CRM Measurement Frameworks : Brand-building & Customer equity building

As discussed earlier, how a company measures its CRM activities depends on who is doing the measuring and what activities are being measured. Below are the common CRM measurement frameworks that both experience and literature review suggests:

  1. Brand-building
  2. Customer equity building
    1. Customer behavioral modeling
    2. Customer value management
  3. Customer-facing operations
    1. Marketing operations
    2. Sales force operations
    3. Service center operations
    4. Field service operations
    5. Supply chain and logistic operations
    6. Web site operations
  4. Leading indicator measurement
    1. Balanced scorecards
    2. Customer knowledge management

Brand-building

The goal in brand building is to carefully manage a company’s name, brands, slogans and symbols, otherwise known as brand equity. Various models (and criticisms) of brand equity have been published over the years. The main challenge lies in how to quantify this important intangible asset. David Aaker (1991) breaks down brand equity into the following components:

Brand loyalty This is a measure of the attachment a customer has to a brand. How likely is a customer to switch to another brand?
Brand awareness This is the ability of a potential customer to recognize or recall a brand as a member of a product category.
Perceived quality This is the customer’s perception of the overall quality of a product or service with respect to its intended purpose and considering alternatives.
Brand associations This is anything that is linked, in the mind of the customer, to a brand. The association also has a level of strength. An association can be a celebrity or person, a life style, a geographic area, various product attributes, some customer benefit, a particular application or use and any other intangible concept.

Brand loyalty can be measured quantitatively in a number of ways. So can brand awareness through surveys and interviews. Many qualitative techniques are used to generate measures for perceived quality and brand associations.

Companies can look at brand building as if they were managing an asset. Brand equity can be calculated by removing from operating earnings attributed to a brand the cost of capital, taxes and risk and then determining the value of the remaining number as a discounted cash flow extending out five or more years (Schultz, 2001). By treating brand value as an asset, investments in brand building can be measured and more easily compared with other corporate investments, the value of the brand and the performance of the investments can be tracked and the performance of specific brand activities can be monitored. Measuring brand value can get complex. Boston Consulting Group’s brand value creation (BVC) approach looks at dozens of variables concerning different aspects of a brand and various competing brands and determining how significant each variable is to the brand’s value (Bixter, et.al., 1999). This approach uses cross correlation analysis, cluster and factor analysis and linear regression to build the brand value model. The authors state that this approach helps companies understand what consumers value most and how well brands deliver it.

Complexity also lies within each brand equity component Aaker describes. Brand awareness has been discussed in depth over the past 40 years yielding plenty of measures such as brand awareness (unaided and aided), brand recall, purchase intention, brand preference and willingness to pay. In addition, brand equity components have relationships between each other. For example, high brand awareness can positively affect perceived quality (Hoyer & Brown, 1990). Brand equity as a measurement framework can also encompass traditional and easier to determine measures such as market share, sales volume, the number of customer inquiries, customer and customer retention, among others. Many managers eschew the more formal and rigorous brand equity measures in favor of measures that are more easily derived (Macdonald & Sharp, 1996).

Davenport and Beck (2001) suggest a different way to think about company or brand awareness. Their technique, called the AttentionScape, helps managers understand what kind of attention they are getting from customers (or employees, suppliers, etc). Data is collected through survey techniques and plotted along three scales:

  1. Front of mind / back of mind attention
  2. Voluntary / captive attention
  3. Attractive / aversion attention

Competitors can be plotted along these axis and companies can develop strategies to reposition themselves relative to their competitors attention profile.

Customer Equity Building

Recently much has been written about the benefits of looking at customers as the key asset, rather than the brand as the key asset. Companies have historically measured products and brands and focused on eliminating unprofitable products from their portfolio. This approach, while seemingly a correct one, fails to account for the multi-product effect on customers and can actually cause a “profitable product death spiral in which weeding out unprofitable products causes initial customer defections, which causes additional products to become unprofitable, which causes further elimination of unprofitable products and so on (Rust et al., 2001). Rust et al. argue for changing the focus from unprofitable products to unprofitable customers.

