Linkage Analysis: Data's Hidden Stories

In the age of big data there never seems to be a shortage of stats and figures. Linkage analysis combines survey data with a client’s in-house statistics to create richer insights.

Linkage Analysis: Data's Hidden Stories

The author(s)

  • Leo Cremonezi Ipsos Connect, UK
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Linkage analysis combines data sets to extract more tangible and actionable insights. Using the example of fast food restaurants this thought piece investigates how the process works and what the key benefits of the technique are. By translating data into stories, linkage modelling can help interpret complex data sets and allow clients to make the most of their data.

 

The Stages:

  • Meet with our clients to discuss the scope of the research and identify the fundamental research objectives
  • Combine the sets
  • Conduct a preliminary analysis of the data using standard statistical techniques
  • Identify strong links and correlations between factors. Strong correlations are those that strongly affect one another, and are used for predictive analysis
  • Predictive modelling is used to add a value to this relationship, and a statistical model is built for each relationship found. Quantifying these relationships allows us to find the optimum moment between factors to provide effective results

 

Key Benefits:

  • Every data set has a hidden story, and rather than delivering raw data that provides few tangible insights, linkage analysis allows us to create a narrative from the data
  • We interpret the data to deliver tailored and actionable insights to our clients
  • Predictive modelling allows us to find the optimum moment between factors
  • These statistical processes let our clients focus on the areas that have the greatest impact on business performance, and ultimately offer the best return on investment

The author(s)

  • Leo Cremonezi Ipsos Connect, UK

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