Data Customer Acquisition and Data Mining

Customer Acquisition and Data Mining

Introduction

For most businesses, the primary means of growth involves the acquisition of new customers. This could involve finding customers who previously were not aware of your product, were not candidates for purchasing your product (for example, baby diapers for new parents), or customers who in the past have bought from your competitors.

Some of these customers might have been previous customer, which could be an advantage (more data might be available about them) or a disadvantage (they might have switched as a result of poor service). In any case, data mining can often help segment these prospective customers and increase the response rates that an acquisition marketing campaign can achieve.

The traditional approach to customer acquisition involved a marketing manager developing a combination of mass marketing (magazine advertisements, billboards, etc.) and direct marketing (telemarketing, mail, etc.) campaigns based on their knowledge of the particular customer base that was being targeted. In the case of a marketing campaign trying to influence new parents to purchase a particular brand of diapers, the mass marketing advertisements might be focused in parenting magazines (naturally). The ads could also be placed in more mainstream publications whose readership demographics (age, marital status, gender, etc.) were similar to those of new parents.

In the case of traditional direct marketing, customer acquisition is relatively similar to mass marketing. A marketing manager selects the demographics that they are interested in (which could very well be the same characteristics used for mass market advertising), and then works with a data vendor (sometimes known as a service bureau) to obtain lists of customers who meet those characteristics. The service bureaus have large databases containing millions of prospective customers that can be segmented based on specific demographic criteria (age, gender, interest in particular subjects, etc.). To prepare for the "diapers" direct mail campaign, the marketing manager might request a list of prospects from a service bureau. This list could contain people, aged 18 to 30, who have recently purchased a baby stroller or crib (this information might be collected from people who have returned warranty cards for strollers or cribs). The service bureau will then provide the marketer with a computer file containing the names and addresses for these customers so that the diaper company can contact these customers with their marketing message.

It should be noted that because of the number of possible customer characteristics, the concept of "similar demographics" has traditionally been an art rather than a science. There usually are not hard-and-fast rules about whether two groups of customers share the same characteristics. In the end, much of the segmentation that took place in traditional direct marketing involved hunches on the part of the marketing professional. In the case of 18-to-30 year old purchasers of baby strollers, the hunch might be that people who purchase a stroller in this age group are probably making the purchase before the arrival of their first child (because strollers are saved and used for additional children). They also haven't yet decided which brand of diapers to use. Seasoned veterans of the marketing game know their customers well and are often quite successful in making these kinds of decisions.

How Data Mining and Statistical Modeling Change Things

Although a marketer with a wealth of experience can often choose relevant demographic selection criteria, the process becomes more difficult as the amount of data increases. The complexities of the patterns increase, both with the number of customers being considered and the increasing detail for each customer. The past few years have seen tremendous growth in consumer databases, so the job of segmenting prospective customers is becoming overwhelming.

Data mining can help this process, but it is by no means a solution to all of the problems associated with customer acquisition. The marketer will need to combine the potential customer list that data mining generates with offers that people are interested in. Deciding what is an interesting offer is where the art of marketing comes in.

Defining Some Key Acquisition Concepts

Before the process of customer acquisition begins, it is important to think about the goals of the marketing campaign. In most situations, the goal of an acquisition marketing campaign is to turn a group of potential customers into actual customers of your product or service. This is where things can get a bit fuzzy. There are usually many kinds of customers, and it can often take a significant amount of time before someone becomes a valuable customer. When the results of an acquisition campaign are evaluated, there are often different kinds of responses that need to be considered.

The responses that come in as a result of a marketing campaign are called "response behaviors." The use of the word "behavior" is important because the way in which different people respond to a particular marketing message can vary. How a customer behaves as a result of the campaign needs to take into consideration this variation. A response behavior defines a distinct kind of customer action and categorizes the different possibilities so that they can be further analyzed and reported on.

Binary response behaviors are the simplest kind of response. With a binary response behavior, the customer response is either a yes or no. If someone is sent a catalog, did they buy something from the catalog or not? At the highest level, this is often the kind of response that is talked about. Binary response behaviors do not convey any subtle distinctions between customer actions, and these distinctions are not always necessary for effective marketing campaigns.

Beyond binary response behaviors are categorical response behaviors. As you would expect, a categorical response behavior allows for multiple behaviors to be defined. The rules that define the behaviors are arbitrary and are based on the kind of business you are involved in. Going back to the example of sending out catalogs, one response behavior might be defined to match if the customer purchased women's clothing from the catalog, whereas a different behavior might match when the customer purchased men's clothing. These behaviors can be refined a far as deemed necessary (for example, "purchased men's red polo shirt."

It should be noted that it is possible for different response behaviors to overlap. A behavior might be defined for customers that purchased over $100

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