It’s more influenced by data and past patterns than current conversations
Editor’s note: Industry consultant Shelly Palmer is taking his popular newsletter and turning it into an Adweek article once per week in an ongoing column titled “Think About This.”
At a recent dinner party, an accomplished executive told a story about how he and his wife were certain that their devices were listening to their conversations.
“I was talking to my wife about a pair of designer shoes that she wanted to purchase, and not 10 minutes later while she was doing some online research for work, she saw an ad for that exact pair of shoes,” he said. “She hadn’t searched for the shoes; the ad just appeared. Clearly, our computers or our phones are listening.”
I listened politely for a few minutes, as other guests shared their own versions of “surveillance state” anecdotes. Then, I spoke up.
“Which do you think is more likely? There is secret software that breaks about 20 different local, state,and federal surveillance and privacy laws, that neither I nor any of my clients know about but that are being secretly used by me, my clients and other advertisers to put the right message in front of you at the right time in the right place? Or, thanks to your online behaviors (and the privacy policies, terms and conditions you have agreed to), we have access to enriched data sets and our predictive models and machine learning tools have evolved so quickly that we have an uncanny ability to understand your behaviors well enough to put the right message in front of you at the right time in the right place?”No one is listening to your conversations with any tool that will be used to put advertising messages or content in front of you.
This begets more questions from the party. “What is an enriched data set?” “What is online behavior?” “What is a predictive model?” “What kind of machine learning are you talking about?” “Is that AI?” And my favorite: “How do you know what I’ve been talking about with my friends?”
Enriched data sets
Data is more powerful in the presence of other data, and much of our data is public: address (phone book), cars (warrantee lists), owning versus renting (public records), work (location data from your phone, LinkedIn or other public websites), how many people you are responsible for (inferred from their purchasing data), what you ordered for dinner (via social media posts), where you had dinner (credit card info, which is legal to obtain if the company has a business relationship with you), how much debt you carry (a credit report), your credit score (credit reporting organizations), etc.
The more data you have, the more accurate your predictions can be. But now let’s add in online behaviors.
When you click on something, you exhibit an online behavior, stopping while scrolling a social media site to look at a meme or message, swiping left or right, tapping an icon on your smartphone, picking up your smartphone (accelerometer), walking or running using a health app (GPS), talking to Siri, Alexa, Google, Cortana or Bixby, or playing a game of any kind on any device. All of these behaviors are captured, logged and used to enrich your profile.
However, your enriched profile is not in one place, and every company that wants to send you a targeted message does everything it can to create a single view of the customer. This includes cobbling together the most robust, enriched data profile possible. The better the profile, the better the predictions.
Most predictive models fall into two general categories: classification and regression.
Classification algorithms identify new data as belonging to a specific class or category. There are binary classifications (two possible outcomes such as male/female) and there are multi-class classifications (data that may belong to multiple classes or categories). This is roughly analogous to a person asking, “What is this?” then thinking about it and declaring, “Oh, it’s a cup. Let me put it in the cupboard.”
You are part of several classes that include your family members, friends and communities of interest. If you have been mathematically placed in a class with people who are likely to be discussing designer shoes, you’re going to see ads for designer shoes. Is it a coincidence that you were just talking about designer shoes? No. The algorithm was 92% confident that you had a 71% chance of talking about designer shoes.
Regression analysis can be used to infer relationships between independent and dependent variables. If I know a bunch of stuff about you such as your income, zip code, monthly mortgage payment, type of car you drive, age and gender, I can use regression analysis to predict what car you are most likely to want to buy or lease next.
Machine learning and AI
You know your business, your customers, your industry. A 10-by-10 spreadsheet with the numbers would describe things you have experienced in real life, and you would be able to explain (using the language of arts and letters, not the language of mathematics) what the numbers mean.
However, if that data set was 25,000 columns by 25 million rows, there is no way anyone could ever look at or interpret the data. That’s why it’s called bigdata.
To look at big data, you need computers. And to make the data actionable, you can teach machines to do predictive analysis. Machines can learn now, and predictive analytics is one of the things machines learn to do very well.
Why you think your devices are listening to you
All of this brings us back to the big question: Is Big Tech now Big Brother?
With respect to your private audible conversations, meaning spoken words that might be recorded and interpreted, unless you are under surveillance by a government agency with a warrant or being illegally eavesdropped on, no one is listening to your conversations with any tool that will be used to put advertising messages or content in front of you.
However, every other device in your world (including Alexa and Google Assistant after you say the wake words) takes whatever behaviors you exhibit and whatever data that can be gathered about you and uses it to make predictions about your behaviors. So, in practice, everything is “listening” to you—just not humans in rooms with headphones. It’s more like computers in data centers using AI.
What to do about it
Now comes the hard part. We have to figure out if the benefits of accurate messaging and the convenience of our machines knowing us at the most intimate level are worth the risks. Designer drugs created from our own DNA seem great. Tools that can read our emotions and help us cope with complex issues seem scary, but also great. Self-driving cars that know where we are going and how we like to travel seem awesome, too.
All of this requires big tech to have unfettered access to our data. Should it? If so, what data? Whose data? In the next few years, we are going to have to vote—mostly with our wallets—about this. Our elected officials will have to deal with it, too.
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