Customer Value: Setting the Gold StandardCustomer Value: Setting the Gold Standard

It's no secret that information gleaned from customer interactions is incredibly valuable. In fact, this information is virtually the only privately held asset that stems from an organization's demand chain efforts. In the context of the classic "five P's" of marketing (product, promotion, placement, pricing, and people), the Web site — for many organizations, a major focus of customer interaction — alone offers ample information for customers, prospects, and competitors. Product positioning is always featured there, and pricing is often derivable. Distribution strategies (placement) can be readily understood, and promotional offers are often presented with animated click-through enticements. The Web site even exposes the fifth 'P' — people — when it highlights the management team (with photos!), new "name" customers, and downloadable customer case studies, usually with a spokesperson's name and title (wisely, specifics of customer interactions are rarely revealed).

On the flip side of these sales-driven revelations, however, it turns out that a surprising number of organizations continue to overlook the customer interactions themselves as a significant source of market and performance intelligence. Measuring relative customer value and establishing priorities on the basis of profitability are difficult objectives: yet such metrics are crucial in the quest for performance improvement. Information drawn from customer interactions, particularly through the Web site, is essential to these metrics. Plus, by making a commitment to customer intelligence at this level, organizations will naturally begin to shift focus to the customer and away from a product-centric, transaction-driven mindset.

A customer-focused strategy becomes the foundation upon which an organization can build its capacity to understand how customer value relates to its success; how it can optimize activities with respect to customer interactions; and how it can align cross-functional group performance in order to leverage and influence more profitable customer interactions. A customer profitability framework provides the organization with a structure and discipline for managing its resources, allowing it to focus strategic, tactical, and operational efficiencies toward enhancing the value of its customers.

How does an organization employ customer data integration and other technical means to define "the customer" across multiple functions? How does it create customer value and profitability metrics based on information that's coming from multiple sources? These are daunting tasks, but they are critical to customer-facing performance management. Meeting such objectives will bring your organization closer to effective management of cross-channel customer experiences. It will attain the ability to assign accountability and responsibility for customers to employees and organizational functions, and ultimately, to understand how to align the organization to achieve higher customer profitability.

A central premise in this context is that customer value is heterogeneous. Simply put, not all customers are created equal. Just as a suite of product offerings represents an array of profit profiles — or a hedged investment portfolio includes a heterogeneous mix of vehicles with variable earnings profiles — so too do customer lists comprise a blend of more and less profitable engagements. Enterprise goals and objectives should reflect comprehension of this truism.

The ability to improve performance from a customer-centric perspective requires broad adoption of a customer-driven culture and customer-aware integration management and information management strategies. This change represents a more significant cultural leap than most organizations can manage in a single bound. I'll explore some ways organizations can use technology as an enabler for making progress toward a customer profitability business focus within the demand chain.

A Repeatable Methodology

Performance management is a business strategy and methodical process of managing execution within an organization toward a common set of goals and objectives. The performance management process enables individuals to understand, optimize, and align business and achieve optimal performance through intelligent action (see Figure 1). Companies achieve this goal by aligning business and IT to leverage technology, providing timely and appropriate information for strategic, tactical, and operational purposes.

FIGURE 1 The performance process and cycle. Source: Ventana Research

Performance mandates and processes aren't new. Interactions among people, reports, and physical meetings are still the standard management methods. These methods create hurdles to responding to opportunities in a timely manner, not to mention problems in managing the overwhelming amount of information existing inside and outside of the organization. To overcome these challenges, many organizations — no doubt including your own — are working to capture and automate steps in managing operations, thereby leveraging technology to improve information relevancy and timeliness.

Ultimately, performance improvement isn't feasible without information and the supporting technology to deliver it. Thus, businesses must prioritize existing and incremental IT investments to leverage maximum return in the form of better performance and higher customer profitability. There's no right or wrong way; what's critical, however, is that the strategy chosen provides an open, repeatable framework that puts the customer in the center of efforts to improve business performance.

Figure 2 shows Ventana Research's "PerformanceNetwork" framework. In this view, organizations gain leverage by linking operational metrics to information requirements across essential levels and focus areas — in other words, using metrics to create a performance network. The framework can be a foundation for individuals at any level to apply the "PerformanceCycle" methodology displayed in Figure 1.

FIGURE 2 The interlocking performance network. Source: Ventana Research

Measuring Customer Profitability

How do you begin to build a demand chain performance improvement strategy upon a customer profitability foundation? The first step is to get your arms around which business metrics matter most. Frequently, the data exists, although not in the form or format needed. Most organizations encounter three common obstacles: difficulty in defining the "customer"; problems in integrating customer-level data sources; and the tough challenge of establishing the component metrics needed to derive a useful, accepted customer profitability metric. Let's take a closer look at these three obstacles.

Defining the customer. Simply defining the customer dimension confounds many organizations. Cross-functional conflicts within the organization are the biggest source of difficulty. Sales, marketing, distribution, service, and other functions have understandably different perspectives on customers. Applications that serve each of these groups typically have their own information silos, within which the definition of a customer is self-evident. The definition only has to reflect the requirements of a single business function and for a given application.

In deference to project deadlines, it's easy to rationalize this status quo. However, the resulting inconsistency not only hinders cross-application integration, but problems also seep into the workings of each functional business area itself.

Compromise is unavoidable. Most organizations should consider convening a decision-making body that can look at cross-functional disputes about customer definitions and then review and negotiate among a limited set of options. This body needn't be a typical task force created to assess a situation from scratch. Instead, it should designate a small group of business and IT representatives already immersed in the business functions and give the group a charter to conduct a requirements gap analysis. The deliverable should be a small number (three is a good choice) of optional short-term paths to resolution, which are then brought before the larger decision-making body.

