What About Small Data? Part 2
By Andy Hasselwander on July 31
Getting Back to Growth by Playing Small Ball
The ADBUDG curve is a 40-year old handy heuristic for modeling marketing spend vs. return. It was first used for broad-reach advertising. The concept is pretty simple:
- The curve starts out flat, as dollars are invested to get breakthrough with a group of consumers
- Then, the curve gets steeper as marginal returns reach profitable levels
- Finally, the curve flattens as the market is saturated with messaging, and the advertising no longer has much marginal effect.
Certainly, both direct and broad-reach marketers know of this curve, even if they’ve never heard of the word “ADBUDG.” There is a maximum amount of goods or services you can get the market to buy before marginal marketing dollars do not drive a profitable return.
However, this “plateau level”, at least on an aggregate basis, seems to be getting lower, year after year. Over the past two decades or so, at least four factors have conspired to compress this curve for marketers, whether B2C or B2B (I use the term “consumer” interchangeably):
- Consumers have become savvier, spotting obviously poor execution and ignoring it out-of-hand
- At the same time, consumer behavior has become more search-driven, turning the tables on the advertiser and waiting until they have a real need to find what they want on Amazon or Google
- The supply of quality interactions—be these on radio, telephone, television, or at retail—has gone down as consumers have shifted their behavior towards platforms like Netflix and Amazon, and have stopped picking up their phones, etc.
- At the same time, the marginal cost-per-touch for low-quality interactions (junk email, crap display ads, lousy ad time on the long-tail of the cable TV spectrum) has gone down, encouraging blasting consumers with touches, thereby further intensifying consumers’ programming to “ignore” marketing tactics.
The net effect of these trends and their impacts can be visualized as a series of curves getting flatter and flatter over time as the aggregate “ROMI,” or return on marketing investment, gets lower and lower.
A traditional way to deal with this problem is optimization. In other words, take that ADBUDG curve and find the best possible marketing mix, message, and consumers to target, which will optimize ROI by lowering costs to get the same return. This works well over the short run. Everyone is happy, because marketing has increased its ROMI. There is no noticeable impact on growth—the plateau of diminishing returns is reached sooner.
However, over the long run, there is a strategic problem—one that CPG companies, for example, have been facing for years—and that is, growth is harder and harder to come by. However, the economy is growing, and consumers are buying things. So where have the dollars gone? They’ve gone to small competitors, who have marketing departments of one or two, no marketing mix models, and are limited to small digital, agile digital campaigns that go from ideation to execution in days, not months.
If you want to see a concrete example of this, go outside and look at anyone between the ages of 14 and 40 today, at the brands they display conspicuously, at the clothes they wear, at the music they listen to. You will notice that most are telegraphing their individuality in an extremely deliberate way. There is a term that Sigmund Freud coined a hundred years ago that I think perfectly describes consumers and companies as buyers today—the narcissism of small differences:
Every single consumer / company thinks that they are unique, even if they’re actually quite similar.
It’s up to marketers and sellers to recognize this, understand them, and get them what they need to feel like they are being treated as individuals.
The implication is that now more than ever, marketers and salespeople need to go “small” when everyone is talking about “big.” Big data, big advertising, scaled campaigns, and machine learning are great—but what they are so often missing is the touch of the artist, and careful, high-resolution insights-driven thinking.
I was talking to someone the other day whose wife is about to age-in to Medicare, and she’s gotten hundreds of “idiotic” (his word) touches from various companies over the past months blasting them with the same message, over and over. She is now completely turned off on Medicare Advantage. She’ll buy something eventually, but only when she receives the right touch that acknowledges her as an individual—well, maybe not an individual, but at least as someone unique.
Companies optimizing their Medicare Advantage campaigns might put her in a lower decile, as she doesn’t respond to these touches, but she has the means and the need and will buy. Instead of optimizing in aggregate, or across coarse segments of tens of millions, these companies should think about micro-segmenting their campaigns, and understanding what she as a consumer actually needs and wants when it comes to health care; what her specific habits are; and how she lives her life. Simply acknowledging these differences will go a long way towards true optimization, and will drive incremental growth.
Concretely, this means micro-segmentation. This isn’t the huge, enterprise segmentation, but it’s rather a guerilla, agile attempt at understanding the small cells of customers, and reaching out to them in unique ways, all measured rigorously. For each micro-segment, a marketer should strive for unique insights, including:
- Core needs, wants, insights: What is the real, non-trivial, second- or third-level insight that makes this small segment of consumer care about what I’m talking about?
- Channel mix: How do I build a go-to-market strategy that intercepts consumers and companies where they travel, and where they care about what I’m selling?
- Content / message: How do I get the right, unique content and message in front of that call of a few companies / a few hundred thousand consumers, where it will really resonate?
- Product: It’s not always possible, but can I build a product portfolio with enough diversity to acknowledge difference, while staying profitable?
This requires striving for breakthrough insights among small cells, and these insights then have to spread throughout a marketing organization that embraces “small ball”—the wins that come from looking for nuggets instead of the whole gold mine.
This does not mean giving up on analytics or data science—it’s actually the logical extension of it. Analytics goes from being huge and aggregate to micro and artful.
It does mean spending more time looking for the insights sources that will get marketers a greater depth of insight into markets, prospects, and customers. It does mean doing bringing data scientists into qualitative research sessions.
So what about that ADBUDG curve getting squashed by oversaturation? If you play analytical “small ball,” you’ll be optimizing lots of little ADBUDG curves, one for each of the micro-segments. The saturation level for each of these is higher, so when they are summed up, the aggregate curve rises.
Some of this can be done with technology—for example, some of the very good targeting that is possible with Instagram today based on location, text mining, and image—but much of it still boils to good old-fashioned insights work, and making cell sizes smaller. Another way to think about small-cell marketing is in terms of a campaign / micro-segment portfolio; each “fund” is optimized, and then the entire portfolio of campaigns is optimized for efficiency, as a whole.
One final note; I continue to believe that insights-driven, small-ball analytical marketing is ultimately an organizational challenge.
Marketing and sales organizations have to be built to think like customers, like individuals, while at the same time being relentlessly data-driven.
These two things are not mutually exclusive. The old trope of the “geeks” and the “creatives” is just plain wrong. Merging these two worlds, and successfully playing analytical small-ball, is a really good way to move a big company from efficiency to efficient growth.