Shapley Values (SV) is relatively well know in the academic world, but only a limited number of consulting agencies adopted it, due to the limitations of most commercial algorithms (e.g., speed of analysis, maximum number of attributes that can be analysed).
- SV analysis is a multivariate statistical approach where an overall metric (e.g., likelihood to recommend) is modeled against various product features.
- SV allow assessing the contribution of each attribute by accounting for the potential overlap between attributes.
- SV overcomes the limitations of correlation and regression analysis:
- all attributes are considered in the model at the same time;
- SV can handle correlated attributes and is well suited to estimate their contribution towards an overall metric.
- Therefore, SV is particularly indicated for the assessment of drivers, allowing the estimation of the impact of each attribute:
- SV algorithm is robust, with results being consistent even when some attributes are removed from the analysis or others are added;
- the approach is ideal for comparisons in time (between waves) or space (across markets).
The functions derived.importance and derived.importance.manyvar available in R-swDrivers allow Shapley Values analysis and can handle most projects, even when many drivers (30+) need to be assessed.
In SV analysis, there are two main deliverables:
- Impact scores: presented as percentages of contribution towards the overall metric (e.g., 30% impact). They are rescaled to add up to 100% to facilitate interpretation and actionability.
- A goodness-of-fit indicator (similar to the R-square of linear regression) to indicate to what extent the attributes included in the analysis explain the overall metric. A high score suggests that no significant driver has been missed when developing the questionnaire.