Money Engine 2
Pricing
from $1.00 / 1,000 results
Money Engine 2
Pricing
from $1.00 / 1,000 results
Rating
0.0
(0)
Developer
Chris Johnson
Maintained by CommunityActor stats
0
Bookmarked
2
Total users
1
Monthly active users
10 hours ago
Last modified
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Buzz-to-Sales Lag Analyzer
Analyze dated social-mention counts alongside a business's own daily units sold. The Actor reports the dates of the largest observed mention and sales peaks, their day offset, and the strongest descriptive correlation between daily changes within a bounded lead window.
Data and access
This Actor does not scrape social platforms or access store accounts. Supply aggregated daily mentions exported from a source you are authorized to use and your own product-level sales counts. This avoids platform credentials, third-party API charges, and collection outside the source's access rules. Do not include post text, usernames, customer data, or other personal information.
Agent input/output
Input is a products array. Each product has a stable productId and 3–366 consecutive daily observations, each containing date (YYYY-MM-DD), integer mentions, and numeric unitsSold. maxLagDays ranges from 0 to 30. The Actor emits one dataset item per product with the peak dates, peak offset, best lag of differenced Pearson correlation, paired-day count, and an evidence status.
Interpretation
This is a historical diagnostic, not a sales forecast or causal model. Selecting the strongest correlation across multiple lags can find chance patterns; seasonality, promotions, stockouts, pricing changes, returns, campaign timing, and incomplete mention coverage can distort results. A longer time series and repeated product launches are needed before a merchant should rely on a recurring pattern. The Actor deliberately does not say when a future sales peak will occur.
The product hypothesis is to help small ecommerce teams calibrate whether authorized buzz metrics tend to lead their own sales data and by how many days. Store demand and willingness to provide paired time series remain unvalidated; the Actor should stay staged until users test the workflow and show willingness to pay.
Development
Run npm test. No external service, API key, or paid dataset is required. A Store price should only be chosen after an Apify production run establishes actual compute cost and marketplace review validates customer demand.
