It’s a cardinal rule of consumer marketing, learned in the first year of business school: You must segment your market. The principle has prompted an endless quest by marketers and their ad agencies, retailers, and research firms for new segmentation schemes that promise accurate — and actionable — insights into consumer behavior. Over the years, demographics (age, education, income) have given way to psychographics (attitudes), and even to geopsychodemographics (age, education, income, attitudes, and location) as a segmentation framework. Whatever the approach — the motivational research popular in the 1950s, the VALS system of the 1970s, or the PRIZM methodology that was the rage in the 1980s — segmentation has always been predicated on a simple equation: Who consumers are indicates how to market what they’ll buy.
The focus on demographics — the outward and visible signs of inward attitudes — grew more out of marketers’ need for analytical criteria than out of any inherent link between a person’s demographics and shopping behaviors. Age, gender, and wealth correlate well — on average, over time — with underlying attitudes; historical shopping behaviors, such as credit-card usage, are the best indicators of typical future behaviors.
Do those predictors hold true for Internet marketing? Since the early days of the Internet, online marketers and retailers have relied on those accepted formulas. They believe that a combination of demographic and attitudinal data, derived from knowledge of the sites users commonly visit, provides all the relevant information needed to create effective messages and aim them at target consumers. But this approach simply applies traditional marketing methods to the e-world, without exploiting the Web’s unique strengths. The abysmal performance of targeted banner advertising on Internet portal sites, where click-through rates today average 0.1 to 0.2 percent, underscores the failure of this conventional wisdom.
Wherein lies the flaw? An exclusive study by the Digital Customer Project, an alliance between Booz-Allen & Hamilton and Nielsen//NetRatings Inc., shows that the most effective segmentation scheme for online consumers first groups them by their individual behavior at a point in time, not by demographics or psychographics, or even by aggregate online behavior. We call this form of segmentation “occasionalization,” since it is based on what people do on the Internet on different occasions. Unlike traditional online segmentation systems, which rigidly define consumers (e.g., as Web surfers, e-mailers, etc.), occasionalization recognizes that effective targeting does not depend only on who the user is. Rather, it analyzes users’ moods and how they are using the Web at particular moments.
Our detailed study of several thousand Internet users uncovered seven discrete usage-based segments — or occasions — each defined by a widely shared, yet distinct, behavioral pattern that revealed how users “consumed” the Internet on these occasions. The study indicates that users’ activities and behaviors during different Internet sessions, regardless of the users’ demographic and attitudinal characteristics, show propensities specific enough to be predictive, within the context of a well-developed online marketing plan.
Occasion-based segmentation has long been applied in offline marketing, but we believe it should be a primary tool in online marketing — hence the need to give it a name associated with its application. Indeed, how occasion-based segmentation in the offline world works is the reverse of how occasionalization does. Offline, marketers must choose a segment and a way to serve it; online, marketers are free to expand their reach to multiple segments, creating distinct offerings as consumers move from occasion to occasion. The same consumer could get a different offer — or no offer — depending on his or her online activity during a specific occasion.
Thus, effective online marketing planning must reflect how the consumer is using the Internet in a given session. Only then do more traditional factors for targeting consumers come into play. By decoding the type of session in which consumers are engaged — for example, one focused on gathering product information — marketers can fully harness the Web’s interactive powers. They will be better able to create messages and offers that will appeal to target consumers when they are most open to responding to those specific messages and offers.