Veteran marketing managers can tell war stories of battles fought to secure marketing budgets – the pitches and cajoling to focus C-suite attention on the strategic and the tactical importance of effective marketing campaigns. Getting something close to the budget you want may be just cause for heaving a big sigh of relief, but these days few marketing managers will be found clinking glasses of Veuve Clicquot in celebration. Once the budget is in hand the real work begins. The economic downturn has put constraints on the total number of dollars you have to spread among competing projects, but it has done nothing to constrain the nearly limitless ways those dollars can be allocated. “Do more with less” is the mantra of the day. To make those scarcer dollars go further means relying on more than traditional finger-in-the-wind gut instincts to tell you what campaigns will work and what campaigns won’t work. Campaign marketing – the art of pulling together targeted messages for specific geographic markets, consumer segments and product types – is in need of a healthy dose of scientific rigor.
Remember the Rubik’s Cube? That delightfully maddening cultural relic of the 1980s was challenging because you had to configure the right sequence of moves in three dimensions. One small misstep – one rotation along the wrong axis – and the whole strategy would fall apart. Now, think of trying to solve a Rubik’s Cube-like puzzle, not in three dimensions but in at least five! Our visual cortex regions boggle trying to imagine what this hypercube would even look like. Yet that is the gauntlet thrown down to campaign marketing managers: configure the (1) right customer with the (2) right product and the (3) right promotional offer using the (4) message via the (5) right channel. A typical challenge of this nature presented itself to one of our clients recently: configure eight potential messages to 50 customer segments in 70 regional markets concerning 50 product categories and four distribution channels:
8 x 50 x 70 x 50 x 4 = 5.6 million unique campaigns for budget consideration!
That is obviously a larger number of alternative spending choices than the unaided human brain can reasonably analyze. But the complexity doesn’t end there. With the old Rubik’s Cube there was only one objective: get each face of the cube to be one single color. Not so with the Marketing Hypercube (pictured in the diagram). There are in fact multiple potential target outcomes of any given campaign. Is the objective to build initial awareness of the company or the product? Or is it to instill preference among an audience already familiar with the product? Or, alternatively, is it to maximize actual purchases through targeted prices, promotional incentives, penetration opportunities, and/or purchase timing strategies? In effect, the targeted sales & marketing outcomes themselves represent yet another dimension of complexity.
So how do we solve a problem of this magnitude of complexity?
Perhaps it is somewhat counterintuitive, given that we have called for a strong dose of scientific rigor, but the first order of business is to put aside the mathematics, take a step back and employ some good old-fashioned human judgment (don’t worry, we’ll shortly come back to the mathematics when we start to build predictive models around customers, messages and objectives). Let’s start by remembering what we are trying to accomplish: to configure a campaign that will most effectively resonate with the target customer segments and accomplish our specified performance objectives. We want to be able to predict the effect of the campaign before it is even launched. This requires making some basic assumptions – but before your analysts integrate these assumptions into predictive models they need to obtain bottom-up business insights. These insights come from experience gained by your sales associates through interaction with their customers. For example, they can be gleaned from short 30-45 second surveys and similar diagnostic tools built around particular initiatives (e.g. price, penetration, wallet share, loyalty, and general awareness-familiarity-preference survey templates).
The next challenge is to align these insights with the right segments. Don’t think of this as a “once-and-done” event. You have hundreds of thousands of customers and there are near-limitless ways to segment them. The segments around which you build your first campaign iteration may not be the segments you employ in the end – or perhaps you will learn that those segments require different campaign strategies. This is an iterative process – sampling, inputting new insights into existing predictive models, aligning campaigns to segments, resampling, revising segment strategies, updating model assumptions and constraints, and repeating.
It may sound tedious. But over time this iterative process will help you greatly improve the accuracy of your predictive campaign models. You will be in the position to pinpoint the effects that a specific campaign had on improving the value of certain customers’ transaction baskets through penetration initiatives, for example, or to measure the contribution of a customer loyalty campaign to actual revenue saved through decreased contract defections. Those 5.6 million alternative budget allocations will start to look less daunting, and you will have a higher degree of confidence in making spending decisions closely aligned at a very granular level with your demand environment (for example, our client was able to more than triple its customer conversion rate through applying science to its campaign marketing process). In short, you will be able to do more with less – even if you are one of the many people who never did get the hang of the Rubik’s Cube!