Our in-house Advanced Analytics team is always on hand to support the design and analysis of any quantitative research project we conduct.
Overseen by Dr David Pearmain (whose PhD is in stated preference methods), their expertise will help you to discover the meaning in your data.
More about the techniques we use
We use several techniques to achieve this, including k-means, latent class analysis, normative methods, hierarchical segmentation, and trees.
To help you action your segmentation, we create a profile of each segment, and can usually append the segments to your database.
The results allow you to target impovement on the drivers which will have the greatest effect.
The techniques we use include correlation, linear/ridge/logistic regression, Kruskal’s analysis of relative importance, and Shapley values.
It can be used for a wide range of purposes including understanding the appeal for different package options, guiding new product development and portfolio management, and predicting whether customers would pay more if more features were added.
These include sophisticated simulators based on conjoint and Discrete Choice Model utility data which can help you to understand the market and feed into your decision-making about the marketing mix portfolio for a brand/product.
They show sensitivity to price, brand loyalty and switching behaviour, and answer questions such as, 'Do customers stick with the same brand?', 'At what price point do they switch?', and 'To what do they switch?'
It's easy to administer and it really comes into its own when there is a large set of attributes (too complex for respondents to do) or if the attributes are very wordy.
This technique is ideal whenever there is a lot of choice, and is most commonly used to help manufacturers decide on flavours to launch. It also enables ‘Basket Analysis’.
They tend to stimulate discussion and strategic thinking, and are often popular with senior management.
It is commonly used in public sector investment studies, including as part of a ‘Cost Benefit Analysis’ (CBA) to inform a business plan.
It's a quick and affordable way to identify the price landscape although it doesn't accommodate competition, assumes respondents know the market, and does not directly measure likelihood to buy.
Contingency Valuation is a variation on Gabor Granger. It produces a similar price curve, but expresses the average amount respondents are willing to pay in terms of an upper and lower boundary rather than a single value.
This validates the insight that are gained but also increases the breadth of understanding.
Social media data can be used to understand changes in the market, changes in sales and can be used to drive sales and/or brand perception.
Bespoke SPSS training courses and 1-1 tuition are also available.