While a well-built index can measure a complex phenomenon and produce an easy-to-digest output, the construction of an index is vulnerable to errors. Already prominent in a wide range of fields, indices are increasingly leveraged in Customer Success (CS), with all major CS software now offering index construction features. This paper analyzes one such software, Gainsight Customer Success, to explore how it can be used to build an index in line with the constructor’s intentions. Concepts from multicriteria decision analysis (MCDA) illuminate possibilities and pitfalls in executing key steps of index construction in the software: value functions in exploring normalization; the distinction between “importance measures” and “trade-off ratios” in examining the meaning of the weights; the concept of compensability in guiding our aggregation analysis. Finally, the MCDA concept of value trees highlights both weighting and aggregation approaches. We find that the Gainsight user must possess some index construction expertise in order to control normalization, weighting, and aggregation, or even to understand how settings related to these steps affect the total score of an index built in the software. Importantly, neither the meaning of the weights as applied in the tool, nor the level of compensability allowed for in aggregation, are transparent to the user. In examining these questions of how construction choices affect the meaning of an index’s output, this analysis may be consulted for guidance by CS practitioners looking to build useful indices in any software.