Ramblings on brand loyalty: Repertoire thinking in light of Byron Sharp’s text How Brands Grow.

For a number of years we have been of the opinion that it makes more sense to think about brand usage in terms of repertoires rather than single brand loyalty.

To that end we developed a probability of purchase model based on repertoire thinking some years back, using some simple questions related to buying behaviour.

A lot of the thinking flies in the face of current marketing dogma which suggest targeting choice segments and trundling consumers down a brand funnel to the holy grail of a band of committed brand loyalists.

brandshare Fishburn brand loyalists


So it was with great interest I read “How Brands Grow” by an Australian academic Byron Sharp who seemed to echo our thinking, and then some. (You can read a review of this excellent text on an earlier blog post I wrote.)

I must warn you that if you are a fan of attitudinal segmentation models, loyalty programs, Kotlerian thinking, and a few others besides, you are in for a rude shock. He equates much current marketing to the medieval medical practice of blood letting.

One aspect he takes issue with is the idea that brands should focus their efforts on building loyalty. Instead of trying to move consumers down a brand purchase funnel from awareness, to consideration, usage, repeat, and arriving at loyal; there is more to be gained by focusing on increasing penetration into as many repertoires as possible.

Sharp paints a world of cognitive misers who are polygamously loyal to a small repertoire of brands (within a given category). Your brand is competing for a tiny slice of attention, against other brands that are to all intents and purposes near lookalikes.

To cope with the large amount of brands they are exposed on something relatively unimportant, consumers are forced to ‘satisfice’. In other words they do not seek to maximize the best choice but opt for what is good enough. In doing this, consumers (often subconsciously) screen out most brands, leaving a small repertoire to which they are behaviourally loyal. The most critical juncture is to get into the repertoire, instead of being (subconsciously) screened out.

Once you are in the consideration set you have a chance of being considered. Within this subset switching is normal, and should not be confused with a change in behaviour, which some mistake for conversion from one brand to another.


We can take a number of points out of this.

1. People have personal repertoires in the category, and they vary from person to person, but they are generally quite small.

2. If one accepts that the typical consumer does in fact have a repertoire, then it makes far more sense to think and measure repertoires than single brand loyalty

3. Market penetration into the repertoire is critical to brand growth. Sharp notes that while it is theoretically possible for a smaller brand to have more regular customers than a bigger brand, giving them an equal market share as illustrated in the table below, it never happens in the real world. Loyalty doesn’t vary much, but what little variation there is goes to the bigger brand. His Double Jeopardy law states that brands with less market share have less buyers who are slightly less loyal.

4. We tend to buy in a category much less that marketers realize. In fact most of a brands customers are very light buyers. ). Sharp notes that Pareto’s ratio is more like 60/20 rather than 80/20. This means your occasional buyer is more important than you realize. Occasional buyers will tend to default to the leading brand, which Sharp calls the natural monopoly law.

When it comes to surveys the light buyer can cause anomalies, because there may be only one purchase in the survey period. This makes it look like they are a brand loyal, when they just happened to buy once. In addition there is natural variation in purchase patterns which means light buyers may become heavy buyers in the next period, and vice versa. He calls it the law of buyer moderation, and it is essentially a restatement of regression to the mean.

These points all fit in with our repertoire model, which estimates the probability of purchase for all brands within a category. We accept it is not perfect, but feel that it is a step forward in the right direction. The thinking is in line with the work of Professor Byron Sharp, who has produced a lot of convincing evidence to back up his ideas.

The model works by analyzing data in a specified period in a single category on most often or preferred brand, other brands bought, and other brands the respondent would consider. The data is then used to build a matrix of each person’s purchasing repertoire. For each respondent a probability of purchase is assigned to each brand (ranging from 0-100).


The analysis yields the following information:

  1. The repertoire size. From the markets we have covered, the average repertoire is surprisingly small, and the number of solely loyal respondents is smaller than most people expect.
  2. The distribution of probability of purchase by brand, which should give a good indication of market share.
  3. The depth and strength of penetration of each brand into the market. One can analyse the market data from the perspective of each brand.
  4. A set of brand weights. This is a slightly technical point. In most research tables, every respondent is treated the same, irrespective of how much affinity they have to your brand. (This means a person who buys your brand and wont even consider anything else, is counted the same as a person who has bought your brand and 2 others). From a survey analysis perspective we can reweight your entire survey according to brand weights.
  5. All this data can be run at any regional or demographic level, for example the repertoire mix and penetration by region may be dependent on physical availability and will clearly show up.

The chances are that you already have this data, as the questions are fairly standard in brand usage and awareness studies.

We’d be interested to know what you think of the model and how it fits with the thinking of Prof. Sharp. We are also happy to run through any old data you may have to show you how it works.

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