Your data is only as good as the quality of your analysis. Lab42 provides additional analytics to help you gain more valuable insights from your data and make sound business decisions. These additional analytics are designed to help you at different stages in the product lifecycle – from product development to tracking performance in market.

Lab4 specializes in the following analysis techniques:

  1. TURF Analysis
  2. Van Westendorp Price Sensitivity
  3. Derived Importance / Quadrant Maps
  4. Factor / Regression Analysis


What it is: The TURF Analysis is a technique that allows us to assess the ideal number of product attributes which will attract the most unique (unduplicated) customers.

When to use it:

  • Launching new products/concepts
  • Introducing new features
  • Estimating market share or potential


What it is: The Van Westendorp price sensitivity analysis is a technique that provides guidance on what price range consumers perceive a product should fall into so it is not perceived as too inexpensive or too expensive. It also indicates the optimal price point that can be used as a reference when determining the product/service price.

When to use it:

  • Determining the optimal price range of new product/service

Derived Importance/Priorities Quandrant Map & Factor/Regression

Brands are most successful when they offer a product or service that is unique and meets a consumer need.  In order to develop this point of difference, you need to understand what you stand for in order to develop and own that area.  That area of differentiation can be defined by many attributes, both functional and emotional.  

  • How does a brand determine which of these attributes are the most important and therefore should be prioritized?
  • What levers need to move in order to achieve greater success in that area?

Derived Importance and Factor/Regression analysis can help you gain further understanding and insights to answer those questions.


What it is: A simple analysis technique that provides associations between different attributes and a Key Performance Indicator (KPI).

When to use it:

  • Determining which attributes set brand/product/service apart from competitors
  • Determining which attributes are important weaknesses and should be prioritized.


What it is: A more advanced analysis technique to help you understand how to prioritize resources by identifying areas that are likely to be more impactful.

The difference between Factor/Regression analysis and Derived Importance (outlined above) is that Derived Importance simply shows an association (correlation) of different attributes (variables) and does not take into consideration that some attributes may mean the same thing to consumers (are highly correlated).

On the other hand with Factor/Regression analysis, Factor first groups similar attributes into categories (areas). Regression analysis then quantifies the strength or impact that these categories are likely to have on a KPI or other metrics.

When to use it:

  • To understand which area is more important to driving a specific KPI in order to prioritize resources and focus strategy.

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