Information is critical, but it is most valuable when it describes customer behaviour
Steven A. Luckie
Vice President of Business Development – Financial Institutions
Senior Manager, Advisory
Banks have had an eye on customer profitability as a key performance metric for decades, yet approaches continue to vary widely in methodology and outcomes. All banks want to measure customer profitability, analyze it, and apply the results of their analysis to product development decisions: Banks want to answer fundamental questions:What do our customers need? What products or services should we offer? How do we effectively market our offerings to meet our customers’ needs? Which customers should we focus on? Which customers appear to be profitable, but are not?
Banks, however, confront a number of challenges in evolving the customer profitability metric. These include the inherent complexity of banking organizations, the siloed nature of internal data management, inadequate costing models, and—ironically—shareholder pressure to generate profit. Still, as they strive to meet the evolving needs and expectations of their customers, banks need to work toward a customer profitability measure that effectively balances traditional costing methods a complex operating environment and an ever-evolving consumer landscape.
Complexity, Data, and Costing
Transforming your approach to customer profitability is clearly a difficult proposition. A bank is a tremendously complex network of organizational structures, strategies, people, systems and information, often operating discretely. With the Marketing department often responsible for measuring customer profitability, they tend to assess success or failure relative to specific marketing campaigns for specific products or promotions. Whatever customer information or knowledge is gleaned tends to be siloed, not necessarily aligned to broader business drivers or strategies, and certainly not a usable aggregate view of product profitability across geographies or customer segments. This segmentation is not just a choice or an accident of corporate evolution; rather, individual groups and bank functions must remain agile and independent of the whole. For example, online banking must be clearly differentiated from retail banking and marketing. And if there is no larger enterprise focus on integrating capabilities then communication and evolution become problematic.
To complicate matters, what if a bank client is also an insurance client, a small business owner, or has an investment account with the bank? Integrating the array of information these relationships generate to deliver a single, unified client view is certainly a customer profitability panacea, and banks have had some success in understanding this across a single channel, such as in branch banking. But the bank-to-customer relationship needs to be 360 degrees, and in all directions—a seamless three-dimensional understanding that connects mobile banking, social networking, ATMs, and telephone banking, as well as traditional branch interaction. Integration, however, has not kept pace with channel expansion. Silos persist and overcoming complexity remains a problem.
Costing, a key customer profitability input, is similarly affected by complexity. Most banks rely on an activity-based costing model, typically creating cost ratios that they then apply to the variable component of costs—for retail banking, the selling costs of products and services. Again, this works reasonably well for a single channel but offers little perspective across multiple channels. Clearly, there is an opportunity for banks to enhance the way activity-based costing feeds into customer profitability. This requires a costing model that integrates costing across all channels. The model will have maximum integrity when it helps banks understand profitability at the customer, product, and channel levels.
Real-Time Information Creates Immediate Opportunities
Collecting and applying ongoing, real-time information to the process is a critical initial step. Monthly data on transactions, costs, revenue, sales, and transfer pricing is not necessarily stale data, but neither is it immediate. When a customer visits a bank representative, the interaction is generally managed with relatively sophisticated customer relationship management tools that often include positioned sales offers of the latest products. Such prompts are often based on some level of specific customer analysis, but if that analysis isn’t based on the most current information, sales opportunities can be missed.
By using customer profitability data at every channel across segments and geographies, banks can gain a much clearer picture of not only where they generate profit, but of what type of profit specific customers tend to generate. Importantly, real-time access does not have to mean instant access to the bank’s complete data set (an unrealistic capability at present), but it should apply to data used to identify and deliver specific offerings. At the most sophisticated level, this would deliver targeted offers based on customers’ current and most relevant financial behaviours—increasing sales, bank profitability, and customer satisfaction. Ideally, this happens not only at the front-end channel, where significant investments have already been made, but at all channels—including ATM, mobile, Internet, and phone.
When you have complete profiles for thousands of similar individual customers, you can begin to make inferences about customers entering that segment and begin to drive profit in the most immediately reactive ways, while also letting customers know just how much you are paying attention to their needs and expectations. No matter what channel a customer goes through, the bank should be able to address their needs effectively within the expected timeframe. Real-time offers through online channels should be made immediately or that, too, is lost profit. While this can be done with existing tools and processes, banks generally do not employ real-time interaction and data integration at this level.
