18d • AI Tips & Tools
Promo Velocity Forecast Prompt (High-Low Scenario + Confidence Ranges)
Using your current trend data and the details of your upcoming promotion, estimate projected sales velocity and model a range of possible outcomes.
Inputs:
- Recent trend data (last 4–8 weeks): [insert weekly units or summary]
- Promo start date: [insert date]
- Forecast time frame: [insert time frame]
- Projected promo lift (%): [insert percent]
Contextual factors to include:
- Promo type and expected impact: [insert promo mechanics and rationale]
- Historical promo performance: [insert comparison data, similar offer results, or benchmarks]
- External influencing factors: [insert competitive activity, seasonality, retailer traffic, inventory position, marketing support, or distribution changes]
- Channel nuances (if applicable): [insert online vs in-store differences, marketplace traffic patterns, or retailer-specific lift expectations]
Please calculate the following:
- Baseline average velocity from the trend data.
- Anomaly adjustments for stockouts, unusual spikes, or distribution disruptions.
- Adjusted baseline: baseline after anomaly correction.
- Promo lift application: apply the projected lift percent to the adjusted baseline.
- Scenario modeling:
- Confidence range: provide a high-low band for the forecast.
- Final forecast: return the projected velocity for the selected time frame (weekly or multi-week/month).
Please present the results in a clear table with these rows:
- Baseline average velocity
- Adjustments for anomalies
- Adjusted baseline velocity
- Promo lift percent provided
- Most likely forecast
- High scenario forecast
- Low scenario forecast
- Confidence range (high and low)
- Final projected forecast for the selected time frame
After the table, provide a short written summary explaining how the scenarios differ and what operators should watch for during the promo period.
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Promo Velocity Forecast Prompt (High-Low Scenario + Confidence Ranges)
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