Enhancing drivers of choice studies

Identifying and understanding how consumers choose the brands and products they do is something we frequently look at within market research. Which methodology we use to arrive at drivers of brand and product choice can vary. Many researchers adopt a more simplistic direct line of questioning to explore these issues, but a more subtle, statistically modelled approach is often more reflective of the subconscious decision-making processes consumers exhibit.

When a statistical approach is used, consumers are typically asked to rate brands across a range of factors, and these are then regressed against a dependent variable such as likelihood to shop or satisfaction in order to “uncover” the drivers of brand choice.

However, whilst this approach is fairly commonplace, formulating an analysis plan which takes a slightly more considered view of the forces impacting on consumer behaviour can result in different recommendations which are more impactful for the business.

Below we’ve highlighted a few areas of best practice which should have been given some thought in order to ensure that your approach to identifying the drivers of choice yields the correct results for guiding decision making:

Choosing the correct variable to measure consumer choice

Some studies incorrectly focus on variables such as satisfaction, or likelihood to purchase, however, these models are unlikely to highlight the factors which cause a consumer to choose your brand.

Focusing on variables such as 1st choice brand, most used brand and share of wallet are more closely aligned to the actions which we are trying to encourage.

Framing the exercise in the appropriate context

The answer you get from your study will heavily depend on the context of the questions you set for your respondents. For example, if you are trying to understand the differences between top-up and main shop missions across grocery stores you need to ensure that respondents are primed to answer any questions within those specific contexts.

You may be surprised by how much difference the correct framing will make to your end results.

Only asking respondents to evaluate brands they recognize and consider

Consumers do not typically make decisions on where to shop across the entire market (although some price comparison website behaviours are changing this), but are instead likely to make their decisions based on a predefined consideration set of brands.

As consumers tend to make their choice from within this set, we should limit questions to the brands they actually care about and would be likely to choose.

Understanding that decisions are made at a relative, not absolute level

In tandem with the point above, consumers aren’t likely to make decisions at an absolute level – “How does this brand stack up in isolation”, but rather at a relative level -“How does this brand compare relative to my consideration set?”.

This might seem like an obvious point but traditional modeling approaches typically assign importance based on the absolute scale. By transforming the data so that scores are based on the relative performance of a respondent’s consideration set, we can account for this more accurately.

As a final example, we recently worked with a retail client who had determined that their returns policy wasn’t an important driver of choice. We reviewed their modelling approach and identified that the relative competitor performance wasn’t being factored into the design. We re-ran the modelling and found that the returns policy was a top tier driver because it was a major point of differentiation for a few key players.

This point had been entirely missed the first time round and had undoubtedly been a missed opportunity for the business.