Yesterday, actually it was a normal day for me, I again experienced why marketing automation won’t work without data quality measures. I attended a webinar by a French marketing automation company who had a really nice tool to track customers on the website and collect leads for further processing. They have put much effort on a rule based engine to segment leads and a state of the art backend to have a nice working environment. I then asked: What happens if a person mistyped his e-mail address? What happens if a person set fills out his name in lowercase? What happens if the person has a typo in the postal address? First there was no answer. But then the webinar leader said: Why should someone do that? The answer is easy: Because we are human! There is a certain percentage of people who are not 100% concentrated when filling out a form. Maybe because they are using their smartphone where a typo can happen very easily, or they simply don’t know their correct e-mail address (I have often seen Austrian or German e-mail addresses with @gmail.at or @gmail.de but, as we all know, there is only gmail.com).
So this sophisticated marketing automation tool had no functions to validate or correct the data a lead / customer puts in the form. Garbage in garbage out. This people will never receive information they requested or have their name spelled wrong. What would you think about a company which addresses you with Mrs. john dOe .
Don’t get me wrong. Marketing automation is a great thing but most of the companies forget about the basics we once had: A responsibility for the data, a penchant for correct data. All we now do is collecting masses of data, and let the automatism do the job.
After that, I helped our team to correct and enrich a client’s customer database. We had 248.246 datasets of customer card information. With our automatic and manual process we saw very clearly how the data quality increases by every step. /data.mill showed us that only 27,81% of the postal addresses were correct and 71,36% could be corrected automatically. In 0,83% of this dataset there was no hit: Meaning our /data.mill could not find any clue. After looking to that data in detail, we saw that there is no chance to get this data corrected. But hey increasing the number of correct postal addresses from 69.029 to 246.182 is a great achievement!
After the postal addresses have been corrected we looked at the names of the customer database. It was the typical mess. Mixed up first- and lastnames, company names as first name, wrong gender code, and incredible academic titles (the funniest one was Autolakira, which describes a car body painter in German dialect). I then thought back to the marketing automation tool earlier. What would be the salutation line for this customer: Dear Mr. Autolakira Johnny Carwash & Tuning Inc. Sounds funny, but is certainly not a way to talk to a customer.
What also was very interesting, that we found 1.718 names, which could not be processed in /data.mill. We looked at the names to get more detail and of course had some funny ones like Mr. Gunsen Roses, but also a big number of names like fdjkslhjakasd. I don’t know why people type in names like this are there any psychiatrist out there who have an idea? but it was interesting that these strings have a one thing in common. Starting with the letters d,f,j,k,s, – so all letters in the middle of the keyboard. We are now looking at these datasets to find patterns and learn more about this phenomenon.
Back to the facts: We corrected 16,94% of first names and 17,82% of the last names (mostly put the first letter to Uppercase). 1,75% of the datasets had the wrong gender. So back to the first question: Why do people do that? I don’t know because we are human I suppose but what I know is, that you need to improve the data quality if working with marketing automation or you won’t be able to communicate with your client or prospect in a respectful dialogue.