Not What it Seems

Yesterday in my mail I received a “pre-approved guaranteed” $5,000 from a finance company. It wasn’t the misleading offer that got my attention. It was the address on my mail.

Nearly 20 years ago I was married to someone with the suffix Jr. on his name and we had a joint credit card account. When the account was dissolved, the credit card company erroneously appended my name with Jr. The finance offer in yesterday’s mail was addressed to Carol Aubitz Jr. That erroneous suffix on my name is still entrenched in data banks after nearly 20 years. I have moved three times and am no longer married to the “ex Jr.” yet the suffix on his name continues to follow me.

That is the problem with information in data banks. The person isn’t always what he or she seems to be. Marketers use the information over and over, multiplying the error so many times it is impossible to extract it and permanently correct the records. While most of the data collected about us is entered and stored accurately, there are many ways that the data can be misleading about who we are, how we live, what we like, and what we will respond to.

I am a big believer in consumer profiling because I know it works. In 1978 I attended my first seminar on profiling. The seminar was two days with Claritas, one of the leading (in my opinion the best) consumer profiling companies. This was 30 years ago when the collection and storage of information relied more on extrapolation and interpretation than today’s highly intelligent computer capabilities. But it was still eerily accurate.

How accurate? From my 30 years of experience I believe profiled data to be within 95% -98% on target every time. If you are buying a profiled mailing list, however, and 2%-5% of the 30,000 names you are mailing are not the customer you hoped to reach, 600 to 1,500 wrong names seems like a huge error, especially if you get complaints from them.

There are many legitimate reasons why this margin of error happens. Understanding them will help you better understand the value of the names you get when you order a consumer list for targeted direct mail marketing. Here are some of the most common ways errors get associated with consumer data.

Special Interests Lists: When a consumer makes a purchase, the vendor they purchase from is an indication of what interests them. For example, if a person purchases fly fishing gear from Orvis it is safe to determine that person enjoys fly fishing. Therefore, it seems safe to determine that person would also be interested in receiving information from other marketers that relate in some way to fly fishing or interests in common with people who enjoy fly fishing.

The biggest factor contributing to errors in special interests is the “gift purchase.” Here’s how the error gets and stays in the system. Suppose my father is an avid fly fisherman. Because of that, I buy gifts for him at Christmas, his birthday, and Father’s Day that relate to his love of fly fishing. Data collection companies do not know these purchases are gifts. My name gets flagged for a special interest file indicating that I am an avid fly fisherman because I purchase fly fishing items several times a year. In actuality, I have never gone fly fishing in my life and probably never will.

But it doesn’t stop there. Because I buy these items for my father, I am likely to respond to related offers from other companies and buy their products as gifts for him. Now I am declared a “hot” name or multi-buyer because I buy from multiple companies. So far that is good, if you are getting my name to sell me fly fishing related products.

Here’s where it goes really awry. There are common characteristics identified about people who are fly fishermen. First, nearly all of them are men. They are also predominantly white collar, higher earning professionals. Marketers who want to reach men in this demographic will use the special interest list to market services and merchandise that are not related to fly fishing but to the demographic. Before I know it my mailbox is full of offers for a variety of merchandise and services for men, and it’s of no interest to me.

Even worse, many of these marketers will automatically append the title Mr. on all their mail. When this happens I receive mail to Mr. Carol Aubitz. Now my name starts to be put in databases with the wrong gender coding. This gets particularly ridiculous when the mailer uses personalization and variable data fields to address the content to me as a man. There are hundreds of scenarios like this that cause accurate information to be interpreted inaccurately. Once it is in the system it is nearly impossible to get it corrected.

Demographic Compiled Lists: Data compiling uses broader information such as census data and self-reported information to cluster names. Self-reported information is what consumers put on surveys, application forms and other research documents. With self-reported information the data frequently becomes household data instead of data linked to a specific person.

If there is a change in the household it is nearly impossible to eliminate that data from the household record. For example, if a person responding to a survey indicates gardening as a hobby, that household is identified as having gardeners living in it.

If they move to a different home and as part of the move no longer participate in gardening, or if their lifestyle changes and they no longer have time for gardening, they will still most likely be in the consumer data banks as a gardening household. It can take years to get this data changed.

The same thing happens with household data for a variety of niches including travel, philanthropic giving, interests, pastimes – especially those that revolve around children who grow up and move out, and many more. Lifestyle changes are a part of life. They happen much faster than data compilers can capture the changes.

One of the major changes that happened since this time last year is the financial status of households. Spending is down in up to 90% of households in the country. Even those with discretionary income to spend are not spending it. Changes in the job market are affecting all, from white collar jobs to minimum wage earners. Neither profiling nor compiling can keep pace with the changes in attitudes and affluence, real and perceived, that have changed how consumers respond and what they respond to.

For marketers this shouldn’t be interpreted as a reason to stop relying on profiled and compiled data. Most data is accurate. But a better understanding of why a small percentage of it may not fulfill the criteria you use when you order consumer lists is essential so you can project returns and response when you use targeted lists.

Don’t build your response projections from the full file. Expect that perhaps 5% of the targeted marketing will not be on target and deduct that from your total projections. It is now mandatory that the post office NCOA (National Change of Address) file is run for all mailings, so that will cut out the majority of names that are non-mailable.

If you are a mailer that defaults to the second address line for “Or Current Resident” be sure your offer is generic enough that it appeals to mass markets. The likelihood that a new resident has the same lifestyle as the former resident is slim. If your offer is connected to the type of residence instead of the profile of the residents, the “current resident” label should work for you.

There are different pitfalls when doing business-to-business targeted direct mail. I’ll talk to you about those in next week’s muse.

Note: Claritas is now a Nielsen Company. I am a licensed Claritas provider.

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