Reducing two problems to one solution
Metrics are hot. Multi-collinearity is not!
There are multiple metrics for measuring the health of the brand and/or company, but simple interpretations seem to be sparse.
Meanwhile, multi-collinearity is generally identified as a statistical problem in which two or more predictor variables are correlated with one another which muddies the interpretations of what variables predict important outcomes such as purchase intentions.
However, if the two are put together, the world of marketing can become very much simpler and clearer.
The existence of multi-collinearity actually makes the world a simpler place in a practical sense, and by understanding the simplifying properties of multi-collinearity, we can simplify the metrics that we use to measure things such as brand-health.
Problem of Multiple Metrics
Market researchers have built their empire on multiple metrics; call them measures or items or questions if you prefer. And modern marketing is encouraging this through the pursuit of ‘metrics’ such as the Balanced Score Card, Brand Metrics, Net Promoter Score, etc.
And in turn, each of these metrics is made up of many apparently independent measures. For instance, most brand metrics will include one or more measures of dimensions such as awareness, familiarity, liking, attribute-rating, attribute-importance, attitude, satisfaction, purchase intentions, etc.
The notion is that what is measured is managed, and so if we are measuring something, then we are on the way to managing it.
The output from studies is multiple metrics, multiple charts, multiple tables, multiple pages, and multiple options but few clear solutions.
Problem of Multi-collinearity
For most researchers, multi-collinearity is a disaster. More disturbingly, for some researchers, it is a bit of a mystery.
Multi-collinearity is simply the way in which two or more predictor variables in a multiple regression are related to one another. For instance, many companies are keen to know what ‘drives’ the customers' decision making. In predicting what drives doctors' prescribing behaviors, three of the attributes that are often included for consideration are ‘efficacy,’ ‘side-effect profile’ and ‘drug interactions.’ It is not uncommon to find that all three attributes are positively correlated to prescribing intentions.
A simplistic interpretation is that each attribute is an independent driver of prescribing. However, if a regression is run, it may be found that only one attribute predicts intentions, the weights for the other two being not significantly different from zero. This is a clue that multi-collinearity maybe in operation.
Multi-collinearity can be seen if intercorrelations of the independent variables are examined. If two or more are significantly intercorrelated, we probably have multi-collinearity issues.
More simply, if three attributes such as efficacy, side-effect profile and drug interactions are correlated with one another, and only one of them is significant in a multiple regression model predicting prescribing, then we can conclude that there is really probably only one dimension underlying all three attributes which is driving prescribing intentions.
Multiple metrics sound great, but too many make simple interpretation difficult. Most market research studies are built based on multiple metrics (or more simply, measures or items or questions). How often do we misrepresent the results by reporting the many correlations between the predictor variables and a dependent variable such prescribing – without allowing for the possibility that the various predictor variables are correlated or simply multiple restatements of one underlying relationship?
What is the solution? Well, first, we can examine the relationships between the key metrics.
Then we have two options for handling metrics that are correlated with one another. One solution is to retain just one of the multiple correlated items. At a more practical level, if we can anticipate ahead of time that two (or more) metrics are going to be highly correlated, we can consider measuring just one or the other.
Another solution is one that combines the separate metrics into a single index as this provides a more stable or reliable estimate. That is, we use multiple measures, even if they are likely to be correlated, and then combine items that are correlated with one another into one scale.
If we want to be really sophisticated, we could conduct a factor analysis for guiding the combination of items. This allows for a sophisticated weighting of each variable in the final ‘scale.’ However, in my experience, clients (and analysts) find simple, averaged scales much easier to interpret than factor scores.
For some, multi-collinearity is understood to be a problem, and something we would prefer to avoid. However, multi-collinearity is not a problem, it is reality. Multi-collinearity tells us that in many senses the world is a simpler place than we originally thought – and when business is as complicated as it is today, this is a very good thing.
My own exploration in this area came from the initially disturbing finding in examining intercorrelations among many of the ratings of brands on various attributes. Even among sophisticated respondents such as doctors and specialists, the ratings they give to a specific drug in terms of potency, efficacy, side-effect profile, drug-interactions, cost and value are all likely to be correlated. More broadly, on many research projects, I have found that many of the intangible qualities of the brand that we might measure (brand awareness, attribute ratings, overall evaluations, satisfaction, usage, etc.) are all highly correlated.
Many researchers appear to be unaware of or unwilling to acknowledge such correlations, and will happily make recommendations to tweak a particular quality of the brand in order to improve overall image, satisfaction, purchase intentions, etc. However, if all these measures are so highly correlated, advice to tweak one or the other is at best rather meaningless and at worst, rather misleading.
However, to offset the bad news, there is some good news. The good news is that the intercorrelations between the many predictor variables means that we do not need to consider a screed of so-called independent measures to assess the health of a brand. Some clients have had me explore various brand metrics, and what I find is that often-times, we need only look at relatively few numbers rather than many to assess the health of our brand! Why? Because of multi-collinearity. The independent measures are often so highly correlated that they can all be combined into relatively few scales which capture most of the important intangibles.
For instance, in research I have conducted in both pharmaceutical and agricultural domains, I have found that we can reduce many of the measures of the intangibles down to three dimensions that operate as very strong predictors of brand purchase.
Of course, you might like to know what those dimensions are, right? Unfortunately, that would be telling! Nevertheless, I have given you the key to simplifying the intangible assets of the brand. You can work it out.