The focus should be on what's doable; yet, the organization must also develop a vision of an ideal solution and consider establishing a parallel project to achieve this vision. Second, I would recommend starting from a sound, defensible set of business logic and applying it consistently as the guiding reason for how customers are defined. Finally, don't expect that any of the functional parties will come away totally happy. That's the nature of compromise.

Integrating customer data. This problem has been known to occupy entire careers. Issues abound concerning logical and physical integration across applications, and much has been written about how to address them. The key point here is that you shouldn't hold up what's doable while you pursue the elusive ideal solution. If nowhere else, customer data can always be found in sales and order processing systems (data under a "bill-to" designation, for example). Start there if other options don't present themselves. You can then integrate information from other systems using this resource as a starting point.

Determining lifetime value and profitability metrics. With the customer dimension defined as the reference point, relevant metrics on that dimension become the focus. Let's consider two commonly used metrics: customer lifetime value and customer profitability. Lifetime value as a concept comes from marketing, especially direct marketing circles. In simple terms, the metric is the discounted present value of expected revenue streams for each customer. It's derived from customer spending history (in some instances a derivative of the recency, frequency, and monetary [RFM] values of purchase behavior), period-to-period customer retention rates, and the organization's internal rate of return.

While customer lifetime value is a very important derived measure that ultimately has applications throughout the enterprise, it's little used outside the marketing department. The metric's derivation calls for a disciplined approach to revenue allocation at the customer level and a recognition that timing is important to the value of both money and customers. The metric and the necessary steps to produce it tell us much about the allocation and analytic requirements of enabling technology.

Customer lifetime value provides a strong basis for calculating customer profitability. It's also a very useful metric in its own right, or as temporary surrogate for a true customer profitability measure. Defining and maintaining this metric is an important incremental step that provides footing for stepping off into the abyss that is the cost side of the equation.

I have deliberately defined lifetime value to address only revenue at the customer level, which is frankly the easier part of the task. Assigning costs in order to bring the lifetime value equation into the realm of customer profitability is the hurdle that many organizations balk at attempting. There are a number of heterogeneous cost components; integration and dynamic maintenance of the pieces and parts will test an organization's cultural and technical will.

You can initially assess cost on the basis of a customer's product mix. Most organizations are armed with a solid understanding of product profitability. At the customer level, unit cost and margin factors can be applied in a reasonably straightforward manner. Among the more difficult challenges are allocations of cost of sales and general sales and administration (GS&A) expenses. Where appropriate, these tasks will often translate into a standard costing or activity-based costing exercise. Process management, data capture, allocation methodologies, and occasionally probabilistic modeling tools will each play a role in reaching resolution.

It's tempting to tackle the cost issue as a one-off effort, making use of spreadsheets, for instance. Resist this temptation. Your objective must be metrics; you want to enable their dynamic use within the context of other relevant customer information. Robust, ongoing support by both business and IT is essential to the success of moving beyond one-off efforts. Remember to focus on what's doable in the short term. As more information becomes integrated into the derivation process, the metric will change. This is acceptable so long as it is consistently applied in the demand chain and the nature of the metric is understood by all.

The process of incrementally building a robust profitability metric has at least one unfortunate consequence. Profit is a precise concept, with little allowance for probabilistic estimation, which is sometimes applied liberally. Thus, customer profitability is best created and used in the context of demand chain (marketing, sales, and service) analysis and planning. Its status as a financial performance metric will come with time, after value is established and a higher level of rigor and precision is achieved. In the meantime, your organization can derive value from the creation and use of component and interim metrics.

The Strategic Dimension

The process evaluation and metric definition process helps provide a PerformanceCycle foundation of understanding (refer back to Figure 1). The realization of performance improvement ultimately comes only with leveraging that understanding to optimize processes and technology to bring the organization into greater alignment.

Getting that alignment requires the organization to turn its attention to cause and effect. What drives customer value? What are the characteristics of profitable, marginal, and unprofitable customers? What can the organization learn from customer attributes and behaviors? How does it explain variations in internal organizational attitudes and behaviors? Which organizational behaviors can be identified that have operational influence on outcomes with customers? These are all standard examples of customer segment analyses. However, they take on new meaning when applied to customer segments defined on profitability. When your organization can develop optimal strategies based on a clear understanding of profitability by customer as well as by product group and channel or vertical target market, the real value of the metric derivation begins to surface.

Cross-functional objective-setting by profit segment is one objective that resonates with virtually any senior business management team. By extending the fundamentals of segmented target marketing into the sales and service functions, you can bring cross-functional alignment on the customer profitability dimension. In this scenario, all customer-facing operations are armed with consistent knowledge; supporting systems differentiate customers and modify prescribed behaviors on the basis of profit potential going forward. The organization can establish goals for qualifying prospects on a profitability basis and migrating existing customers into more profitable segments. Each profit segment can receive specialized targeted treatments that reflect not only marketing messaging and promotional offers, but also specialized sales support materials and value propositions as well as customer service procedures and practices.

Commitment and Execution

A customer value-based strategy only works as part of a deeper commitment to the customer as focal point of the business. Transitioning an organization from a traditional product-centric mindset is a nontrivial change management challenge. Very often it requires some positive manifestation of change reflected in the organizational structure itself. For instance, why not establish business development teams defined on customer profitability segments, just as many organizations have product development and marketing teams?

The execution of new strategies in any organization requires that people, processes, and technology be brought into alignment with the new objectives. Building strategies on customer profitability is no different. IT enablement is critical to making it happen.

Jack Hafeli is VP and research director of Ventana Research's Customer Intelligence and Demand Chain Performance Management practice. He has more than 25 years of experience in driving decision support, OLAP, and BI into customer-centric solutions for both software vendors and service organizations.