While we’re all focused on delivering our holiday plan, every CMO I’ve spoken with in the last three months is focused on the same things. In 2016 “we need to achieve greater scale” — in other words, to get bigger. It would seem for sure, bigger is better. Bigger is the American way. Bigger sales, bigger profits, bigger staff and teams, bigger assortments, and bigger margins. Yes — bigger it seems is much better to CEO’s, CMO’s and board members everywhere.
“The Onceler” from Dr. Seuss’s “The Lorax” succinctly said: “… I’m figgering on biggering.”
For marketers, new customer acquisition is the most effective way of to grow the organization “bigger.” It’s the lifeblood of growing organizations, and is generally considered a sign of a business’s overall health.
Yet customer acquisition can be resource and budget intensive, even if an solid value, and marketers require effective more intelligent approaches to achieve greater scale while maintaining budget guidance.
So as you lay out plans for 2016 and how you’ll scale your business “bigger” — and hopefully better as well — it likely makes sense to think through the most effective ways to drive scale. Leveraging customer data with ever increasing intelligence is the common thread from some of the best strategies I’ve worked with brands on successfully over time.
Advertising is being increasingly automated, and over time, it’s expanding across web, mobile, and now television and “over the top” television (think web-based TV — where a programmatic buy may land your ads on Netflix one day soon). Programmatic display advertising is largely however a web based phenomenon. It adds data and certain controls to your ability to target ads that are very different than the traditional “insertion order” contracts of yesteryear where you were locked into a certain number of ads for a certain amount of time at a fixed cost. Now you can choose your targeting criteria using oceans of third party data sets that are overlaid through cookie exchange.
Some of the downsides of “PA” — well it’s still advertising. That is, we’re displaying ads that are increasingly more relevant, but are they really seen? Do they have the palpability or engagement of other forms of advertising? Generally not.
Viewability is another problem, as is fraud. Often times PA’s don’t provide an accurate identification of where your ads are even being served, much less if they are served well below the fold, where basically you’re paying for an ad that no one’s ever seen, or is going to see.
Also, for the highest value ads, more advertisers are likely to bid on them. Like any bid based vehicle, programmatic advertising models ultimately bid up the cost of reaching the consumer that more marketers want, effectively making for maximum competition (and again, higher ad prices) even if excess inventory may drive some ad inventory costs down today. Remember, this competition amongst advertisers bidding up programmatic advertising is in large part designed to best serve the media outlet, even while delivering better ad products to the market.
List Rental and Univariate List Selection
With the explosion of data access and availability, it’s easier than ever to rent a list and prospect to individuals who visit Morningstar mutual funds, live in New York City or are active runners, and virtually anything else you can dream up. List quality remains the challenge, and testing your way into a process is critical.
Univariate list selection is typically a select by an interest, though it can be on many other singularly focused targeting criteria. It’s a list where members share one criteria, such as an interest in sailing or presence of children.
The downside of traditional list rental is price and performance. You really have to be buttoned up to make traditional list rental on a CPM basis work well, and you tend to get it performing best when you or your agency have developed a relationship with the data source, and earn preferential pricing and terms. There are instances where the target and the list line up very well, and thoughtful univariate list rental can work well — typically these are substantially niche or “edge” cases or, very much ‘mass market” circumstances. The huge “middle” is high risk for a univariate list, in my experience.
Multivariate List Selection
This is list rental as well, but instead of working off a single common attribute, a data broker takes your set of multiple “selects” and produces a “count” of the number of matches. Examples can include age (usually banded, not to the year) and other demographic attributes like income, presence of children, and interests. It is relatively painless to create a target that sounds a lot like your target customer, if you work with reputable data vendors.
Combining multiple data points zeroes in on your target, and is usually expected to improve response. Since each select or targeting criteria adds cost to the list rental, your conversion requirements must go up to achieve your economic goals for the program. The same can be said for programmatic display buying. You pick the criteria for the target you want to advertise to, at a cost.
Most “look alike” marketing methods are little more than a multivariate selection.
The question it will help to answer beforehand, if you can, is are you choosing the right variables or criteria? If it sounds right — it may be, but that’s not a guarantee. Because data vendors don’t have a stake in the outcome and are typically responsible only for providing the data you ask for, list rental of this nature is often maligned for poor performance and exorbitant cost. This may not always be the case however — in many cases in my experience, the marketer didn’t define the target particularly well.
Target definition is no small matter, especially when your mandate is to efficiently become “bigger.”
“This underscores the real challenge — when picking the criteria for prospecting, you are, either consciously or unconsciously, presuming that the criteria you are choosing, is actually predictive of buying behavior.”
Multivariate Modeled Selection, The Next Level Up in Sophistication — and Efficacy
In many cases, it may not be enough to choose variables and simply pick criteria to define your prospect. So some proven math and statistics can become the marketer’s best friend. Models are statistically derived targets. When we build a model we start with data, not opinion. We then use a marketing database like BuyerGenomics, to provide customer intelligence and understand the varying segments of buyers that exist — even if we didn’t have resolution to see them yet.
With these insights we can now develop a model that shows us the criteria or attributes of our customers that are most impactful. They often are clear enough and may sound like your expectations, though we do find nuances, which can improve the selection of prospects we will market to.
Our models can combine data points to infer new ways of looking at who our target customer is, like ethno-demographics, the relationship between ethnic attributes and income/age, or geo-density, the concentration of a customer in a specific location, for example.
This is surely a step up from “look alike models” (different from user selected “look alike criteria” in multivariate marketing).
Multivariate modeling can also require that we enhance customer data with many, many variables and then determine the variables that are most predictive of who our ideal prospect really is. As a result, we have a fairly reliable approach for testing our target definition and validating that we’re zeroed in on our target.
True Response Modeling: The Gold Standard
For marketers seeking to scale up and grow substantially, for example thousands or tens of thousands of net new customers, a true “Response Model” is the most sophisticated, and highest value in targeting a universe of most likely responders.
This should not be confused with any of the increasingly common “look alike” approaches, which are common to programmatic display advertising, univariate or multivariate targeting. It is also substantially more effective than the multivariate modeled approach we’ve already described above.
A true response model has a number of characteristics that can dramatically distinguish it, and improve your prospect marketing performance. Firstly, it’s a custom model, you can’t get a custom response model built by someone who knows nothing about your business — and it’s a different solution altogether than buying a target based on one or more variables or targeting criteria.
Response models actually use the target from, for example, a multivariate model, like we described above. This “effective target” is then marketed to, and the responders from that target are then further analyzed and refined into one of the most exacting targets, one which we can project or simulate the lift we expect over the multivariate model.
What does this all accomplish?
The RM solution gets us to a place where we can now identify the statistically validated “ideal” target customer and then back out ever larger populations to target to maximize the number of new customers acquired.
Putting it All Together
The prospect marketing approaches herein often build on one another, and the most effective growth for performance marketers often comes from building on prior learnings and successes. It all begins, of course, with having your data in good order, and having governance and hygiene process that are working.
But to change the acquisition game, getting started is often the hardest part. That generally means having a plan to move from investment, to learnings, to optimization and then to achieving the scale desired. While it’s usually not an instant gratification effort, the approaches described herein can be done expeditiously and with ever-increasing intelligence and efficacy.
So if you’re organization is “figgering on biggering” in 2016, there’s no better time to assemble the Data Athlete’s on your team, and focus on proven prospecting technologies we’ve discussed herein. When you do, your customer base will grow “bigger,” and done well, your growth will have increasing mathematical certainty.