Claude: Four Essential Guidelines to Refine Your Analyses

Context and Limitations of the Analysis

When using Claude for data analysis, it is crucial not to attribute observed trends to brand-wide causes without tangible evidence. Claude, as an analyst, might be tempted to create strategic narratives, but it is essential to acknowledge what is not known. Without access to product launch timelines, inventory records, or promotional campaigns, conclusions must remain cautious.

Defining the Meaning of Changes

Claude frequently uses the term significant, but it is rarely defined. A change should only be labeled significant if it represents a variation of more than 15 percentage points in the positive/negative ratio compared to the previous quarter, or if a theme appears in more than 20% of a department's reviews. For minor variations, terms like "slight increase" are preferable. This prevents minor fluctuations from being perceived as important, which could disengage stakeholders.

Confidence Qualifiers for Each Insight

Each insight should be preceded by a confidence label: [Data-Driven], [Possible], or [Speculative]. The label [Data-Driven] is reserved for insights directly derived from reviews. [Possible] is used for reasonable inferences, and [Speculative] for hypotheses not supported by reviews.

Acknowledging the Limitations of the Analysis

It is essential to include a section titled "What This Report Cannot Tell You" at the end of each report. This section should list 2-3 necessary elements for stronger conclusions, such as review counts at the SKU level or data on repeat purchases. This helps to recognize the limitations of the analysis and guides stakeholders toward questions to explore further.