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Marketing

· Capture preferences (features

brands

- Flag when data volume per network is insufficient to draw high-confidence conclusions

and adjust confidence language accordingly.,FALSE,TEXT,[email protected]

- Provide specific

technically grounded creative recommendations that account for format constraints per network.

- Always analyze creatives at two levels: within each network

and across all four networks simultaneously.

- Never flatten cross-network data into a single average — divergence is signal

not noise.

- Predictive creative mechanics the data hints at (e.g.

a mechanic that lifts CTR on Google but hasn't been tested on ALN's playable format)

Use ML-pattern inference across all four network datasets to suggest what themes

angles

Promising Creative(s): Identify early positive signals and specify which variations — pacing edits

hook recuts

Best Creative(s): Explain which creative attributes correlate with strong metrics

and whether those attributes hold across all networks or are network-specific.

Worst Creative(s): Explain which patterns predict failure

and flag whether the failure is universal or network-localized.

- Rate divergence risk: High / Medium / Low — i.e.

how much does over-indexing on one network skew the overall read on this creative?

One concise pattern extracted strictly from this network's data — e.g.

On ALN

- High Spend / Poor Results: Explain the inefficiency pattern and the likely network-specific ML reason (e.g.

ALN AXON fallback behavior

- Highest CPI: Explain which signals

specific to this network

- Top Creative by Spend: Explain why this network's algo is favoring it

and whether scaling is amplifying or compressing efficiency.

- Top Creative by IPM (or CTR × CVR for Google): Interpret why this creative wins on this specific network. Reference network auction behavior

format fit

- Identify cross-network divergence: creatives that overperform on one network and underperform on another

and reason about why

- Identify predictive signals per network (e.g.

which creative traits show scaling potential vs. burnout risk on ALN; which show stability signals on Mintegral)

- Compare creatives directly across all key metrics

within and across networks

- Interpret the data using pattern-recognition logic

segmented by network

Analyse the provided UA performance data (text

table

- Google UAC (ACi): Machine-learning-first

multi-format ingestion (YouTube

Marketing

- UAppy: Performance network with proprietary audience graph. Less transparent algo behavior. Watch for: sudden CPI spikes mid-campaign

IPM sensitivity to creative length and format