Defining an advertising strategy to target those who will pay more

Editor’s note: Michael Lieberman is founder and president of Multivariate Solutions, a New York statistical and market research consulting firm.

Not every dollar spent in advertising and sales efforts is created equal. Some of those dollars generate far more revenue than others. Indeed, it seems that if one only knew beforehand which dollars to spend, and at whom they should be directed, success would be far more certain.

My client wanted to find, define and then speak to a particular group of consumers who are willing to pay more for a commodity that is highly price elastic: gasoline. Our approach to market segmentation and customer targeting is importance-based, using a common segmentation technique called Q-factor. This uses the output of fairly straightforward factor analysis to group respondents into four distinct segments - two of which were combined to form a group of people, roughly 25 percent of the population, who were willing to pay more for gasoline.

This article follows a case study from the identification of the target segmentation, to determining sociodemographic characteristics, psychographic information, automobile self-description, client gasoline brand equity based on future behavioral intentions, and finally to developing a brand communication strategy.

Finding the payers

Cluster analysis is a term thrown around liberally in the market research world. In fact, it encompasses any number of techniques - formal and otherwise - that can be used to cluster respondents or consumers into marketable groups.

I prefer to apply a Q-factor. This technique uses a principal components analysis - more commonly known as a factor analysis - to determine underlying behavioral structures in those attributes included in the analysis. In simpler terms, factor analysis creates “families” of attributes that tend to be rated near the same level.

Our survey contained a section in which respondents are asked to rate the importance of 13 gasoline attributes. The factor analysis (Figure 1) grouped those 13 attributes into the families mentioned above.

Figure 1

After the factor analysis, SPSS software creates four new variables. Each respondent receives a factor score for each of the variables. He is then assigned to the one where his score is the highest. The segments are named according to the importance ratings contained in the division (Figure 2).

Figure 2

Not surprisingly, more than two-thirds of respondents clustered into the Price segment. Gasoline is a highly price elastic business and the biggest issue on people’s mind when they pull up to the station is price.

The Q-factor segmentation was only the first step in the search to find our payers. Now that we have the respondents in four groups, we want to know what they will spend for gasoline.

In the next section of the survey, respondents were asked, “How much more are you willing to pay?” across a series of gasoline attributes. It is by comparing each of the four segments above that we will be able to find our target audience.

Naturally, the largest group, the Price segment, were not amongst the biggest payers.

Though our cluster analysis yielded four segments, our target group consists of two of those segments combined - Dependability/Protection and Environment (Figure 3). These two groups are willing to consistently pay more for gasoline. Together they make up 22 percent of the total sample.

Figure 3

When combined and tallied, our group is willing to pay, on average, 1¢ per liter, or 4¢ more per gallon. Given the size of the gasoline market, that is a sizable chunk and a target for aggressive communication. We will call these people the Payers.

Who are the Payers?

A run across demographics gives us an idea of who these people are. The target group is better educated, has higher income, and not surprisingly, has a significantly higher usage of premium gasoline. This bodes well for the communication effort, as many of these Payers will not have to be persuaded to move to a higher priced gasoline. Also, more than half (54 percent) already use the client’s gasoline. Again, it is easier to retain a customer than to attract a new one.

Figure 4


Figure 5


Figure 6

Most importantly, the investigation into what they want from a gasoline yields the clues on how to speak to these people.

The Payers are for the most part responsible, busy people, who buy well-known brands. What most distinguishes them from the rest of the pack is that they will pay more for quality and are willing to try exciting, new things.

Payers are confident drivers who cannot live without their cars. They maintain their automobiles well, are willing to pay more (we know this), and believe that there is a difference in brand quality.

Perhaps our client should consider positioning its premium gas as the quality, new choice for serious drivers.

Speaking to the Payers: gauging which gasoline uses drive purchase intent

The importance of brand image lies in the fact that consumers’ response to the brand revolves around it. This makes the concept an essential input into marketing strategy since a positive, strong brand image will lead presumably to higher sales. There are a number of approaches that would be useful to the client’s brand managers.

We know that the Payers are willing to pay, on average, 4¢ more a gallon. We know who they are and, on balance, how they view themselves and their cars. The next question is, what characteristics of a gasoline brand will raise purchase intent?

Key association analysis measures the strength of descriptive attributes or performance ratings in relation to a strategic characteristic. The strategic characteristic could be determined by asking, “How likely are you to purchase [the client’s] gasoline?,” or gauging overall satisfaction with the client brand of gasoline.

Basically, in the survey, respondents were asked how many times in the next five fill-ups they intended to use the client’s gasoline. Then they were asked a series of questions about the client’s brand of gasoline (as well as performance ratings of key competitors).

Using the “next five purchases” as the dependent variable, and the client’s brand performance ratings as the independent variables, we run a key association regression (Figure 7) to see which performance ratings are driving purchase intent. In marketing terms, this is brand image and is also known as brand equity. An effective communication effort is based on these attributes.

Figure 7

The client brand attributes shown in the key association map are those that are significant in the regression (i.e., they influence purchase intent). Proximity to the top bubble gives their order of importance.

When designing the final brand communication strategy, all of the above come into play. In the brainstorming sessions to design the campaign to bring in the Payers, the chart below should be shown and considered.

Chart 1

Looking for an edge

These days brand managers are looking for an edge. They are turning more and more to the marriage of good research and advertising know-how to get there.

The above mix of methods - cluster analysis, simple top box rankings, and regression - is the process by which value is added to the client/agency relationship, one that fuses solid research with advertising savvy. The strategic focus of any advertiser’s effort on behalf of a client is to intensify the relationship of its loyal customers to their premium products. However, the process of segmentation, data mining and uncovering existing brand equities may reveal other opportunities that represent a significant volume increase for the client.