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Before you consider which metrics to use, your database should have:
- customer records with less than 2-percent duplication. This is critical for segmentation and assigning lifetime value. It's also helpful to be sure that 90 percent of the customers' addresses are accurate for appending third-party household or firmagraphic data.
- a definition of what constitutes a customer, a past customer and a prospect. Companies may define a customer as someone who has purchased in the last 30 days. In B-to-B, a customer may be defined over a span of years.
- a transaction table that has historical transaction records with accurate and consistent dates and dollar value, as in association with the customer table.
- a product table that has the product associated with the transaction, organized by the type of product or service the customer has chosen.
- a communications table with the communication type, date and response associated with the customer.
Tools for Customer Value Analysis
Let's try to understand what types of analytical processes make the most sense for your situation.
1. Simple Regression Analysis: This takes all the customer interactions and transactions and hierarchically lists them. It does not give you a complete picture of your customers or the importance of multiple purchases, highest profit service, product purchases or frequency. A one-time purchase made at the oldest part of your "customer time period" can distort who the best customer could be.
2. Recency, Frequency, Monetary Value (RFM): This analysis is used frequently by catalog merchandisers and some retailers. It provides a good perspective of how recently a customer's shopping activity occurred, the frequency of a customer's shopping and the aggregate of the amount of money the shopper spent.
RFM analysis lets you evaluate and rank your customers in quintiles. For example, quintile one provides the best performing customers. However, RFM does not tell you about the profitability, the potential of a customer or prospect and what offer or communications (if any) he responded to.
3. Lifetime Value or Net Present Value: Lifetime value is defined as the future of a customer's worth over the expected lifetime of interactions. For this, a company needs to know the historical net present value of the customer, modified by the cost of funds since the customer came into the database. The company needs to know the next time period that customer is likely to last before going dormant or lapsed.
But before this can be accomplished, a number of conditions must be met. These include defining periods of activity and inactivity, and the duration of a customer, based on her profile or the segment from which she came. For instance, a customer lifetime in segment one may be seven years, while a lifetime in segment three might be measured in months. The value for each of these segments will yield substantial differences in customer value.
4. Quadrant Analysis: This divides the customer base into four parts and provides views of their transaction behavior and potential (see chart)
. Usually segment one is the best and most profitable segment. Segment three is the opposite in a four-segment quadrant, and reveals the lowest profitable customers. The analysis looks at customer profit, velocity of transactions and customer profiles, which emerge as a result of the customer behavior. This analysis is relatively under-used because many companies don't possess the profit-per-transaction data (purchase minus service and returns) as well as appended household or firmagraphic elements necessary to evaluate each customer.
For example, a dependent variable of age discovered during a profile analysis may be highly predictive and common among all of the population. It may help to segment the customer groups. In many cases, transaction amounts or frequency become the dependent variable.
5. Campaign Management Analysis: This is a valuable analysis used to determine what type of communication, offer or package yielded the highest return on investment (ROI) to a segment. This analysis looks at the cost of contact with the customer by measuring the total cost of the marketing activity divided by the total number of customers or prospects contacted during the campaign. It reveals the cost of response by taking that same campaign cost divided by the number of respondents.
Finally, it shows the cost-per-order/acquisition, by taking the total cost of the campaign divided by the total orders/new customers. Applying ROI, or return on customer (ROC), allows a company to evaluate the effectiveness of a campaign. This helps companies retain customers by building campaigns and implementing interaction strategies across multiple channels and touch points.
6. Real-time Scoring Analytics: This analysis assumes that customers remain the same. But in reality, our tastes change, our lifestyles change and we pass through different life cycles. Businesses change, too, although their process of change is different and slower, and is affected by the economy and technology.
Real-time scoring analytics begins early in the process of a customer's interaction with the company. The customer initiates the communications interaction at the touch-point, behind which there is an automated scoring interaction.
For example, when a customer comes to a Web site, the site asks for a PIN. The underlying database recognizes a new customer from an existing one. It delivers a dynamic set of pages based on the customer's previous transactions and asks some qualifying questions. Depending on the answers, the database calculates a score that dynamically serves new pages (offers or products) and scores the customer's potential value to the company. This can set off a series of communications all the way from a phone call from a company associate to a simple confirmation and thank-you e-mail. The database can have built-in triggers that store the customer information and contact the customer in the future when he is in the window of opportunity.
Why Customer-centric Marketing is Important
The reason for creating customer-centric marketing is to identify the most profitable and least profitable segments of the business, and to gain reward in leveraging your marketing dollars to get the best response for your buck—and to not market to customers who are habitual under-performers with little or no potential for development. Valuing the customer helps companies focus on the business practices that ensure long-term customer relationships and company profits.
Since customer behavior is much more predictable than appended third-party data, customer-centric marketing also allows companies to offer the right product through the right channel at critical buying times.
A Deloitte & Touche Global Corporate study revealed that customer satisfaction is declining while product quality and technology are at a high performance level. This is because companies are not focusing on their customers. They're putting their emphasis on operations and profit before customer satisfaction.
With the heat coming from privacy legislation and rising consumer concerns, knowing how to accurately contact a customer or prospect will be essential for business success.
The power lies with robust measurement and analytics leveraging both historical and predictive intelligence about a customer's behavior. A campaign executed without measurement and customer intelligence will fall short of potential revenue returns and customer expectations.
Bob McKim is CEO of Software Solutions Group (SSG), a Sourcelink company. He can be reached at (310) 208-2024.
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