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CRM Performance Management

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Why finance must create CRM objectives and metrics that will translate back into its traditional framework.

In two previous articles I outlined how to create analytical methods that support customer-focused profitability and forecasting & budgeting analysis (“The Art of Customer Profitability Analysis” and “The Financial Side of CRM“). I explained that finance cannot continue to conduct business as usual in a CRM environment, because its conventional analytical toolkits and systems lack customer dimensions.

This article, the last in the series, focuses on the third area where finance has to evolve: creating CRM objectives and metrics that can be translated back into the traditional framework of income, balance, and cash flow statements for financial planning and reporting.

After all, CRM success ultimately depends on irrefutably proving that realizing CRM objectives leads to improved financial performance and is the result of specific CRM activities. Becoming too preoccupied with customer retention, acquisition, and satisfaction goals for their own sake is perilous, because their initial appeal inevitably wears off, and rightfully so, when tangible payoffs cannot be traced to them.

Reorienting to a Customer Perspective
At the core of every CRM initiative are objectives containing a customer-value proposition that describe clearly the targeted customer segments and the corresponding product/service strategies. The first provides the underpinnings for differentiated customer treatments that are the basis for CRM efforts; the second the process to measure.

However, as uncomplicated as this sounds, supporting these objectives is not easy for the finance department. Its product-centric analytical methods and systems reflect the philosophy that companies are solely the sum of theirs individual parts; a view that fails to take into account the complete chain of activities that creates customer value across the whole enterprise. The chief hurdles finance will encounter are finding and consolidating the data needed to develop a enterprisewide customer perspective; translating non-financial goals into financial ones; and pulling CRM objectives out of their ambiguity. This article focuses on the latter two, because they create the greatest amount of confusion due to the nebulousness of many CRM objectives.

Demystifying CRM Objectives
First, CRM objectives need to be clearly defined. For example, catalogers view retention on a sliding scale based on customers’ most recent purchases. Recent purchasers are considered “more” retained than those who bought a while ago, because the probability of additional purchases diminishes as the time since the last purchase increases. Conversely, wireless companies view retention as cut and dry, customers either continue their service or they don’t–retention equals not disconnecting the service.

Second, because no clear-cut way of understanding or evaluating the financial repercussion of CRM objectives exists, standard measurements need to be established that hold them accountable. You have to move from clarification to dissection–cumulative goals are pulled apart to reveal their internal workings: a process practically identical to the one used for conventional goals. Take, for example, a publisher that wants to increase the renewal rate of subscriptions from 40 percent to 60 percent over a three-year period. Certainly, management wants to know prior to that time passing the financial impact of achieving this goal under different scenarios. Table 1, which breaks the cumulative 60 percent goals into yearly intervals for two scenarios, gives you an idea of how this can be done.

Year 1 Year 2 Year 3
New Customers Active Checking Ret. Rate Inc. Ret. Rate Active Checking Ret. Rate Inc. Ret. Rate Active Checking Ret. Rate Inc. Ret. Rate
Scenario 1 10,000 9,000 90% 90% 7,700 77% 86% 6,000 60% 78%
Scenario 2 10,000 8,000 80% 80% 6,800 68% 85% 6,000 60% 88%

You can see that both scenarios reach the goal (shaded column). Scenario 1, retains 90 percent of customer after the first year, 77 percent after the second, and 60 percent after the third, while Scenario 2 retains 80 percent, 68 percent, and 60 percent over the same time period. However, from a financial and marketing analysis perspective, reaching the goal is of minor value; far more important is to understand the causes of the disparity in retention patterns and their effect on the bottom line.

Creating Visibility
Understanding the later is a two-step process. It requires moving beyond the apparent decline of retention from one year to the next, to exposing concealed dynamics. A good way to start is by calculating the incremental retention rates: the percentage of retained customers for a given year divided by the percentage from the previous year.

For Scenario 1, divide 90 percent by 100 percent for the first year, 77 percent by 90 percent for the second, and then 60 percent by 77 percent for the third. The numbers you get are 90 percent, 86 percent, and 78 percent. The same calculations for Scenario 2 yield 80 percent, 85 percent, and 88 percent. Input the newly created data, and the original raw retention percentages in a spreadsheet and create the two graphs as shown below.

