PDF File: 

CRM6_wp_tanner.pdf

Baylor University and BPT Partners, LLC

Not too long ago, a washing machine manufacturer in India noticed that there were buyers ordering eight, 10 or even 20 washing machines. Thinking these buyers were laundromats, a representative was sent to one particularly large customer to offer coin-operating and heavier-duty machines. Imagine the representative's surprise when it was learned these customers were using the machines to make cheese!

Data and Data Traps

Companies need to answer the following questions: How well do you know your best customers? For that matter, how well do you know any of your customers? Most companies, like the washing machine manufacturer, divide their customers into groups based on some measure of value. Depending on the maturity level of the CRM program and the experience of those leading it, that value might be gross sales, an index based on profitability estimates, or some other metric. But like the washing machine manufacturer, most companies also fall into the CRM data trap.

When a company falls prey to a data trap, it makes decisions that seem appropriate for the data that they can get. Transactional data such as the size of an order creates a data trap because today’s enterprise data warehousing technology makes transactional data easier to access and analyze. Transactional data also adds a layer of information that was previously unavailable, giving CRM professionals more to work with than they ever thought possible. The trap, though, is that so much data creates an illusion of knowing, rather than actual wisdom.

The fault lies in an assumption that all customers are alike if they purchase the same amount. Like the washing machine company assuming that all multiple machine purchases were intended for laundromats, many companies assume that the drivers for heavy use or high-volume purchasing are the same across all high-volume purchasers.

Traditional Data Trap

Transactional data is not the only data that creates a data trap. For example, what type of householder would cook all meals in microwaves, owning top-of-the-line units with browning elements? Or have two dishwashers, one for dirty and one for clean dishes?

Most people think such a person is likely to be a young professional or a working mother, someone without time. My 80-year-old-plus grandmother actually had a kitchen like that – not to save time, but because it was easier on her to not have to unload a dishwasher or slave over a hot stove. But most marketers would assume incorrectly that, because of her age, she was a technophobe, rather than an innovator (for her age). Demographic data can also create a data trap.

CRM professionals fall into a transactional data trap; traditional marketers fall into a demographic data trap. Traditional marketers tend to treat all members of a demographic group as being the same, while many CRM professionals treat all members of a transactional decile the same. Victims of either trap are not practicing relationship marketing. What they are doing is using the knowledge they have available to create an illusion of knowing, an illusion that makes them feel like they know their market when, in fact, the actions they take are based on incomplete pictures.

Removing the Illusion of Knowing

Is transactional data not necessary? Does all demographic data lead to the illusion of knowing? Of course not – both transactional and demographic data are needed. Transactional data can help you identify event-based opportunities for dialogue or sales, aid in determining the value of a particular customer or provide insight for other activities. With demographic data, offers can be couched in the right language, and other decisions are supported. But these two forms of data are only a part of the total picture.

Overstock.com’s CEO, Patrick Byrne, believes in the value of transactional data because it is behavioral data. He says that you can use that behavioral history to transform future behavior, and he’s right. For example, if some buyers do not make a purchase within a certain window, they are lost forever. For Overstock.com, that window is 45 days after their first purchase. Byrne suggests you make a discounted offer to them on the 44th day. The trouble is that not everyone will reply, nor are you necessarily communicating the right message. One result could be that you are training discount-oriented buyers to wait, or it could be that you only get price-sensitive buyers to return. For example, Foley’s has a “Red Apple” sale so often that few regular customers ever buy anything at full price. They know that twice a month, many products are drastically reduced, so they wait for the markdowns. Maybe that’s why many companies are finding that their largest (they think “best”) customers are not as profitable as they thought. They’ve trained their best customers how to work the system to get the best deals, buying more at lower prices and yielding less profit. Perhaps it would help Overstock.com more to learn why buyers haven’t come back and create an offer more suited to their needs, rather than simply offer a discount.

An Alternative Approach

Assume Overstock.com had two types of buyers – those who were hard-core discount buyers and those who bought because it was fun. How would this knowledge help them? One way it would help is less reliance on deep discounting.

As it stands now, with a new buyer, Overstock.com does not necessarily know which they were dealing with until the 44th day and a discount was offered. Either the buyer responded to the discount and bought or the buyer ignored the discount. If the buyer does purchase with the discount, perhaps it is a hard-core buyer while the “fun-seeking” buyer was lost. One alternative would be an offer of some fun items or a promotion that appeals to fun-seekers on the 34th day. Ten days later, all nonresponders get the discount offer. Now Overstock.com would know who the fun-seekers are and who the hard-core discount buyers are (see Figure 1). Future offers could then be tailored to fit the drivers of their behavior. Further, if a large group did not reply to either offer, then perhaps a third group of buyers is appearing. Overstock.com needs to do something to identify what motivates those buyers – and, therefore, needs more data.

What Data?

