Commonly measured marketing outcomes such as customer satisfaction, value for money, advocacy, and commitment are driven by the performance of a number of attributes or hypothesised drivers. Given limited resources, businesses would prefer to allocate resources to improving the performance on those attributes that are considered most important by the customer. But how best do we ascertain this hierarchy of importance?

The collective term used for most of the common analysis tools used in research today is 'multivariate analysis.' So, what does the term include and what is left that is not multivariate?

Tired of being told again that you shouldn't be calculating averages from your Likert scale data? Well, here's your answer.

Earlier this year, Forethought was awarded second place in the globally prestigious ISMS-MSI Practice Prize for its Prophecy® research for Jetstar.

Rarely a month goes by that I don't receive an email for a new or 'improved' piece of statistical software. Many of these software allow for a 'check box' approach to analysis whereby the user need only check a box to generate output. For those trained in statistical analysis, thoughtful uncertainty is the modus operandi in modelling. This is because judgment so often figures predominantly in the solution and checking boxes must be complemented with judgment.

Sample bias all too often rears its ugly head, although usually well disguised. There are many issues to contend with when considering the veracity of a sample. All of these methodologies have at their core the primary objective of ensuring that the sample is representative of the population which it is attempting to estimate, and from which it was drawn.

Modelling the drivers of anything is a costly process. What clients want from marketing models is a clean, hierarchical list of mutually exclusive drivers. Instead, invariably what they receive is a list of drivers suffering from some degree of multi-collinearity. Multi-collinearity becomes a problem when the predictor variables are so highly correlated that it becomes challenging to distinguish their individual explanatory power.

The use of structural equation modelling (SEM) has grown steadily in the market research arena since it first appeared in papers in the late 1960s. Described as a second generation technique, SEM relegates the long standing ubiquitous traditional techniques such as ANOVA, MANOVA, linear regression etc. to 'first generation'. Does this mean that SEM, the 'new kid on the block' has brought with it the beginning of the demise of the loyal, first generation techniques, specifically regression analysis?

The pharmaceutical industry is governed by rules and regulations often not present in other product and service industries. Generally for prescription only drugs, consumers have decreasing input into the decision of which brand is supplied. In light of this, the question becomes, 'how can quantitative marketing research assist pharmaceutical marketers'?

Pivot tables are a powerful tool with virtually no barriers to entry other than perhaps the lack of awareness of the potential users.

The Net Promoter Score (NPS) might be fine for executives to demand but for managers it fails to provide sufficient diagnostics to be meaningful.

Modelling in market research has become a staple in the budget allocation of many large corporations. What began with customer satisfaction modelling has been extended to customer value analysis (CVA) where satisfaction modelling is just one component. But how does this improved customer value impact on the company's performance? What is the predicted change in market share that can be reasonably expected to result from improving customer value? And most importantly, what is the financial return to the company for effecting such an improvement in customer value?

In marketing measurement there appears to be a steady drift away from a customer orientation towards a market orientation such as Customer Value Analysis. From a data collection perspective, CVA requires the researcher to capture the views of the customers and non-customers of the competing brands proportionally to the incidence of each brand's market share. However, if a respondent has no view whatsoever of a brand, is the perception rating just noise?

On average you are not OK when your head is in the oven and your feet are in the fridge. But management's fixation on averages or point estimates to approximate performance is equally ambiguous.

Marketing researchers are an increasingly capable group. Perhaps in our rush to apply the sophisticated techniques we have forgotten some of the tried and true tools? Here is an application of a simple spreadsheet tool: the boxplot.