing institution by providing a superior customer experience will gain market share and retain customers longer, further enhancing overall profitability through retaining more loyal customers.
By 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.
Customer Loyalty Recognized Through Data
In the data age, customer loyalty involves more than just a measure of retention through service or adopting a bank product. It involves emotional or psychological ties; trust in the firm; the likelihood of their advocacy; and their lifetime value to the bank.
Banks that recognize this evolution of customer loyalty have put their data analytics to work measuring and continuously improving upon customer loyalty across the breadth of these measures.
For loyalty measures, the banking sector has traditionally relied on time with the account, number of products used, and volume of transaction activity. While they are certainly valuable, these metrics are ultimately surface-level snapshots.
The analytics we have available today allow us to go much deeper by examining patterns of behavior that reveal the true drivers of loyalty.
This same data might show customers who engage with mobile banking functions regularly and use financial education content are still loyal as economic downturns unfold, even if those customers possess lower balances than previously considered “valuable” customers.
Recent research has established that 84% of companies investing in their customer experience are seeing their revenue grow, which just goes to show the direct impact that investment in loyalty-building efforts has on business performance.
In banks, this relationship is particularly strong because they offer financial services based on relationships with customers, who exhibit switching costs when they are satisfied with the service provider.
Cultural and economic contexts of loyalty choices in West Africa
When we consider the cultural and economic aspects of loyalty measures in West Africa, banks must adopt a broader approach to interpret loyalty. Banks will need to understand not just the individual customer behaviors, but also family and community behaviors in their financial dealings.
Data analytics are revealing new insights, and the extended nature of family relationships, the seasonal nature of their incomes, and community-based patterns of savings are especially relevant in establishing regional market customer loyalty.
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, cost to acquire customers decreases significantly and 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.
Risk management improvements will have a huge impact on revenue because they will decrease loan defaults and fraud losses, while also allowing banks to service previously underserved segments.
Advanced analytics allow very sophisticated risk models that include hundreds of variables rather than old-fashioned credit scoring.
The analysis suggests that if banking productivity can increase by 20-30%, revenue for the banking system can increase by 6%.
This reflects the combined benefits of using data-driven decision making across the entire bank including customer service, product development, regulatory compliance, and strategic plan implementation.
For banks in Ghana, the overall revenue impact includes financial inclusion opportunities that generally align with national development goals and importantly, will create opportunities to generate sustainable profits through models of innovative risk assessment and delivery channels.
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.
The human element is possibly the most critical determinant of success or failure. Banks will require teams with banking domain knowledge, technical skills in data analysis, and an understanding of customer psychology.
They will need to focus on hybrid skills through skill development within the existing workforce and selectively add expertise in areas where they cannot develop it in-house.
Any technology choices will need to emphasize the importance of scalability and integration above sophistication. They should primarily look to the cloud, as they should be able to achieve the requisite scale from cloud-based solutions and reduce the costs and time to implement technology infrastructure.
Change management is possibly the most difficult to navigate. Banks must transition their decision-making processes away from intuitive judgment-based decisions, toward processes that rely on data and analytics.
This will require leaders to be open about the benefits of transition, build confidence and engagement with training programs, and set incentives to reward the intended behaviors.
Customer Personalization at Scale
The ideal representation of scalable data analytics is being able to provide a personalized banking experience to thousands of customers at the same time.
Delivering personalized experiences at scale has the potential to transform banking from a commoditized service into personalized differentiated value propositions that customers actively seek.
Personalizing experience begins with understanding customers, and knowing the customer’s profile.
Second, developing a profile that goes much deeper in understanding beyond just demographics, to build behavioral patterns, understand preferences, establish and remember the customers’ financial goals, and identify the life circumstances affecting that person and their household.
The next level is continuity of delivery across all channels. Customers might receive personalized offers on their mobile app, but will equally have the same tailoring in a conversation with a contact center, or if they go into a branch.
For banks, providing this type of service requires sophisticated integration of information across different data sources, using the analytical models in near-real-time, to enhance their customer’s experience.
Looking towards 2025, top brands will double down on seamless omnichannel experiences using predictive analytics to deliver relevant and timely information that provides additional value on every customer journey, specifically and personally tailored to that one customer.
This will largely benefit banks and other financial service products such as credit because during their journeys, customers are consistently evolving their financial situations, from savings to purchasing and making decisions, not forgetting the importance of timing for purchase offers, as all customers are happy in retrospect with their journey.
Artificial intelligence and machine learning help banks to find personalization options that human analysts might miss, digesting massive amounts of customer data to reveal nuances and patterns that enable companies to build a personalized representation of individuals.
Success Stories and Evidence
The successes of businesses implementing AI and machine learning speak for themselves in terms of their impact on customer loyalty and overall revenue.
For example, a major bank in West Africa implemented a holistic customer data platform and increased cross-selling by 35% and reduced churn by 25% within 18 months by using customer life events as personal recommendation triggers.
Digital-first banks demonstrate some of the strongest results, with customer acquisition costs that are 50-70% lower than traditional banks and higher satisfaction scores. They have used their analytics capabilities to engineer and optimize every area of the customer experience.
Banks making the move to personalize generic loyalty programs based on individual behavior increased participation by 200-300% in these programs making them key enablers of emotional connection resulting in increased wallet share and advocacy.
Revenue implications have always been greater than planned. Eventually, banks see a 15-25% increase in lifetime values, on average, within two years of implementing a comprehensive data analytics program that specifically enables customer analytics.
The compounding effect of the improved customer acquisition, retention, cross-selling, and risk management can create a unique and sustainable competitive advantage that strengthens as the data asset and capability matures.
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 processing of increasingly complex patterns of data relationships and assist with predicting customer behavior with a greater level of accuracy.
The global loyalty management market was assessed at USD 12.07 billion in 2024, and it is expected to grow at an 8.7% CAGR through 2030, which represents significant potential for banks to develop loyalty programs based on advanced analytics.
Real-time analytic capabilities are poised to be standard expectations rather than competitive advantages. Customers will expect products and services to respond immediately to their individual situation and not just historical data. Making this happen will require streaming analytic platforms and real-time decisions that occur in milliseconds.
Conclusion
Transformation in banking enabled by scalable data analytics represents a fundamental shift toward customer-centric financial services that create value for both the financial institution and customer.
Banks that can best leverage analytics to make meaningful customer experience improvements will realize significant increases in loyalty, revenue, and efficiency that compound over time and can create a sustainable competitive advantage.
The evidence is clear; banks that have implemented mature, holistic analytics programs have increased cross-selling by an average of 20-30%, reduced churn by 25-40% and increased customer lifetime values by 15-25%.
It is difficult to ignore these results and their impact on the bottom line, as well as the associated funding for innovation.
For banks in Ghana and in other West African markets, implementing scalable data analytics to make a meaningful impact on both customers and the business is ideally suited to the regional objectives concerning development as well as market conditions.
The combination of technology driving growth of digital services, the spike in smartphone adoption, and the steady proliferation of fintech actors creates a landscape in which banks must find a way to differentiate based on the quality, relevance, and availability of the information collected on their customers in order to survive and thrive.
Starting on this journey is a commitment requiring investment and patience, but the payoff will justify the effort. As customer needs evolve, and competition heats up, banks that are able to successfully leverage scalable data analytics will flourish in the banking sector of the future.
The future is going to belong to the banks that understand the value of their data assets and invest accordingly in the capabilities required to bring that knowledge and understanding to life.
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Victor is a Former Banker | Graduate Research Fellow, W. P. Carey School of Business, Arizona State University.
