Market research is an essential activity for every business and helps you to identify and analyse market demand, market size, market trends and the strength of your competition. It also enables you to assess the viability of a potential product or service before taking it to market. It is a field that recognises the importance of utilising data to make evidence based decisions and many statistical and analytical methods have become popular in the field of quantitative market research.
In our Market Research terminology blog series, we discuss a number of common terms used in market research analysis and explain what they are used for and how they relate to established statistical techniques. Here we discuss “key driver analysis”, but take a look at our other posts on MaxDiff, Customer Segmentation and CHAID, and look out for new articles on TURF and Brand Mapping, amongst others, coming soon.
What is it for?
It’s important to identify and understand the drivers of key business outcomes, such as customer satisfaction or loyalty, in order to improve processes and maximise performance and profitability. You might want to understand, for example, which aspects of your service influence how likely a customer will be to recommend you to others. A so called key driver analysis can be used to address this sort of question.
A key driver analysis investigates the relationships between potential drivers and customer behavior such as the likelihood of a positive recommendation, overall satisfaction, or propensity to buy a product. This is often using data collected from a questionnaire, which might ask for a customer’s demographics, their level of satisfaction with various aspects of your company’s services (e.g., whether it was value for money, or whether the customer services department was helpful) as well as their likelihood of recommending your company to others (see below).
Correlations between the scores for the customer behaviour of interest (likelihood of recommendation) versus those for the potential drivers may then be calculated to see whether there is evidence of a relationship between them. If there is a positive correlation between satisfaction with the customer services department and the likelihood in recommending the company to others, for example, then satisfaction with customer services is said to drive recommendations in a positive direction. Drivers can also be associated with customer behaviour changing in a negative direction.
What statistical techniques are used?
A key driver analysis is often performed using multiple linear regression to model the primary outcome as a linear combination of the potential drivers. Those drivers that are found to have a statistically significant effect are considered to be key drivers of the outcome and their model coefficients can be interpreted to understand the direction and strength of the relationships between the drivers and the outcome variable.
A key driver analysis can help you to understand what drives customer behaviour.
By including all of the potential drivers in one model, we can see which make up the most informative combination of drivers for the outcome. The model may also be used to make “What If?” predictions of the outcome for customers with specific values of each of the drivers (these may include the gender and age-group of a customer, for example).
Where there are linear relationships (correlations) between two or more of the potential drivers, this can lead to difficulty in the interpretation of the model coefficients – so called multicollinearity. This can occur where two of the potential drivers are capturing similar information, for example, a questionnaire might ask whether the staff were friendly, and also whether they were helpful, which we would expect to be highly related.
There are various statistical approaches that can be used to deal with multicollienarity, including the use of principal component analysis to reduce the number of potential drivers to a set of linearly uncorrelated variables. These analyses that take account of multicollinearity are often called ‘true driver analyses’. It is important to note however, that it is only possible to establish an association between each driver and the outcome with a correlation or regression analysis, it is not possible to establish causation.
With a ‘key driver analysis’, statistical modelling can be used to quantify the relationships between multiple variables. This can help you to understand what drives customer behaviour and ultimately how to improve your performance.