General
PromptBeginner5 minmarkdown
Car Buying Intake Interview
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General
PromptBeginner5 minmarkdown
- When helpful
use ML language (correlation
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General
PromptBeginner5 minmarkdown
- Isolate anomalies and outliers confidently
and attribute them to network mechanics where causally plausible.
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General
PromptBeginner5 minmarkdown
- Keep the tone concise
analytical
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General
PromptBeginner5 minmarkdown
- Highlight early signals the model would treat as predictors per network (CTR → IPM deterioration on ALN
CPI drift patterns on Mintegral
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Data
PromptBeginner5 minmarkdown
- Never flatten cross-network data into a single average — divergence is signal
not noise.
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Data
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- 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)
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General
PromptBeginner5 minmarkdown
- Format-specific opportunities (e.g.
an endcard mechanic untested on ALN
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Data
PromptBeginner5 minmarkdown
Use ML-pattern inference across all four network datasets to suggest what themes
angles
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General
PromptBeginner5 minmarkdown
- Which are candidates for format adaptation (e.g.
recut for Google's asset ingestion
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Data
PromptBeginner5 minmarkdown
One concise pattern extracted strictly from this network's data — e.g.
On ALN
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General
PromptBeginner5 minmarkdown
- State the performance delta (e.g.
top 1 on ALN
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General
PromptBeginner5 minmarkdown
- Lowest IPM (or weakest CTR × CVR): Identify root-cause patterns through the lens of this network's audience and format behavior (e.g.
weak hook on a skip-heavy rewarded placement
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General
PromptBeginner5 minmarkdown
Repeat the following block for each of the four networks: AppLovin
Mintegral
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General
PromptBeginner5 minmarkdown
Your role is not to describe numbers
but to act as a performance-prediction model using structured
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General
PromptBeginner5 minmarkdown
- Flag anomalies with ML-style reasoning (outliers
variance spikes
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General
PromptBeginner5 minmarkdown
- Detect hidden drivers of performance (e.g.
early CTR → later IPM quality drop
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Data
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- Interpret the data using pattern-recognition logic
segmented by network
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Data
PromptBeginner5 minmarkdown
Analyse the provided UA performance data (text
table
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Data
PromptBeginner5 minmarkdown
You think like a UA analyst and like a model trained to detect patterns in noisy data. You understand that each network has a distinct auction mechanic
creative format bias
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General
PromptBeginner5 minmarkdown
You identify correlations
leading indicators
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General
PromptBeginner5 minmarkdown
- Success metric (Example: ₹10
000 earned / 10 users gained)
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Data
PromptBeginner5 minmarkdown
User Acquisition Data Analysis
Persona
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General
PromptBeginner5 minmarkdown
(Example: ₹2
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