As you can see, the graphs portrait two very different flows. The raw retention graph shows a gradual decline in percentages towards the same point for both scenarios, while the incremental graph shows that them moving during the second and third year in opposite directions and then ending up at very different end-points. From a marketing perspective these dynamics reveal that customers in Scenario 1 remain in their first year in the service at a greater rate than those from Scenario 2, but that this behavior reverses itself in the following two years as the incremental retention from Scenario 2 increases. What this data suggests is that Scenario 2 might have greater retention than Scenario 1 in the long run, despite its poor performance in the first year.

The next step is translating the retention process into cash flows. Table 2, shows cash inflows assuming annual revenues of $200 per customer.

Year 1 Year 2 Year 3
Scenario 1
Total = $4.54m
9,000 * $200 = $1.8m 7,700 * $200 = $1.54m 6,000 * $200 = $1.2m
Scenario 2
Total = $4.16m
8,000 * $200 = $1.6m 6,800 * $200 = $1.36m 6,000 * $200 = $1.2m

You can see that Scenario 1 creates $380,000 more than Scenario 2. However, 100 percent of this difference is accounted for during the first two years; in year three cash inflows are equal and looking beyond that time it appears the Scenario 2 will generate more revenue. Results that confirm the impression from the incremental rate analysis that in the long-run Scenario 2 might be the stronger one.

Next you need to assign the costs that correspond to the revenue stream. Go to the first article in this series, ” The Art of Customer Profitability Analysis,” for ideas on how to do that. Keep in mind that when doing so in the context of developing an attrition model, as we are doing here, you need to figure out how to deal with potential unamortized amounts that are created by customers leaving the program at a net loss. Some companies write them off, because they belief that customer profitability should be evaluated on the individual level; others spread them over the remaining customer base, because they consider the loss part of the cost of acquiring and servicing those that remain active.

Meeting the Challenge
Frustration with traditional financial systems and objectives quickly emerges in a CRM environment, as they are an outgrowth of a time when a company’s main objective was optimizing manufacturing processes. The irritation is as much with the philosophy that separates cost and revenue at the customer level, as with the inflexibility and isolation of the systems–attributes that successfully block, or at the very least make it very burdensome to use them for customer relationship management. The latter requires an unimpeded flow of information throughout the organization.

But the challenge is not limited to the infrastructure level. After all, its design is a mere reflection of the philosophy that companies are nothing more than the sum of their isolated operations. A viewpoint that generally permeates every aspect of a company, from who is hired, how accountability is doled out, types of analysis done, and certainly the nature of objectives.

However, changing, or evolving to a customer perspective doesn’t mean that everything has to be reinvented or existing knowledge become invalid. Evolution in this context means gradual change to a broader perspective, it is inclusive not exclusive. You don’t relinquish existing know-how, but apply it in a different way. For instance, the ROI ratio, despite its shortcoming, is popular, because it communicates concisely the critical relationship between income and invested capital. You can only compare business results if you know what it took to generate them, a principle that holds true in a CRM environment.

Evolving finance from a strictly product-focused perspective to a function that is flexible enough to include a customer perspective requires also the willingness to question deeply imbedded views of control and accountability. Challenges you will encounter are not going to be limited to hardware, software, or data issues, as CRM is about the empowerment of employees so that customers are better served. The types of goals you choose, and the methods to evaluate them, can have a profound influence on the behavior of individuals, and therefore the organization–for better or worse. So as is the case with the development of customer-based profitability and forecasting models, be pragmatic when creating CRM goals: clarity and simplification reign.

Tom Richebacher is an information specialist with the EDS Business Intelligence team. His area of expertise is the creation of statistical and financial models based on database services that are used for customer relationship management purposes. He also develops the infrastructure and reporting systems needed for financial, marketing, and operational analysis and information delivery. Contact him at thomas.richebacher@eds.com.

CRM Performance Management
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About Tom Richebacher
Tom Richebacher is an information specialist with the EDS Business Intelligence team. His area of expertise is the creation of statistical and financial models based on database services that are used for customer relationship management purposes. He also develops the infrastructure and reporting systems needed for financial, marketing, and operational analysis and information delivery. Contact him at thomas.richebacher@eds.com. WebProNews Writer


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