Time is the only solution to privacy objections to behavioral targeting

Everyone involved in technology and marketing has had this conversation: They’re in a social setting, and the subject comes up about how technology is crafting messages to match consumer behavior. Someone pipes in, “Oh, like in Minority Report? That’s so wrong!” Although Gmail’s ads are customized based on the content of email messages, I’ve noticed that few object to that practice. Instead, they’ll complain about the ads appearing in Facebook — ads that clearly are using information users have given the network about themselves.

Big Brother Is WatchingIt’s a Catch 22 for online marketers. In order to boost response rates, we need to know more about the people viewing our ads. This work of behavioral targeting sounds like a win/win: “We’ll only provide you with the ads that you will likely care about.” But in practice, consumers get spooked.I’m reminded of direct response research done years ago. It was a survey to find out how people like to be hit up for contributions to non-profit causes.

Here’s how consumers responded:

  1. Least favorite: Personal asks
  2. 2nd Least: Telemarketing calls
  3. 3rd Least: Direct mail

What researchers at the time found telling was the direct correlation that existed between disliking a method of asking for donations and its effectiveness in getting them. In other words, personal asks — your sister-in-law selling Girl Scout cookies for her daughter — are most effective in terms of closing rates. The closing rates of telemarketing (back when this was a more viable medium for fund raising) were nearly as good. And a distant third in terms of effectiveness was direct mail.

So what’s really going on here? The accepted theory is this: We all have limited money to contribute to causes, and we would prefer to put off making decisions about where that money should go. Therefore, the most effective ways of forcing a decision are the least preferred.

Similarly, we love DVRs, because they allow us to zoom past commercials. They give us a way to avoid participating in commerce. They can’t touch us because we’re averting our eyes.

Behavioral targeting, if done properly, presents ads that also touch us. Thus, we look for reasons to hate the practice. Privacy is as good a reason as any and better than most!

So what’s the solution?

I do think that attitudes are slowly changing, and this change will eliminate privacy concerns as a reason to hate behavioral targeting.

Here’s an example. Consumers using social media are getting more comfortable with the various personas that they present on networks. They’ll show their “all-business” persona on LinkedIn, and their more casual facade on Facebook. Both are true depictions of the user, but they’re single facets of a full personality. Context determines how consumers behave on these sites. They are becoming accustomed to being watched by friends and business associates.

Reading Online Body Language

These same consumers are seeing how they can watch their friends right back. They are learning to “read” a friend’s feelings and preferences, based on online behavior. Consumers are getting accustomed to the online equivalent of body language. Or maybe that’s too strong a word. We’re all quite conscious of what we’re conveying, and body language implies unconscious action. Maybe what we’re doing on social media is more akin to a Kabuki dance.

Reading these dances is what marketers behind behavioral targeting are learning at a mass level, and turning into surprisingly accurate algorithms. But when it’s done by machines, in the service of a sale, many consumers still insist it’s “so wrong.”

Perhaps not forever.

To predict how people will react in the future — especially in areas such as privacy — I try to look at real world analogies. It’s not hard to imagine a web site as similar to a retail store. A while ago I wrote about how stores are analyzing shoppers using arrays of cameras. One system, called PRISM, is finding some unexpected insights into shopper preferences and behaviors. Shoppers aren’t getting freaked out. The prime reason: Most are oblivious to the surveillance.

But what if the people behind the cameras sprung up from their chairs and raced into the aisles, to say things like, “I saw you were looking at those lawn rakes. Can we interest you in some yard waste trash bags as well?” There would probably be lost sales, at the very least!

In the online world there are intercepts like this, but they are automated. This automated “intercept” is something people will become more accustomed to over time, as a generation weaned on social media, and used to their online movements being watched, comes into the majority. They will be able to understand that their behavior is being measured by machines as well as their online friends. They’ll realize there is no man behind the online ad “curtain” … just a predictive model.

