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I'm excited to have Tim Wilson from Gilligan on Data contribute today's guest post. Tim is one of the smartest guys on data management and data quality in the industry and brings a great perspective on what works in the real world. He also has one of the wittier writing styles out there, that makes his posts fun to read. I enjoyed this one, and I hope you do too.When Steve asked me to write a guest post about marketing automation and data quality, I couldn't resist, as we've been going back and forth on our respective blogs exploring the issue. It really started with Steve's Contact Washing Machine post late last year, which he followed up with in April of this year with a post about the need for that washing machine to be managed in-house, largely due to the diversity of sources of contact data. I added my own thoughts about the teeter-totter of customer data management a month later. That back and forth led to Steve thinking I might have a worthwhile direct contribution to his blog.
So, here it is:
Data management is like a mattress. It's not nearly as interesting as what gets done with it (on it)...but it's still awfully important!The truth is, you can ignore the mattress and still get some interesting things done, but, eventually, as you wake up with a sore back, as you don't sleep well in the first place, and as you get shoved into awkward positions by pits and valleys...the interesting stuff just isn't going to be as interesting and effective.
Let's see how far we can push this analogy before it absolutely collapses under its own metaphorical weight.
Know What's Important about Your Mattress
Imagine the scenario: you're a spastic sleeper, flailing about on the calmest of nights; your significant other is a very light sleeper and wakes up at the slightest of touches. What's important? A mattress with enough room for you to roam about. That may be way more important to you than, say, the firmness of the mattress, which may be very important to someone with a chronically sore back.
It's easy to shoot for the stars with your contact data by trying to ensure that every contact attribute you capture is complete, accurate, and current. The problem is that shooting for a star is overly ambitious -- NASA is only now getting close to pulling that off for the first time. The same goes for your contact data. If you expect to have all of your data 100% clean, you will wind up with all of your data equally dirty, and it will hurt you. Prioritize your contact attributes so that you know what data is most important. The most important data will always be your core communication details: email address, mailing address (if you use direct mail as a communications channel), phone number, etc. After that, it really depends on your long-term marketing strategy -- focus on the data that matters most.
Start with a Good Mattress
Steve's contact washing machine is one example of this: at every point where you are capturing contact data, do what you can to capture it accurately. Be prepared to invest more -- in internal technology development as well as in third-party tools -- to ensure the highest accuracy of your most critical data. For instance, check that the e-mail address the prospect provides is well-formed. If the mailing address is a high priority, then, for U.S. addresses, consider validating the address provided against a CASS-certification tool. Build in other logical checks -- can the user put in that they have 5,000 employees at their company but have annual revenue of less than $1 million of revenue?
Be careful: it can be tempting to build in all sorts of logic to check that you are capturing good information, but that can be risky for two reasons:
- Faulty logic in your checking -- we've all been to a web site at one time or another that tells us we've entered something incorrectly...when we haven't. I've been on the inside of a company that had this happening with one of their most highly-trafficked lead acquisition points. It's not pretty. It's better to get 95% perfect data quality and have 100% of the visitors to your site get to the information they want than to have 99% data quality and 10% of your visitors getting caught in an endless (flawed) validation loop that leads them to give up and leave (with a bad taste in their mouth about your company).
- Losing sight of your priorities -- have you ever been to a web registration form with the "Red asterisks denote required fields" note...and then every field has a red asterisk? This is bad. Yes, you want your data as clean as possible, but you want the data that is most important to really be clean. Prioritization sucks, but you've got to do it.
Flip Your Mattress
"Will everyone in the room who has flipped their mattress in the past six months as per the manufacturer's instructions please stand up? Wow. There's one guy. Usually no one stands up when I ask that question. Oh. He's just taking a call on his cell phone."
Data management cannot stop at the point that you've got your data capture mechanisms set up. This is where the mattress analogy breaks down a bit, as ensuring that you are constantly working on the quality of your data is wayyyy more important than your mattress-flipping schedule.
Here's the contact data-equivalent mental exercise to the mattress-flipping survey above:
- How many people are in your department at work? How many of those people joined the department in the last year? How many people were in the department a year ago and are not any longer? How many people have had a change in job title or responsibilities in the last year? Given your answers to these questions, roughly speaking: what percentage of your department has had key attributes of their contact profiles change in the last year? 10%? 20%? More?
- Now look at your database. What percentage of your contacts have had no updates to their key profile data in the last year?
Do you see where this is heading?
The point: we tend to be wildly optimistic about the quality of our contact data, because we underestimate how rapidly that data decays. We assume that the rest of the business world is more static than our own immediate environment.
This is where marketing automation, and your overall marketing program, really start to show their symbiotic relationship with the management of your contact data. All too often, we live with some cognitive dissonance, in that, when we talk about the quality of our customer data, or when we manually inspect a handful of records, we quickly realize that much of the data is old or incomplete. We then turn around and build automated marketing programs that pretend the data is perfect. We reconcile this by telling ourselves that it's the best data we have, it's better than nothing, and there's nothing we can do about it. This is not true.
While there is no magical, easy way to maintain your customer data quality on an on-going basis, you do have opportunities in many of your marketing activities to fight off the beast of data decay:
- When known users hit a registration form on your web site, prepopulate it with the data you have about them and include a simple note asking that they confirm the accuracy of the information before submitting the form
- Alternatively, or in conjunction with the above, add a persistent element throughout your web site that shows the 3-5 most critical fields about the visitor with a clear "Update my information" link
- In direct mail and direct e-mail campaigns, include the explicit information (including information you have determined based on implicit/behavioral data, when applicable) about the person, with a secondary call to action for them to update that information. (For four years in a prior role I regularly received direct mail from Microsoft targeted to me because I was an "IT executive" who, apparently, had responsibility for IT infrastructure -- if there had been a way for me to tell them I was woefully misflagged in their database, I would have done so.)
- Factor in the "last updated" date for the contacts when developing your promotional lists. You may already be running some form of reengagement program on old leads -- don't assume that the job title or role is remotely accurate for these contacts. If this program includes a, "We haven't heard from you in a while" component, a non-aggressive tactic can be to ask them to update their information and interests so that you will not bother them with information in the future that is not useful to them.
- Don't assume that the humans in your company are thinking of data quality when they have direct interactions. Do some digging into your telemarketing and inside sales processes to ensure that they include steps to check for the currency and accuracy of the key data points when they interact with leads directly.
In short, flipping your contact data mattress is not something you can do with a few simple steps on a bi-annual basis. It really needs to be an on-going process that is embedded in small ways throughout your marketing programs, always keeping in mind that the burden on the contact himself/herself needs to be kept to an absolute minimum.
Sleep Well!
At the end of the day, you want your contact data to be as accurate as possible so you can drive more sales. A better mindset, though, is to recognize that "more sales" is the end, and the means to that end is "provide more value to your leads by better understanding their wants and needs." In other words, contact data management is about being customer-centric first, which will lead to improvements in your lead qualification process, which will improve the handoff of leads to Sales, which will lead to higher revenue...and a good night's sleep!
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