Placing Sure Bets on Customer Knowledge
In the game of roulette, there are black rows and red rows. Gamblers can place their bets anywhere they choose. On so-called even-money bets (red/black, high/low, and even/odd), the odds of winning would seem to be exactly that: 50:50. Yet given enough time, the casino always wins.
That’s because the casino has a subtle edge that tips the odds in its favor: a green zero and double-zero slot. So the odds really aren’t 50:50. On an American-version table the house advantage is actually 5.26%.
“Mass marketing does the same thing for our brand,” a marketing exec at a consumer goods company told me, rationalizing the huge sums of money he continues to pour into prime-time TV spots despite the overall decline of mass-marketing effectiveness. “Everybody knows our brand, but a continuous stream of gentle reminders keeps it top of mind.”
His company isn’t the only one lining up to keep those streams flowing. As of last week, some of the major broadcast TV networks had already sold almost half of their upfront ad inventory, at 2%-5% price increases over last year on a cost-per-thousand-viewers (CPM) basis. And while a 5.26% advantage works well for a casino, it’s not much in the competitive free market.
Yes, traditional mass-marketing tactics are alive and well. At the same time, to help compensate for audience fragmentation and boost their house advantage, smart companies are infusing more customer knowledge into their marketing decisions. For starters, this means learning as much as possible about the key attributes and behaviors of profitable customers within narrowly defined segments and then acting upon those insights to deliver more relevant and context-sensitive messages and offers.
Consider Best Buy, the $30 billion consumer electronics retailer. Its well-publicized customer-centricity strategy—with a segmentation scheme that uses names like Buzz (the young tech enthusiast), Jill (the suburban soccer mom), Barry (the wealthy professional guy), and Ray (the family man) to identify audience segments—has already boosted the company’s marketing effectiveness, allowing it to interact with customers in ways that are far more responsive to their individual wants, needs, and situations.
Speaking last month at Fair Isaac’s InterACT conference in San Francisco, Matt Smith, Best Buy’s senior marketing director of customer insights, explained that the infusion of customer knowledge allows the company to combine the creative messaging and tonality best suited to each customer with very specific offers, based on their behavior, and to then package and deliver it in a format consistent with the brand—and in a consistent way.
A year ago, said Smith, the company’s direct marketing pieces looked like “postcards from around the world.” Each piece had a different look and feel because each piece was developed by a different product marketing team. Now the pieces are developed by a customer-centric segment marketing team and look like “personalized stationery.” According to Smith, customer centricity translates into mass advertising in a simple way: “We can make TV buys in areas where we know Barry or Jill are going to be frequenting, and we can target the different publications they read.”
Nowhere does customer knowledge play a more critical role in driving marketing decisions than in the pharmaceutical industry, where companies use a broad spectrum of marketing levers—from direct-to-consumer channels, including TV and print ads, direct mail, and e-marketing, to sales force activity, which involves making sales calls to doctors’ offices and leaving behind drug samples. The question is, given the proliferation of available tactics, how does a company allocate its marketing dollars to get the highest marginal ROI?
The common industry approach to promotion allocation is to conduct a retrospective analysis that studies the impact of past marketing channels at the portfolio level. The problem is that a lot of assumptions are made to estimate the affect of changing the investment outside of what has been spent in the past. “Gee, I spend $50 million on TV advertising, and I sort of have a sense of what the impact was on my entire portfolio,” a brand manager might reason. “So maybe bumping that up by $5 million will yield a 2-to-1 ROI.”
In other words, the relationship between spend and results usually involves a fair amount of guesswork. This is especially true when it comes to understanding direct-to-physician marketing. What’s the physician’s reaction to the different channels? What’s the impact on a particular physician of, say, an additional two sales calls a month vs. another 30-second TV spot? Also, giving more drug samples to doctors will typically increase their adoption in terms of prescribing the drug. On the flip side, by oversampling, companies can cannibalize their own markets, since doctors usually give away those extra samples, resulting in a downturn in prescription rates.
My colleagues at Fair Isaac approached the challenge of promotional mix optimization by developing a physician-level decision model that takes into account a broad array of inputs to determine what set of marketing actions to take for each individual physician. Given a certain type of sales tactic, what is the likely reaction in terms of prescribing behavior for, say, Dr. Jones as opposed to Dr. Smith? The decision model allows companies to create profit curves that show what the projected increase will be in terms of prescriptions being written by individual doctors. By understanding each physician’s response to each channel at a granular level, companies can understand how to move the needle in terms of prescription volume. And by understanding the interaction effects between channels, they can make better decisions at the strategic level. This means looking at the total allocation across channels to understand the opportunity in terms of shifting dollars from channel to channel.
The decision model relies on multidimensional physician profiles based on data that include the physician’s area of specialization, how long he has practiced medicine, and whether he is a solo practitioner or part of a group practice (in which case, samples often get dispersed across the group). Most important, the model looks at past prescribing behavior. What drugs did the doctor prescribe during the past 12 months for a particular brand and within a particular class (such as hypertension)? All of this information is publicly available from data vendors like IMS Health, which aggregates data from more than 29,000 pharmacies and other data suppliers. For practically every physician in the United States, it’s possible to know what brands of drugs he is prescribing and in what quantities.
The output of the decision model guides physician-level actions. It tells companies which physicians to visit, how often, and the number of samples to leave behind. By optimizing these models, companies stand to gain a 10% increase in net revenue—which, in some cases, translates into tens of millions of dollars.
In short, whether it concerns a suburban soccer mom or her child’s pediatrician, the power of customer knowledge enables companies to make smarter marketing decisions. In an era of microsegmentation and advanced analytics, companies shouldn’t resign themselves to a house advantage that is merely the corporate equivalent of the green zero and double-zero slot on a roulette table.
Jeff Zabin is coauthor of “Precision Marketing” (Wiley, 2004) and a director in the Precision Marketing Group at Fair Isaac, a leading provider of marketing decision management solutions. He blogs at http://www.paretorules.com/ and can be reached at [email protected]
