Silent film with burned-in narrationOpen WebM directly
Product performance / 42.60s
Which recommended product was chosen?
Recommendation measurement becomes useful when impressions and clicks remain attached to the product, SKU, placement, strategy, rank and recommendation set.
What You Will See
- Product identityName and SKU keep performance attached to something stakeholders recognize.
- Exposure and responseImpressions, client-reported clicks and CTR appear together.
- Decision contextAverage rank, placement and strategy explain where and how the product was served.
- One accepted tupleThe view isolates a specific click by product and recommendation-set context.
Why It Matters
An aggregate CTR without product identity cannot tell a merchandiser what was actually seen or selected.
Product-level facts make instrumentation inspectable while preserving the distinction between a recorded click and a trustworthy human preference.
The System Underneath
Served setProduct, placement, strategy and rank
⇢
Clicked eventSet ID + selected product tuple
⇢
ClickHouse factsItem-grain event identity
→
Product viewImpressions, clicks, CTR and recency
What This Story Proves
Visible proof
Product-aware recommendation instrumentation appears in the BI dashboard.
Not claimed
The synthetic click proves event capture, not human intent, causal lift or commercial impact.
AI engineering connectionExecutable feedback constrains autonomous change