Problem Statement
The workflow needs to transform raw KPI tables, definitions, segment cuts, and contextual business events into a concise, executive-ready performance summary for the last 7 and 30 days. The summary must highlight the top changes, identify likely performance drivers, flag anomalies, and generate targeted follow-up questions for metric owners. The workflow ensures consistent, high-quality briefings without requiring manual analysis each cycle.
Steps
Ingest KPI Inputs
INGESTCollect the KPI table, metric definitions, segment cuts, and any notes on launches or incidents from the user. Normalize the input so KPIs, time windows, and segmentation metadata are structured and ready for processing.
Retrieve Context and Historical Reference
EXECUTEPull relevant historical or contextual details, such as prior-period trends, known initiatives, and segment benchmarks. This provides grounding for interpreting performance changes.
Generate Performance Summary
EXECUTEDraft a 1-page executive narrative summarizing 7-day and 30-day business performance, including top changes, likely drivers, anomalies, and questions for KPI owners. Ensure the tone is concise, analytical, and executive-ready.
Validate for Completeness and Quality
EXECUTECheck that major KPI movements are addressed, contextual factors are correctly applied, and no unsupported causal claims are made. Flag any gaps or inconsistencies for user review.
Prompt Configurations
Executive KPI Summary Prompt
You are an expert KPI briefing analyst. Using the provided KPI table (current vs prior), metric definitions, segment cuts, and known launches/incidents, create a 1-page executive summary. Requirements: 1) Highlight the top 5 performance changes across 7-day and 30-day windows. 2) Provide likely drivers for each change, using only the information given. Do not invent data. 3) Flag anomalies and data-quality concerns with clear explanations. 4) List 5 questions to validate with KPI owners. 5) Maintain a concise, analytical, executive-ready tone. 6) If data is ambiguous or insufficient, explicitly state the uncertainty rather than making assumptions.
Example Inputs/Outputs
KPI Table: DAU: 1.2M (7d) vs 1.05M prior; Revenue: $480k vs $500k prior; Activation Rate: 42% vs 45%. Definitions: DAU = daily active users, Activation = new users completing onboarding. Segment Cuts: US, EU. Launches: New onboarding flow released 7 days ago.
7-day DAU increased 14%, driven largely by US growth, likely linked to the recent onboarding improvements. Revenue fell 4%, potentially due to EU payment issues noted in prior incident logs. Activation dropped from 45% to 42%, suggesting the new onboarding flow may still require tuning. Anomalies include unusually sharp EU DAU fluctuations suggesting possible instrumentation delay. Questions for owners: 1) Are payment error rates up in EU? 2) Was DAU artificially inflated by marketing spend? 3) Did onboarding A/B experiments affect activation? 4) Were any segments missing partial-day data? 5) Did the new flow change activation definitions? Next actions include validating EU instrumentation and reviewing onboarding experiment logs.