What Will Prompt Change?
While this kind of profitability analysis is common to Internet-based businesses, it is very complex and expensive in the “real world.” For organizations already achieving a certain level of profitability, the necessary changes can be hard to justify. But the pressures are there and mounting, including:
- Competitive pressure–Although new entrants to the Canadian banking market tend to be niche-oriented – for example, those that cater to a specific demographic or offering mono-line (typically Credit Card) products, the cumulative effect of smaller players is likely the erosion of the major Canadian banks’ market share for certain types of products or in these target demographics, limiting customer and growth potential. In addition, following the Global Financial Crisis, Canada’s reputation as a sound environment for financial services has attracted many foreign institutions – each of which may appeal, in part, to the existing Canadian customer base.
- Internal pressure—Profitability data often resides within the Marketing function and is not typically shared throughout the enterprise. Other lines of business, however, are calling for this information in formats they can apply to product development, customer intelligence, and in the development of line of business strategies.
- Simple business intelligence—Going forward, bank profitability will increasingly rely on data clarity, consistency, and currency. Consider the example of transfer pricing ratios: How old is the data that is used? Where did it come from? How much does it reflect the data it is being applied against? If current transfer pricing data is applied to months-old customer data, is it relevant? Data must also be managed with an enterprise approach—not necessarily collected and used within all groups or areas, but universally accessible. To contribute to a truly accurate profitability picture, data must not only be current and strategically aligned, it must also be integrated and available.
The more you know about your customers, the better you can serve them. And if you can do that in a way that meets their needs as well as those of your employees and shareholders, you have a winning combination. You can conceivably stay one step ahead of consumer demand—and drive further demand—by providing more capabilities, services, and offers, but until you have a complete and balanced understanding of customer profitability, you can’t intelligently analyze the risk/reward implications.
Taking the Broad View—and Applying It
In the retail banking landscape, where understanding customer profitability is critical, banks are, in some ways, stymied by seemingly conflicting drivers. On one hand, the bank is under shareholder pressure to deliver profits. On the other, as banking channels and public interfaces continue to expand, banks feel the responsibility to provide services and interact with the public across the networks they prefer—and doing that effectively and comprehensively introduces additional cost to the business.
These business drivers may seem to be opposed, but they are not (though decisions are often made as if they were). Ultimately, selling retail products and services hinges on banks understanding who their clients are and what they need, both as groups and as individuals. Despite the potential for information exchange enabled by real-time interactive channels, banks do not currently use these to effectively measure customer behaviour and leverage it in product development.
Banks are constantly working on data quality and integration issues—standards, accessibility, consistency, currency, accuracy—and with much success. By systematically applying these improvements across internal functions and to the full continuum of bank-customer interaction, the opportunity exists to improve profitability while meeting evolving customer needs and expectations. However, a wall remains between IT capabilities and the needs and expectations of the various business lines. Until these functions are strategically aligned, even the best data cannot be put to the most effective use. Interestingly, one of the enablers for enhancing data integration and integraity is the common move to a shared services model across many business functions within the enterprise. Currently, there is as much variation in maturity, readiness and adoption of these models as there are models themselves. But the premise is to not only share infrastructures, resources and processes but also to gain in the consolidation of enterprise information – i.e. customer information. Aside from the tangential benefits shared services operating models can bring to understanding customer profitability, many banks are taking a “virtual” approach. Through the utilization of information aggregation systems – banks are “integrating on the fly” – in part to respond to the increasing requirements surrounding the handling of customer information and also to respond to the large internal demand for data.
How It All Plays Out
Taking all challenges into consideration; competitive pressures, organizational complexities, stakeholder demands and budgetary constraints; it may seem daunting to move forward to harness the potential of understanding customer profitability. Business cases aside, the promise of developing a comprehensive, enterprise-wide customer profitability measurement and management program lies beyond simply understanding the final measures. The process itself necessitates breaking down organizational silos (to information at least) and developing a comprehensive view of products, organizational structures and their relative costs and contributions to overall bank performance and even correlations to individual, regional and group performance. While customer profitability measurement provides a lens to the end-result of the bank’s efforts, the individual measures, once clearly understood can provide a new and results-oriented view to the levers of organizational performance.