Driver Analysis (Key Driver Analysis)

Driver analysis, also called key driver analysis (KDA), uses statistical methods to determine which factors most strongly influence an outcome metric like overall satisfaction, NPS, or likelihood to renew. It answers the question: "Of all the things we could improve, which would have the biggest impact?"

The typical approach involves surveying customers on both an overall metric (e.g., "How satisfied are you overall?") and on specific attributes (e.g., "Rate your satisfaction with product quality, support, pricing, ease of use"). Statistical techniques like regression analysis then determine which attributes are the strongest predictors of overall satisfaction.

The output is often visualized as a priority matrix with four quadrants: high importance/high performance (maintain), high importance/low performance (improve immediately), low importance/high performance (potential over-investment), and low importance/low performance (lower priority).

Driver analysis prevents the common mistake of improving areas that customers do not actually care about. A company might invest heavily in faster support response times when driver analysis would reveal that support accuracy, not speed, is what drives satisfaction. This evidence-based prioritization maximizes the ROI of CX investments.