Tag Archives: Data

June 7, 2019 · 5:34 pm

Who Owns You?

Foodie Friday! I installed a couple of the food-delivery apps on my smartphone this week. Some of my favorite local places use the delivery services to expand their business and I thought having the ability to order in might be a nice option. Of course, that got me thinking about what exactly the restaurants got besides the additional order (at a lower price when you factor in the service’s cut but no service cost). The answer, as it is with almost everything today, should have been data but as it turns out, not so much.

The reality is that the delivery apps hang on to the data. They “own” the customer, not the restaurant, and that’s a problem, or it should be. Restaurants are giving up the direct connection to their customer by not getting that data and they have no way to combine it with their offline, real-world data gathered when I actually show up to eat as well as with the data they might get from a reservation service such as Open Table.

Ownership of the customer is an enormous issue no matter what business you’re in. For example, your car spits out reams of data about your location, your driving habits, and many other things. How many? A report by Consumer Reports said that “There are more than 200 data points in cars today, with at least 140 viable business uses.” Who owns the data and, therefore, the customer? The dealer who sold you the car? The manufacturer? I, of course, think the right answer is that YOU own the data until you give it to someone for a specific purpose.

Think about how many things around you gather data these days. Your TV, refrigerator, heck, even your toothbrush might be collecting information about you and your habits. Who owns you as a customer? I bought my TCL TV through Best Buy. It has Roku built in. Who “owns” me? What’s being shared?

It’s a question you need to ask as a business person when you partner or work with a third party. I think customer ownership is a fundamental issue and it’s only going to become more important. Of course, as a consumer, you ought to be every bit as concerned but we’ve talked about privacy a lot here so not today (84 posts and counting in the last 11 years!).

I really don’t care much about DoorDash or GrubHub. Without the restaurants they serve, I wouldn’t ever install or use them. I’m not their customer in any real sense – they provide a nice service but it’s the food I’m after, right? So why do they think they have a right to own me? Are you asking that question at all? Maybe you should!

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April 18, 2018 · 12:11 pm

Flying Blind

I almost called this post “Nobody Knows Anything” but that might have been overkill. I’ll say what I have to say and let you be the judge. Let’s say that you buy a friend’s newborn a gift. You have it shipped to your house. The data says, correctly, that you bought an infant gift. That might also lead to an inferred piece of data that places your household into the “presence of infant” bin, leading to you seeing lots of ads for diapers. If you’re the one placing the ads for those diapers, you’re wasting money.

Lots of the data marketers routinely use is of that sort. It’s inferred. You can see that some thinking at work if you’re a Netflix user: the recommendation engine infers what you might like based on your past viewing. Of course, if your kids or someone else in the house watch something in which you have no interest, the accuracy of those recommendations is diminished (which is part of why there are separate profiles available when you log in). Inaccurate data is, sadly, more the norm than an aberration. Since this data is really what’s behind personalization and targeting, that inaccuracy is a big problem. Any business that buys data from third parties – and an awful lot do so – may be putting garbage into their system. Unfortunately, most don’t know that because there is little transparency in the data business and it’s impossible to verify what’s good and what’s not.

What should you do? Invest in collecting your own, first-person data. You can also demand transparency in any other data you use (good luck with that) with respect to how it was gathered and what it really represents. Is it inferred or does it come directly from consumers (did someone tell you they had a baby in the house or did you guess they did because they bought one infant item?). Who owns the data and was it gathered with the consumer’s permission?

When Facebook tells its customers (marketers) that they have data on 41 million adults aged 18-49 in the US and there are only 31 million of those adults living in the US, you know much of the data is inferred and also that we have a problem. A recent study that found that 70% of marketers believe that the customer data their organizations are using for marketing is low quality or inconsistent. Why bother to market at all when you’re just flying blind?

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February 28, 2018 · 12:21 pm

Measure What You Can Measure

The NFL is getting ready for the annual combine. This is where players get tested both physically and mentally to see if they’re NFL material. There is psychological testing to test intelligence. They run the 40-yard dash. It’s a 4-day job interview, much of which plays out on TV.

Teams use the data to make decisions about which players to select in the annual draft. They can stack the reams of information from the combine with the data generated over the course of a player’s college career and choose someone who will, hopefully, fit into a team’s depth chart as well as its philosophy.

Anyone who follows the NFL will tell you that all of this data has its place but it’s far from infallible. Kurt Warner, a 2-time NFL MVP went undrafted. So did Warren Moon, a Hall Of Fame quarterback too. Put Tony Romo on that list as well. No team looked at the data and thought any of these men were worthy of a draft pick. Oops.

You just might be guilty of the same thing in your business. The data isn’t infallible and the data only measures what it’s designed to measure. Tom Brady (selected 199th in his draft year) recently told NFL prospects that they can’t measure heart. He’s right, and it’s because there isn’t a solid way to capture that data.

How are you making this mistake? You might be using one data point to draw a conclusion that isn’t right. Correlation isn’t causation, as we hear so often. Grateful Dead fans don’t all smoke pot and have long hair. Identifying a target as those fans doesn’t mean you should be promoting to the stereotype.

Another faulty conclusion might be due to an error in the data itself. I had an advertiser on a site I ran complain that they weren’t getting great results. They had neglected to respond to a question from their salesperson about turning on frequency capping to extend their reach and limit the number of times a day someone saw their ad. They were reading the data correctly but the data itself was faulty due to an underlying issue.

One of my favorite data error is the foundation of the entire TV business, the Nielsen Ratings. The TV and ad industries have attached an accuracy level to Nielsen ratings that even Nielsen says is unreasonable. A study of a few years back found in analyzing 11 years of data that the margin of error for reported results was often more than 10%. That might not sound like much but it can represent hundreds of thousands or even millions of impressions. The issue here is that buyers are too focused on the (inaccurate) numbers rather than on precise metrics such as sales.

Measure what you can measure. Don’t extend that measurement to other things that aren’t measured as well. I bet your results will improve. Let me know?

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