Personas, Be Gone: 1:1 Marketing Revisited

Soccer moms, coffee house professionals, gears-and-gadget guys — in the world of data marketing, the audience personas available to select from enterprising data vendors go on and on and on. Tailoring and targeting based on personas — with hundreds of variables and data elements — dominate the business rules that direct billions in media spending and gazillions of business rules built inside customer journey mapping.

Millennials are not the only ones who eschew labels.

Soccer moms, coffee house professionals, gears-and-gadget guys — in the world of data marketing, the audience personas available to select from enterprising data vendors go on and on and on. Tailoring and targeting based on personas — with hundreds of variables and data elements — dominate the business rules that direct billions in media spending and gazillions of business rules built inside customer journey mapping. Practically every retailer, every brand, has a best customer look-alike model — and segments to that model.

But ask most consumers — they say they don’t want it that way.

An international survey released last week by Selligent Marketing Cloud, reported by Marketing Charts, says that 77 percent of U.S. consumers want to be marketed to as individuals, rather than as part of a larger segment.

Credit: MarketingCharts.com

The take-away seems to be that personalization at a 1:1 level should be any brand’s consumer engagement mantra. Throw out those data segments to which you may think I, the consumer, belong. “Pay attention to what I’m doing!”

That Darn Privacy Paradox … Again

Yet there’s a paradox here. “Paying attention to what I’m doing” raises the creep factor. The same survey shows that nearly eight in 10 consumers have at least some concerns about having their digital behaviors tracked, findings that seem to echo greater societal concerns about technology and business, with real branding impact.

Part of the addressable media conundrum comes down to intimacy. My mailbox is outside my door. I have no issues with personalization there, and I expect it. But pop “into” my laptop and now you’re getting closer to how I spend my days and nights — moving between work, play and life. That gets even more pronounced on the most intimate media of all, my smartphone. (I suppose a VR headpiece might be the “what’s-next” level of intimacy — or an embedded chip in my forehead.)

Conflicted as a marketer? Which path does my brand follow?

Revisiting Moments of Truth

One might argue that going from mass marketing to 1:1 marketing is an easier step than going from database marketing to 1:1. I’m reminded of Procter & Gamble’s moments of truth, freshly updated. A brand doesn’t need to know everything I do all day long in order to recognize the critical moments when purchase consideration comes into play. Less in-your-face, more in-the-right moment.

“Delighted, table for one.”

Whether database or 1:1 (or some combination of both), I cannot think of a smarter marketing scenario — one that engages the consumer — that does not depend on data, analysis, insight and action. Even the beefs that consumers have with marketing — remarketing when the product is already bought, not being recognized from one screen to another, for example — are cured by more data (transaction data, graph data, respectively here), not less, and such data being applied in a meaningful way.

“I’ll order the sausage, please. It’s delicious.” (Just don’t tell me how it’s made.)

In this age of transparency, we can no longer hide behind veils of ad tech and algorithms. We must explain what we’re doing with data in plain English. Based on the Selligent Marketing Cloud survey, for most consumers, it seems the path is to tell exactly how data are collected and to serve each as individuals. And we need to be smarter when, where and how ads are deployed even ad professionals are blocking ads today.

As for vital audience data, maybe we should re-think how we explain segmentation to consumers — less about finding “lookalikes” and more about serving “you,” the individual.

Measuring Customer Engagement: It’s Not Easy and It Takes Time

Here’s what’s easy: Measuring the effect of individual engagements like Web page views, email opens, paid and organic search clicks, call center interactions, Facebook likes, Twitter follows, tweets, retweets, referrals, etc. Here’s what’s hard: Understanding the combined effect of your promotions across all those channels. Many marketers turn to online attribution methods to assign credit for all or part of an individual order across multiple online channels. es as the independent variables.

Here’s what’s easy: Measuring the effect of individual engagements like Web page views, email opens, paid and organic search clicks, call center interactions, Facebook likes, Twitter follows, tweets, retweets, referrals, etc.

Here’s what’s hard: Understanding the combined effect of your promotions across all those channels.

Many marketers turn to online attribution methods to assign credit for all or part of an individual order across multiple online channels. Digital marketing guru Avinash Kaushik points out the strengths of weaknesses of various methods in his blog, Occam’s Razor in “Multichannel Attribution: Definitions, Models and a Reality Check” and concludes that none are perfect and many are far from it.

But online attribution models look to give credit to an individual tactic rather than measuring the combined effects of your entire promotion mix. Here’s a different approach to getting a holistic view of your entire promotion mix. It’s similar to the methodology I discussed in the post “Use Market Research to Tie Brand Awareness and Purchase Intent to Sales,” and like that methodology, it’s not something you’re going to be able to do overnight. It’s an iterative process that will take some time.

Start by assigning a point value to every consumer touch and every consumer action to create an engagement score for each customer. This process will be different for every marketer and will vary according to your customer base and your promotion mix. For illustration’s sake, consider the arbitrary assignments in the table in the media player, at right.

Next, perform this preliminary analysis:

  1. Rank your customers on sales volume for different time periods
    —previous month, quarter, year, etc.
  2. Rank your customers on their engagement score for the same periods
  3. Examine the correlation between sales and engagement
    —How much is each point of engagement worth in sales $$$?

After you’ve done this preliminary scoring, do your best to isolate customers who were not exposed to specific elements of the promotion mix into control groups, i.e., they didn’t engage on Facebook or they didn’t receive email. Compare their revenue against the rest of the file to see how well you’ve weighted that particular element. With several iterations of this process over time, you will be able to place a dollar value on each point of engagement and plan your promotion mix accordingly.

How you assign your point values may seem arbitrary at first, but you will need to work through this iteratively, looking at control cells wherever you can isolate them. For a more scientific approach, run a regression analysis on the customer file with revenue as the dependent variable and the number and types of touches as the independent variables. The more complete your customer contact data is, the lower your p value and the more descriptive the regression will be in identifying the contribution of each element.

As with any methodology, this one is only as good as the data you’re able to put into it, but don’t be discouraged if your data is not perfect or complete. Even in an imperfect world, this exercise will get you closer to a holistic view of customer engagement.