With the customer as the primary unit of analysis, the CRM literature suggests two frameworks: understanding how customer equity links to business value and understanding how customer behavior works and is linked to parts of the overall customer equity. The first framework is a management framework for linking various customer-facing activities in a reasoned way to overall customer equity and business success. The second framework is a marketing research framework that seeks to understand how customer behavior is influenced by a company’s customer-facing activities.
Customer value management
Different approaches exist for measuring customer value. Four approaches are considered here: customer equity management, customer value analysis, loyalty monitoring, and customer satisfaction. While customer equity management, as described by Rust et al. in 2001 is perhaps the most encompassing of the approaches, each of these approaches has a history of research and literature behind it.

Customer equity management

Rust et al. identify three main components to customer equity:

Value equity The customer’s objective assessment of the utility of the brand, with quality, convenience and price satisfaction as key components.
Brand equity The customer’s subjective and intangible assessment of the brand beyond its objectively perceived value. Key components include the customer’s awareness of the brand, customer’s attitude towards the brand and how the customer perceives the brand’s social ethics.
Retention equity The customer’s tendency to stick with the brand above and beyond the customer’s objective and subjective assessments of the brand. Key components include loyalty, special recognition, affinity, community and customer knowledge-building programs.

Each of these areas of customer equity require measurement and the authors identify some preliminary drivers of each area of equity that can be measured.

Customer Value Analysis (CVA)

Much has been written about customer value analysis (CVA), which was devised by Bradley Gale and utilized by Ray Kordupleski at AT&T. CVA compares price and quality (or value) of a product against competitors. The purpose of this analysis is to determine how changes in price, value or quality can affect market share and as such, this framework provides a linkage between a company’s customer facing activities with overall corporate performance. One form of this analysis compares two competitors in a grid with two axes: relative cost and relative product and service quality.

Since each product or competitor’s scores for price (relative competitive price or RCP) and quality (relative total quality or RTQ) are expressed as relative percentages (for example, between 90% and 110%) of each other. If one company changes price or quality in its product, the position of both company’s products will change on the map. In essence, this map tries to show how customers perceive the product relative to a competitor and how price and quality perceptions will affect their choice in purchasing (Gallagher & Kordupleski, 2000). Most of the analysis work is in determining the components to quality, although depending on the product and category, price can have several components that require analysis as well. When performing this analysis, perceived price (or price satisfaction) and perceived quality are the key measures versus actual price and quality. Surveys are a primary means of capturing CVA data. Frequencies and sampling can vary depending on how dynamic the customer base and competitive environment are and how frequently internal processes within the company change.

CVA fits inside of a comprehensive framework call Customer Value Management (CVM). CVA is the information component of customer value management (APQC, 2001). CVM has a strategic component that helps companies answer 4 basic questions:

  1. Where are we now?
  2. Where do we want to go?
  3. How do we want to get there?
  4. Are we there?

CVM also has a continuous improvement component or an operational component that helps companies understand the root cause of delivery failures, improve the value delivery systems, enhance team development across all improvement initiatives and establish customer recovery or intervention programs to keep and enhance profitable customers and shed unprofitable ones. The APQC identifies 4 basic steps for establishing and monitoring a CVM measurement system:

  1. Identify strategic priorities in the context of customers and products.
  2. Conduct qualitative research to get a comprehensive understanding of the ways customers think about value
  3. Conduct surveys that will provide data for analysis so that the company can determine what from the customer’s perspective are the 3-4 key benefits of the 10 or 12 benefits for each product. These surveys need to be specific to customer segments.
  4. Monitor the value proposition with a limited subset of questions.

CVM proponents feel the method addresses limitations within the customer satisfaction survey approach. According to the APQC, customer satisfaction scores lack linkage to key internal performance metrics and may be unrepresentative of how customers really evaluate product and service purchase decisions. The customer satisfaction framework is older and widely adopted in North America while the customer value framework is newer and being adopted by leading edge companies (Gale, 2002). Gale positions CVM as the latest evolutionary version of voice-of-the-customer initiatives with conformance quality as the first followed by the customer satisfaction and then the customer loyalty paradigm.

