It wasn't that long ago that direct marketers were a breed apart: data-obsessed spreadsheet jockeys who were constantly tweaking the knobs and dials of campaigns to yield incrementally better results.
Now, of course, everybody's data-obsessed; everybody's busy tweaking the knobs and dials. If you're marketing digitally, you're a direct marketer -- period.
Think about some of the hot trends in digital marketing -- from behavioral retargeting to real-time bidding on ad exchanges -- and they're all about direct marketing to individual consumers. Even image-burnishing branding campaigns that don't have an e-commerce component (i.e., they're not specifically designed to prompt a consumer to click through and make a purchase) are deployed using cookie-based data to target, in real time, consumers as they surf the web.
What direct marketers have known for years is that more data means better targeting, and better targeting means better results. And the best results are all about optimization. Of course, "optimization" means different things to different people. That's the problem with industry buzzwords: they tend to get diluted and distorted. So let's start with a couple of conventional dictionary definitions:
1. Making the best of anything.
2. A mathematical technique for finding a maximum or minimum value of a function of several variables, subject to a set of constraints, as linear programming or systems analysis.
When most companies talk marketing optimization, they mean the former (basically, Let's give it our best shot!). But the "mathematical technique" approach to optimization isn't necessarily complicated either, conceptually speaking. For instance, in key marketing areas such as email- or website-optimization, the most common techniques used to optimize are A/B testing and multivariate testing.
With A/B testing, a marketer will deploy two versions of an email, or two versions of a web landing page, and watch how each performs. The difference in metrics -- e.g., open rates, click-through rates, conversion rates, etc. -- might be subtle, or they might be dramatic, but either way the goal is to pick a winner and then keep on testing with new A/B sets.
Multivariate testing simply expands the number of elements that can be monitored at once. Essentially, though, A/B and multivariate testing are sort of a general version of "making the best of anything." They're old-school, see-the-forest-not-the-trees approaches in that they look at consumers as relatively monolithic groups; the underlying characteristics of individual consumers are ignored. The goal may be to try to get as many trees as possible in the consumer forest to sway a particular way, but the focus isn't on any individual trees within that forest.
The traditional response to the underlying weaknesses in A/B and multivariate testing approaches has been to customize content based on individual data elements - e.g., location, age, sex, etc. - or by delivering different content versions based on statistically determined segments. But in no case has specific content been statistically and mathematically optimized for each specific consumer according to their relationship at that point in time (i.e, "in real time") with a brand.
Optimization only gets really interesting when individual consumers are regarded as, well, individuals. Consider, for instance, a 34-year-old working mother, an existing customer of a brand, who visits a brand's website. Cookie-based data can tell an incredibly rich story about her that can allow the brand marketer to, you might say, hyper-optimize. The idea is to "see" the website visitor as a specific consumer with a relevant past - meaning she has a transactional history with the brand consisting of all her past purchases, as well as a track record of reactions to marketing campaigns (e.g., emails opened, clicked, etc.).
Wouldn't it be cool if the next email sent by the brand contained references to the content she's just consumed, as well as suggestions about a next logical purchase based on past purchase history? Imagine if the email also contained messaging specific to her psychographic characteristics. And then suppose an offer included in the email had been tailored specifically to her needs and desires based on real-time analytics.
That's the potential of optimization. When optimized campaigns work, they can be incredibly powerful for brands, and heartening for the consumer, who no longer feels like just another face in the crowd.