Blurring The Lens To Reduce The Creep Factor

Another trend that will help the acceptance of behavioral targeting is a move toward more explicit boundaries. For instance, I expect an eventual backlash to camera surveillance such as PRISM. But before this reaction can take hold, the boundaries of store monitoring will likely improve. This improvement in technology will, for once, be toward discreetly blurring the “analytical lens” — instead of making it sharper.

Radio tomographic imaging is a new way to study store traffic. It uses arrays of extremely inexpensive radio transmitters and receivers, placed around buildings, to display people moving within. Below is a demonstration of the technology.

The benefits of this technology over cameras, at least to marketers, is cost — both in equipment and the labor of monitoring. Because people become moving “blobs” of color, it’s easier for computers to analyze traffic patterns and behaviors. Less can sometimes be more. Combine this information with RFID signals and you have a way to track a shopping basket all the way to check-out.

Imagine how this could be used:

Before he leaves that store, a consumer might someday pass an “intelligent end cap.” This smart store display knows — based on radio tomographic imagery, enhanced by RFID data sent to it — when to light up. The end cap would know the consumer has a yard rake, and offers him the trash bags he forgot to add to his list.

This hypothetical consumer will probably be grateful, since he really did need the bags for his yard project. And after all, he was just a blob of color moving through the store.

Ironically, this is the level of detail being used for most online behavioral targeting. This is the “privacy invasion” that is causing such a fuss on Facebook and elsewhere.

Over time, consumers will become more comfortable with behavioral targeting’s perceived betrayal of privacy.

That will leave only one valid objection the technique: They just don’t want to buy more stuff.

PRISM, a bricks and mortar store analytics effort, takes its cue from e-stores

It seems improbable that there was ever a time when skilled marketers didn’t use at least some traffic data to make improvements to their sites. The availability of this data, no matter how flawed, has been a chief impetus for the growth of web marketing as a discipline. The comparison was always with the dearth of similar data from bricks and mortar stores. Here are a few specifics:

Stage of Purchase Web Metric Store Metric
Initiation Unique Visits Foot Traffic Counts
Consideration Page Views None Available
Completion Transactional Data Transactional Data

This list is over-simplified, but it makes a point. In the Consideration Stage — between the time when the door store swings open and the time the purchase is rung up — there is little to help the bricks and mortar marketer understand the motivation of the customer.

By comparison, a web marketer has a full toolkit of metrics. There are page views to show visits to specific product pages and web site sections, exit pages to show when a consumer decides to leave without buying, and shopping cart abandonment metrics to show exactly when a consumer decided to stop his or her purchase.

This stark difference is changing fast.

PRISM (short for “Pioneering Research for an In-Store Metric”), is a Nielsen Media / In-store Marketing Institute co-production. Working with a consortium of retailers and consumer-goods manufacturers, the duo completed a test recently using sensors placed at key points in over 160 stores around the country. These sensors monitored the entrances and exits, as well as some store aisles, composing data sets and even heat maps of customer-traffic patterns.

In a way that is uncannily similar to web analytics, the PRISM system combined these traffic data with transactional information. The end game is to achieve greater insight into consumer behavior.

A chart from the videoAs you can imagine, the potential for improving the in-store experience is huge. Just as web marketers have walked away with significant improvements to their sites through web analytics, these marketers are nearly giddy with new-found knowledge. At least, that’s the impression I’ve received by press accounts of PRISM. This piece in In-Store Marketing Institute’s site is characteristic of that excitement, particularly in this accompanying Flash and Quicktime video. (Warning: The video is all talking heads. Sadly, demonstrations of the system at work are being held closely under wraps.)

Here is a typical insight, disclosed in the video by David Calhoun, CEO of the Nielsen Company:

“In some food stores, the heaviest traffic flow is not through the carbonated beverage and snack aisles — which might be the conventional wisdom based on sales rates — but through the yogurt and eggs section of the store.”

The chart above shows this (but not very well — it was captured from the video and only Calhoun’s narrative can identify the categories), with the two circled categories being the Yogurt and the Eggs sections.

Since the advent of web analytics, physical world marketers have looked at us web marketers with envy. It appears their time to play next to us, in the sandbox of database marketing, is just around the corner.