Loyalty monitoring

Frederick F. Reichheld’s writings on loyalty (not just customer loyalty, but employee and shareholder loyalty as well) are widely cited with the CRM world as a framework for measuring the effect of customer-facing activities. This measurement framework helps companies look at the customer base along a longitudinal axis. The central notion is that if a company can cause fewer customer defections, the long-term effects on company performance would be significant. Customer loyalty data, then, serves as a predictor of financial performance. For example a 5% increase in customer retention rate can have between a 30% and 95% impact on customer net present value and a similar impact on corporate profits (Reichheld, 1996).

To perform the analysis discussed in Reichheld (1996), companies need to collect defection data, sales data and gross profit, marketing and expense data in a way that can be attributed to customers. This data needs to be analyzed by customer cohort (grouping customers into periods of acquisition. For example, all customers acquired in 2002 would be in the 2002 cohort and reported on). This type of analysis helps identify and manage loyalty problems pertaining to a specific acquisition period. Customer-facing activities can then be tailored to customers based on their loyalty.

Reichheld offers two key loyalty measurement documents: a customer balance sheet and a customer value flow statement. The balance sheet looks like this:

Customer category Number % of Revenue NPV
Beginning Balance
+ New customers
+ Gainers
– Decliners
– Defectors
Ending Balance

The term new customers refers to customers acquired. The term gainers refers to customers who bought more in this period. Decliners refer to those who bought less and defectors refer to customers who left. The customer value flow statement captures the following information about a company’s customer and some of its key competitors:

Price Quality drivers Retention
Share of wallet Gain Yield
New customer NPV Current customer NPV Defector NPV
Average profit per customer Average revenue per customer

The gain rate is the ratio of new customers to the current customer base. The yield rate is the percentage of customers who actually convert to buyers, or sign up. As do Rust et al. (2001), Reichheld discusses the use of an acquisition/defection matrix that shows how many customers defect from one company’s brand to another.

To collect defection data, understand what are the components of quality and service from a customer’s perspective, and enumerate which measures will represent the company’s value proposition’s success (in addition to the measures discussed here), requires ongoing customer surveying and other qualitative research techniques with their concomitant data collection approaches.

Customer satisfaction

For the past several decades, businesses have been determining customer satisfaction to help improve their customer-facing activities and predict and improve financial performance. Customer satisfaction, then, is an antecedent to some form of loyalty behavior. Customer satisfaction has been defined as a “satisfactory post-purchase experience with a product or service given an existing pre-purchase expectation, (Vavra, 1997).

Vavra (1997) offers a model for customer satisfaction in which satisfaction is an antecedent to repurchase behavior and has several antecedents as well. The most important antecedent is prior experience that “serves as a ‘memory bank of all the previous experiences with a product or service. Several factors can influence prior experience, such as the customer’s demographic characteristics, their level of personal expertise, the nature of the competition, advertising and PR influences, and the evolution of technology. Along with prior experience, customer desires and expectations, the perceived product or service performance and ease of evaluating that performance are all antecedents to a mental process customers go through to compare what was expected and what was delivered. This “disconfirmation/confirmation/affirmation process, in which expectations are not met, met or exceeded can be visualized as a sigmoidal function (Vavra, 1997). As “perceived performance exceeds expectations, satisfaction increases but at a decreasing rate. As performance falls short of expectations, satisfaction decreases at a faster rate than it does for exceeding expectations (Vavra, 1997).

Following Vavra’s model, satisfaction is an antecedent to repurchase behavior, but the relationship between the two is mediated by several factors including the industry structure and life cycle, switching barriers, channel structure, complaint management and relationship management. Within this model are a host of measures companies need to collect. Before data collection can be done however, the company must design a survey instrument. The challenge is to formulate a customer satisfaction survey that balances internal company-process issues with external customer needs issues. When designing this survey, companies can use a variety of qualitative data collection techniques to determine the product or service characteristics and attributes to survey. Once designed, surveys are distributed through a variety of channels: face-to-face, mail, fax, e-mail, web and phone. Standard data analysis and data mining techniques are then employed to understand the represent the survey data.

