Data Quality Makes the Best Neighbor

So this week’s #dataqualityblunder is brought to you by the insurance industry and demonstrates that data quality issues can manifest themselves in a variety of ways and have unexpected impacts on the business entity.

Case in point – State Farm. Big company. Tons of agents. Working hard at a new, bold advertising campaign. It’s kind of common knowledge that they have regional agents (you see the billboards throughout the NY Tri-State area) and it’s common to get repeated promotional materials from your regional agent.

But, what happens when agents start competing for the same territory? That appears to be the situation for a recent set of mailings I received. On the same day, I got the same letter from two different agents in neighboring regions.

Same offer. Same address. So, who do I call? And how long will it take for me to get annoyed by getting two sets of the same marketing material? Although it may be obvious, there are a few impacts from this kind of blunder:

  • First of all – wasted dollars. Not sure who foots the bills here – State Farm or the agents themselves, but either way, someone is spending more money than they need to.
  • Brand equity suffers. When one local agent promotes themselves to me, I get a warm fuzzy feeling that he is somehow reaching out to his ‘neighbor’. He lives in this community and will understand my concerns and needs. This is his livelihood and it matters to him. But, when I get the same exact mailing from two agents in different offices, I realize there is a machine behind this initiative. Warm feelings gone and the brand State Farm has worked so hard to develop, loses its luster.
  • Painful inefficiency.  I am just one person that got stuck on two mailing lists. How many more are there? And how much more successful would each agent be if they focused their time, money and energy on a unique territory, instead of overlapping ones.

There are lots of lessons in this one and there are a variety of possible reasons for this kind of blunder.  A quick call to one of the agents and I learned that most of the lists come from the parent organization but some agents do supplement with additional lists but they assured me, this kind of overlap was not expected or planned. That means there is a step (or tool) in the process that is missing. It could require a change in business rules for agent marketing. It’s possible they have the rules in place but requires greater enforcement. It could just be a matter of implementing the right deduplication tools across their multiple data sources. There are plenty of ways to insure against this kind of #dataqualityblunder once the issue is highlighted and data quality becomes a priority.

Data Quality and Gender Bending

We have all heard the story about the man who was sent a mailing for an expectant mother. Obviously this exposed the organization sending it to a good deal of ridicule, but there are plenty of more subtle examples of incorrect targeting based on getting the gender wrong. Today I was amused to get another in a series of emails from addressed to [email protected] The subject was “Eileen, will the ECJ gender ruling affect your insurance premiums?” 🙂 The email went on to explain that from December, insurers in the EU will no longer be able to use a person’s gender to calculate a car insurance quote, “which may be good news for men, but what about women…” They obviously think that my first name is Eileen and therefore I must be female.
Now, I know that my mother had plans to call me Stephanie, but I think that was only because she already had two sons and figured it was going to be third time lucky. Since I actually emerged noisily into the world, I have gotten completely used to Stephen or Steve and never had anyone get it wrong – unlike my last name, Tootill, which has (amongst other variations) been miskeyed as:

• Toothill                    • Tootil
• Tootle                      • Tootal
• Tutil                         • Tooil
• Foothill                    • Toohill
• Toosti                       • Stoolchill

“Stephen” and “Steve” are obviously equivalent, but to suddenly become Eileen is a novel and entertaining experience. In fact, it’s happened more than once so it’s clear that the data here has never been scrubbed to remedy the situation.
Wouldn’t it be useful then if there was some software to scan email addresses to pick out the first and/or last names, or initial letters, so it would be clear that the salutation for [email protected] is not Eileen?

Yes, helpIT systems does offer email validation software, but the real reason for highlighting this is that we just hate it when innovative marketing is compromised by bad data.  That’s why we’re starting a campaign to highlight data quality blunders, with a Twitter hash tag of #DATAQUALITYBLUNDER. Let’s raise the profile of Data Quality and raise a smile at the same time! If you have any examples that you’d like us to share, please comment on this post or send them to [email protected].

Note: As I explained in a previous blog (Phonetic Matching Matters!), the first four variations above are phonetic matches for the correct spelling, whereas the next four are fuzzy phonetic matches. “Toosti” and “Stoolchill” were one-offs and so off-the-wall that it would be a mistake to design a fuzzy matching algorithm to pick them up.