Conjoint analysis
In order to create new products and services you need to know your individual customer’s needs and preferences. You need to understand what features the new product should have and how it should be priced. Essentially, you need to know how likely it is that they will buy your product.

Conjoint Analysis is a special type of statistical analysis which is used to measure the perceived value of specific product features and to forecast the likelihood of buying/ using the product. It determines how people value the different features that make up an individual product or service.

There are various methods of Conjoint Analysis available to you and Digitab can help you to identify the appropriate approach for your needs and guide you through the research, however complex.

Conjoint methodologies available from Digitab include:

  • Choice based conjoint/ Discrete choice modelling
  • MaxDiff
  • K&S analysis
  • SIMALTO
  • Brand Price Trade-Off/ Essential Rank analysis 

Key Driver Analysis (KDA)
One of the main objectives of market research is the measurement of customer satisfaction. The technique  most often employed is regression analysis (often referred to as a Key Driver Analysis). The objective of the analysis is to determine which components of overall satisfaction are the most important.

Digitab offers a wide range of methodologies to suit your needs. These include the more familiar analysis methods –  linear/non-linear or logistic regression and more recently developed techniques including:

  • Shapley Value Regression

This is a relatively new technique, which Digitab has developed into an analytical tool for examining how independent variables (components of overall satisfaction) overlap with each other and how this affects the dependent variable. This tool overcomes the problem of multi-collinearity in regression analysis. It also has the advantage of producing easily understood results, i.e. the contribution of each independent variable as a percentage of the whole (out of 100%).

Shapley Value Advantages
Shapley Value Regression overcomes the problem of multicollinearity (where individual predictors are highly correlated), which can create erratic results. Shapley Value shares the overlap between competing variables into the net effects and overcomes suppression or inflation.

Shapley Value Regression results are easy to understand. This method is great for delivering insight as they show how the dependent variable (R2) is explained by the combination of all the independent variables. The size of the contribution of each independent variable is shown as a percentage of the whole (net effects), i.e. out of 100%; with no illogical negative values and with the effects of suppression accounted for.

Shapley Value Regression is more stable than other regression methods when measuring effects over multiple waves.

Shapley Value Background
The Shapley Value method was developed by Lloyd Shapley within the context of ‘game theory’ in order to evaluate the worthiness of individual players in a co-operative game (utility of the player). Shapley Value gives an indication of the prospects of winning when co-operating with another player. The higher the Shapley Value, the better their prospects.

  • Latent Class Regression

Latent class regression combines the two analysis objectives: KDA and segmentation (see below), into one step. It fits regression equations to segments exhibiting similar response patterns.

  • TABOO

Targeted Bootstrapping (TABOO) is a simulation technique that measures the effect of changes in one or more variables on a dependent variable. Unlike ‘conventional’ statistical methods, TABOO does not define a theoretical model but works only with the data, by selectively re-sampling the data many times.

  • Structural Equation Modelling (SEM)

SEM is a complex model, merging regression and factor analysis. It’s a dedicated method to confirm or build a statistical model based on a theoretical model.

Segmentation
As a consequence of the increasing diversity of customer needs and behaviour and the variety of products on the market, segmentation has become one of the most popular statistical analyses.

By dividing the relevant market into smaller parts that are internally more homogeneous and externally heterogeneous it is easier to develop products which more closely meet individual consumer needs and to create appropriate promotional campaigns to reach each target group. Furthermore it is also possible to identify groups of similar products which give companies information about the competitive products closest to their own product.

Depending on the researcher’s objectives, the following statistical methods can be applied by Digitab:

  • Latent Class Analysis (LCA) – for any combination of nominal, ordinal and continuous variables (one level or multi-level/ hierarchical)
  • Classification trees – AID/CHAID/CART – segmentation and identification of predictors, especially useful in exploratory analysis
  • Hybrid methods – combining features of classification trees and LCA
  • K-means – traditional non-hierarchical clustering
  • Linkage clustering – a hierarchical clustering method
  • Discriminant analysis – confirmatory method
  • Conjoint and mapping techniques – relates products to attributes

Perceptual and Preference Maps
Once the researcher has decided which customer groups within which market segments to target, he has to determine how to present the product to this target audience. Positioning shows how the brands/ products/ services are perceived by customers.

There is a wide range of mapping techniques which Digitab can offer to present customers’ perception including:

  • Correspondence Analysis and Multidimensional Scaling
  • Principal Component Analysis/ Factor Analysis

Launching a new product
In order to develop new products and services which will be competitive in the company’s relevant markets it is necessary to know individual customer needs and preferences. Therefore it is essential to first identify which attributes of a specific product consumers will prefer and then the likelihood that consumers will buy the product. Conjoint Analysis may be used to measure perceived values of specific product features and to forecast what the likely acceptance of a product will be.

Pricing Research
One of the prime determinants of a product’s success in the market place is clearly its price. The analysis of price is a key area in market research.

Digitab has a wealth of experience in this area which includes the following techniques:

  • Brand Price Trade Off
  • Gabor Granger
  • Van Westendorp
  • Price Elasticity of Demand

Other areas of research

  • Data mining (Neural Networks, Genetic Algorithms)
  • Simulation models
  • Time series and forecasting models