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Key Drivers Analysis

Consumer Experience
Brand Insights

Go beyond simply measuring self-reported importance of attributes and experiences to understanding their impact on your brand’s key performance metrics. Conduct a key drivers analysis to identify and quantify which variables are most important in driving KPIs such as likelihood to purchase, customer satisfaction, likelihood to recommend, and even customer loyalty.

What you'll get
Your custom report will deliver the results of the key drivers analysis through both graphics and write up. The analysis will identify both the total variance in the outcome variable that is explained by the full set of predictor variables, as well as the relative importance of each predictor variable in terms of its impact on the outcome variable (e.g., the degree to which a brand’s customer service experience predicts likelihood to recommend a brand). Accordingly, the analysis will make clear which predictor variables are most critical to invest time and attention into in order to move the needle on your brand’s KPIs.
Key metrics
  • R-squared
    The total variance (in the outcome variable) explained by the full set of predictor variables.
  • Relative importance
    A derived value from each predictor variable in the model. It is represented as a percentage of the total variance explained, and higher values indicate a more impactful predictor variable.
Common applications

Product development

Assess the impact of various product features on overall product satisfaction.

Brand insights

Assess the impact of various brand touchpoints and experiences on customer satisfaction, loyalty, and likelihood to recommend.

In order to set up a survey for key drivers analysis, respondents must provide:

  • An evaluation for a set of product or experience attributes that are of interest for predicting one or more key outcomes. Common scales on which to measure predictor variables include satisfaction and performance, but NOT importance (rather, importance is derived through the analysis).
  • An evaluation of any key outcomes for which we seek to identify key drivers (e.g., likelihood to recommend, brand satisfaction, likelihood to reengage/repurchase, loyalty, or even selection of a brand among a competitive set).

In the analysis, the role of predictor variables on the outcome variable is assessed through multiple regression with relative importance analysis layered on top. The primary benefit of relative importance analysis is that it provides a decorrelated view of the importance of each predictor attribute on impacting the outcome variable. The decorrelated approach is useful in that it provides distinct and unique contribution values for each of your predictor variables, which helps to prioritize areas of product/brand experience to focus on in order to most efficiently impact your brand KPIs.