The linkage between customer satisfaction and financial performance is often cited as the weak link in the customer satisfaction discipline. Attempts have been made to resolve this by linking customer satisfaction with some notion of product or service quality and customer loyalty and retention. A model for doing that is pictured in Figure 3.

Figure 3. Source: Johnson & Gustafsson (2000).

To implement this financial causal model, Johnson & Gustafsson (2000) argue for a cyclical process that starts with identifying the overall purpose (strategy and planning), moves to building the “lens of the customer (qualitative research), which moves to building the quality-satisfaction-loyalty survey which moves to performing data analysis which then moves to making decisions before starting all over again.

Others have also linked customer value analysis concepts to customer satisfaction to address some of the inherent limitations in the customer satisfaction paradigm (Woodruff & Gardial, 2001). Woodruff & Gardial list the following differences between the paradigms:

  • Customer satisfaction is a reaction to value received. Customer value determination tries to capture the relationship between the product, the user and their goals in a specific use situation. Satisfaction measures the gap between expected and actual product performance. Satisfaction measures and customer value determination complement each other.
  • Satisfaction measures are historical. They measure what has been delivered. Both the customer value paradigm and the customer satisfaction paradigm build out, through qualitative techniques, a model of how customers perceive value. The satisfaction paradigm applies to model to value that has been delivered. The customer value paradigm is not tied to post-delivery measures. Customer value can be measured before, during and after consumption whereas satisfaction is measured after consumption.

The problem with many implementations of satisfaction surveys is that what is being measured are attributes of a product from a company’s perspective rather than how the customer arranges their hierarchy of values in the context of specific use situations. This can cause companies to be measuring correctly but measuring the wrong thing.

Researchers and practitioners within the CRM, marketing and customer satisfaction circles have argued among themselves as to which approach: loyalty, satisfaction, value, quality or some other attribute is what matter most. The CVA crowd looks at CVA and CVM as the successor to the customer satisfaction paradigm. Customer satisfaction practitioners have expanded their model to resemble the CVA/CVM model. In some respects, the debate is pointless, since nearly every paradigm tries to establish a sequence of causal relationships at three levels:

  1. Company behavior towards customers
  2. Customer behavior in total (including factors outside of the company’s direct control)
  3. Financial results derived from changed customer behavior

The debate is about how to arrange the various nodes in the influence diagrams to model, more accurately, the causal linkages. The risk in all measurement paradigms is not so much inaccurately measuring, but in measuring irrelevant things.
Customer behavioral modeling
Embedded within brand-building and customer equity measurement frameworks is some form of a customer behavioral model. These models try to explain one or more customer behaviors by describing the antecedents on that behavior and the level of influence each antecedent has. The reason customer behavioral modeling is discussed separately here is that the market research literature is rich with studies that do not necessarily try to tie customer behavior to financial performance or company responses. Instead, the research simply wants to understand customer behavior better more or less removed from specific company goals, objectives or performance. In addition, researchers are focusing on new concepts to link to customer behavioral loyalty.

An example of this kind of model with its appropriate measurement issues is shown in Figure 4. Here the authors (De Wulf et al., 2001) are probing how different relationship marketing tactics impact customer perceptions of relationship investment by the retail company. Through predominantly qualitative techniques, including surveys, interviews and focus studies, the authors established measures and collected data to understand how each of the relationship marketing tactics did or did not affect purchase behavior.
While this example is very research-oriented, companies can use these kinds of measurement techniques to understand customer loyalty behavior in depth. This detailed level of explanation can be useful for critical customer interactions, especially where the type of product, service or customer experience is unique to the company and no relevant research is applicable.

Figure 4. Source: De Wulf et al. (2001)

These types of measurement frameworks abound in the academic literature and are usually cloaked in veils of secrecy within the few companies that perform this type of research. The vast majority of companies, especially mid-sized and small companies, never go to this level of analysis to understand customer behavior. This measurement framework requires a robust qualitative research capability that is refreshing the data and revising the behavioral model frequently as markets and customer behaviors change.

by Vince Kellen
March, 2002
CIO, DePaul University
Faculty, School of CTI, DePaul University
Chicago, IL. U.S.A.
http://www.depaul.edu