Glossary

Brand fit measures the fit of a brand’s profile of product qualities with the desirability of these qualities within a segment. For example, if a brand performs well on criteria that are important to a particular segment, then that brand will have a good fit with the segment. A very helpful analysis for optimising brand positioning.

Brand Price Trade Off is a technique used for establishing brand and price preferences. Respondents are presented with a set of branded products priced at the lowest possible price point for each of the brands in question. The respondent is asked to select which products they’d choose. The price of the product selected is increased to the next level, and they are then asked again which product they’d choose. This process is repeated until a product reaches its maximum price and is still selected. Competitive demand curves can be created as well as specific price scenario testing.

A statistical technique used to identify how much one set of independent variables (e.g. Age, Gender, Social Class) drives another set of dependent variables (e.g. Snack Choice). It is particularly useful when there are multiple dependent variables or the variables are categorical (e.g. Age, Gender etc.). It can also be used within segmentation, for example to segment on the relationship between attitudes and behaviours.

Choice-Based Conjoint is a specific type of conjoint analysis where respondents are asked to make a choice between different sets of products/services, to derive the overall appeal of each component part. This is given either as a discrete choice, or as a chip-allocation style response (e.g. number out of 10 next purchases allocated to each product/service).

CHAID is a type of decision tree technique, based upon significance testing. It can be used to create rules to classify future respondents into identified groups using a number of different questions, or detecting interrelationships between different questions. For example, the combination of age and gender helps to further explain the different types of snacks that people consume.

Cluster analysis is the statistical term for the creation of segments – the process of dividing markets into groups that are similar to each other, but different to the other groups. There are a number of different ways to segment, the two most common being: Consumer Segmentations – used to understand which consumers to target and service with distinct marketing propositions, or to tailor brands, products, pricing, communication to specific groups and make more effective use of marketing resource.

Occasion-Based Segmentations – used to understand Needs on different occasions (e.g. Had a coffee to wake up in the morning, or had a social coffee with friends after work) in order to help with new product development in repertoire markets, or for brand positioning for clients with multiple brands.

An abbreviation of “consider jointly”, Conjoint Analysis is a powerful statistical technique to understand what combination of a limited number of attributes or features is most influential in the consumer’s decision-making process Conjoint Analysis is a multivariate technique based on the idea that when choosing a product or service, people will trade off features simultaneously.

This process is replicated within a respondent exercise – a number of scenarios are shown where respondents are asked to express choice or preference amongst a set of products/services comprising of different combinations of attributes levels. A model is created from the resulting data to simulate “what if” scenarios and to assess to impact of each attribute in the decision-making process. Conjoint analysis is often used in concept testing studies, and pricing research.

Discriminant analysis uses the responses to a set of questions (e.g. attitudes) to predict existing group membership (e.g. segments). The output can then be used to classify future respondents into the same groups using their responses to the same set of questions. Similar to CHAID, but used more frequently when the independent variables are scales.

Factor Analysis is a statistical technique to examine the similarities between items in order to identify a more concise summary of themes. For example, from a list of 20 statements on car imagery, we may identify a factor on reliability, design, performance, environment and image. This can be useful for ordering statements in a presentation so that similar statements are presented together, or for data reduction purposes e.g. to focus a segmentation or to take account of bias in questionnaire design that included too many statements from one particular theme.

Gabor Granger is a pricing technique used to understand price elasticity for set products. Respondents are asked how likely they are to purchase a product at a number of different price points. The purchase intention measures can be converted to estimated take-up scores and plotted to establish which price point is most suitable. The technique can be extended to present products within a competitive context.

Impact indices measure the impact any independent variable has on changing a dependent variable. Often used when the independent variables are binary (e.g. Yes/No), it can be used for example to evaluate the impact of different product qualities on preference for the product.

The analysis of the relationship between a dependent variable (e.g. brand strength) and one or more independent variables (e.g. brand imagery statements). Its purpose is to determine whether a relationship exists and the strength of the relationship, and used to help prioritise what to focus on. There are many different statistical techniques that fall under this term, from correlations to Structural Equation Modelling. Different techniques can be applied depending on the objectives, the type of data and how the results will be used.

A type of Key Driver Analysis, Kruskal’s relative importance analysis is as an alternative to other techniques such as ordinary regression analysis, which can give misleading results when there is missing data, or when variables are strongly related to each other (which is typical of research data). For example, the influence of brand imagery items onto brand appeal.

Logistic regression is used to find out the relative importance of different drivers in order to re-create a dependent variable when the dependent variable is binary (e.g. Yes/no or Buy/not buy). It is used when the usual linear regression cannot be used, and is particularly useful in propensity modelling.

MaxDiff is a technique used to understand relative importance or appeal amongst a list of features/statements. Respondents are asked to compare sets of typically 4-5 features or statements stating which of these are the most and least appealing/important to them. The results give a % appeal/importance score for each item, and can be used to identify which are most popular. Often used to force differentiation, when simple scales may not work as well (e.g. consumers thinking all attributes are important).

Menu-based conjoint is a specific type of conjoint analysis able to handle a variety of menu choice situations in which respondents make from one to multiple choices in the process of building their preferred selection. An example situation to which this would apply would be a fast-food restaurant where it’s possible to choose something from the ‘fixed-menu’ section, with some personalisation e.g. choice of side dish – or instead purchasing a series of single items.

Price Sensitivity Measurement (PSM) is a technique used to understand price preferences. Respondents are asked at what price they would consider a product to be: too expensive for them to consider it too cheap so they would start to doubt its quality expensive but would still consider buying it a bargain or good value for money The data from these questions is plotted across the sample to see where the cumulative frequencies intersect. For example the optimal price point is determined to be where an equal proportion of respondents have said “too cheap” and “too expensive”.

The process of dividing markets into groups with people or occasions that are similar to each other, but different to the other groups. There are a number of different ways to segment. The two most common are: Consumer Segmentations – used to understand which consumers to target and service with distinct marketing propositions, or to tailor brands, products, pricing, communication to specific groups and make more effective use of marketing resource. Occasion-Based Segmentations – used to understand Needs on different occasions (e.g. Had a coffee to wake up in the morning, or had a social coffee with friends after work) in order to help with new product development in repertoire markets, or for brand positioning for clients with multiple brands.

Structural Equation Modelling (SEM) is a statistical technique for testing and estimating causal relationships, using a combination of statistical data and qualitative causal assumptions. Factor analysis, path analysis and regression all represent techniques used within SEM, which also allows the construction of variables which are not measured directly. As an example, it can be used to model and understand the relationship between different aspects of customer satisfaction and how these explain customer loyalty.

TURF stands for Total Unduplicated Reach and Frequency, and is used for providing estimates of media or market potential and devising optimal communication and placement strategies. If we take appeal of ice cream flavours as an example, TURF analysis can identify the number of users reached by the combination of ice cream flavours in a client’s range, as well as how frequently they will be consumed – very useful for deciding which flavours of ice cream to have in a range.