To determine what drives or motivates a particular segment, two key areas of data are also needed in addition to transactional and demographic data. These additional types of data are motivational data and lifestyle data. These two types of data are intertwined at the gathering, the analysis and the application stages. In some situations, motivational or lifestyle data may fall into the “nice to know” category, but consider my grandmother once more. Why hasn’t anyone sold a side-by-side dual dishwasher that accomplishes the same thing? Would GE or another company create a market if they understood the people living lifestyles that would benefit from such a product?

Basic CRM promises, such as making an offer individualized to a customer, cannot be fulfilled without motivational and lifestyle data. Even some of the most basic CRM foundations, such as determining your customer’s value, are suspect with only transactional or demographic data.

For example, assume you own a fashion retailing firm targeting young women. Reaching them through catalogs, stores, emails and websites, you have that multichannel approach down. One question haunts you, though: What is the “life” of your customer? If she is 20 years old, is her customer life another three years, five years or eight years? And can she then be moved into another customer category reached through another division of your company?

Rather than lifetime customer value, perhaps it is better to think about defined customer value. Defined customer value is the value of a customer for a defined product category for a defined period of time. And these definitions also require motivational and lifestyle data.

Determining Defined Customer Value

Earlier, I said that a data trap of any kind is any situation in which you have a lot of data, and it is the volume of data that gives you the illusion of knowing. Some have argued, for example, that the sum of a customer’s total transactions over time, multiplied by some expectation of life span, provides a good picture of that person’s lifetime value. Assuming no major changes in the market or innovations that change the relative value, and further assuming the buyer only buys from one company, that could be an acceptable level of knowledge. But it ignores share of wallet, or your share of purchases that the buyer makes out of the budget for that type of purchase. Share of wallet seems easy to calculate, but the key is how big that wallet is.

To really have an idea of a customer’s potential value, you have to know what the total purchases are in that category of the buyer’s budget. Soft drinks, water, sports energy drinks and beer are all beverages, but the buyer may not consider those equally as substitutes. That means understanding when the buyer considers your product and against which other products is important in order to calculate your share of wallet. How the customer calculates wallet is more important – you may think you compete against energy drinks, for example, but if the customer doesn’t consider your beverage when quenching a need for energy, then energy drinks aren’t your direct competition.

That buyer who is seeking to quench a need for energy is motivated by that need. Motivational data, then, is knowledge that identifies what drives a buyer to make a purchase. To understand wallet size, you have to understand how the buyer sees a purchase, and the buyer sees a purchase based on motivation.

Motivational knowledge is important for both B2C and B2B. In the research I’ve conducted over the past two decades on how organizations make buying decisions, I’ve learned that buyers have personal needs as well as organizational needs, and sometimes these personal needs are dominant. To an organizational buyer, the situation may be about showing off decision-making skills or meeting a profit target by cutting expenses, not about choosing benefits from two wonderful products. To be sure, budgets are on paper and more formal. At the same time, there is discretion within those budgets and it helps to understand the motivation underlying customers’ choices.

One challenge is determining size of wallet; the other is determining life. Back to the young women who shop at the fashion retailer. There is one event that changes forever how they shop – college graduation. Identify that, and you’ve identified a major change in their shopping habits. Yes, they still buy jeans, but not as many, and not always at the same price since they are now on their own. Nor is the rest of their fashion shopping the same. These shopping changes are driven by their lifestyle changes.

Where’s the Data?

Part of the allure of transactional data is that it is already there in the enterprise. Motivational and lifestyle data are not; you have to go get it. And that means research.

There are two ways to get data: ask or observe. For example, motivational data can be gathered using a sample of several hundred customers. Using Overstock.com again as an example, you can start with focus groups, asking 10 buyers at a time, “What is the biggest benefit you get when you shop at Overstock.com?” If some say, “It’s fun,” then you know that there is the possibility that there is a funseeking group of buyers. Follow this up with a survey of several hundred more customers. Ask additional questions so that you can understand what they mean by “fun.” Simply by asking, you’ve identified a new group of buyers.

How valuable are these buyers? Hard to say at this point, but that is something you can easily determine with observational data. As suggested earlier, on the 34th day after someone has made a new purchase, and using the additional information about what constitutes a “fun” shopping experience on the website, you create and make an offer that should appeal to the fun shopper. You don’t have to ask that shopper if he or she is fun-driven – the shopper will tell you by responding either with a purchase or a “delete message.” Now you have gathered observational data, and done so at a reasonable cost. In fact, you’ve gathered data and probably made a profit at the same time.

As for determining the relative value of the fun shopper versus the hard-core discounter, time will tell you which is more valuable. As you build observational data on these two groups, you’ll be able to determine the defined value of each as it stands – given how you currently market.

You have an impact on that value. When discounts are offered willynilly, the value of the customer base is lower. When offers are appropriately targeted, value curves shift, meaning that some customers increase in value because they respond, while others may decline in relative value because they respond to lower-value offers.

Further, acquisition strategies may then change. As relative value of each group becomes known, motivational profiles should be continuously tested. In other words, the motivational (and lifestyle) profile of the group is continuously fleshed out by testing offers and responses, which can then be used to more effectively acquire similar customers.