Is it cleaning fluid?
Is it your espresso maker?
Maybe your paper cutter?
No, no and no. Well, unless you have a REALLY big paper cutter.
No, friend, the most dangerous thing in your office doesn't have sharp edges, heating elements or questionable chemicals. The most dangerous thing in your office has something far more treacherous: your marketing data.
Like nuclear power, your marketing database has the potential to do something very beneficial at a low cost. However, like nuclear power, your marketing database can, if used improperly...well, you know.
Lenny's the one on the ladder, right?
More to the point, marketing data have an inherent liability: good data look pretty much like bad data. Think of it this way: imagine a warehouse. Not a data warehouse. Certainly not Men's Wearhouse. Just a plain old warehouse. Even if you knew nothing about warehouse operation, you've probably been to Costco and you'd sure know whether a warehouse had anything in it. With a little bit of experience, you'd probably even know whether the warehouse worked well, whether the manager had organized the inventory efficiently, whether the forklift drivers ran over people or what have you.
How do you know when your data work well? It's not like your database has a big red light that goes off when your data are crap.
To make matters worse, most marketers only find out they have lousy data the hard way: by using them to power campaigns. If your results significantly trail past performance or industry benchmarks, you've very possibly used bad data.
Fortunately, marketers can get a good idea of how well their data work without making an embarrassing misstep. It requires a little elbow grease, but really gives the marketer a clear indication of quality.
Step 1: Profile your data
Take a look at the major fields in your database by running banners of key variables such as:
- Demographics such as age, gender and family composition if you market B2C
- Firmographics such as company size and industry if you market B2B
- Location (city, state or country depending on the scope of your business)
- Preferences elected via your preference center
- Any fields you may have that matter to your industry
Preferably, you should have nice clean tables and graphs showing how each field breaks down across all or most of your customers and prospects.
Step 2: Look for WTFs
Do any numbers look funny to you? Do you have a preponderance of customers or prospects in an unexpected place? Perhaps your brand appeals to men and women equally, but men outnumber women in your database 6:1. Perhaps a staggering number of your customers live in Delaware when your business focuses on the Midwest. I've seen crazier things in perfectly nice databases, like the time I found that a hotel chain had 20,000 members of its loyalty program listed as living on an island of 40,000 in the Arctic Ocean.
Step 3: Keep WTFs from becoming WMDs
By identifying questionable data, you and your organization can either work around them or correct them. Working around bad data might mean simply not using that field to power marketing communications. For instance, in the hypothetical example of the Midwest company with suspicious Delaware addresses, don't use a dynamic field to insert maps of your lone Wilmington location. Correcting data involves more work--either appending data or contacting customers to ask for corrections--but allow for more positive use of that field. In either case, a company can avoid making a costly mistake with bad data.
Keeping a close eye on your data makes them more useful and less dangerous. Now then, let's talk about why you need such a big paper cutter.
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