# Buzz-to-Sales Lag Analyzer (`rustic_soybean/buzz-to-sales-lag-analyzer`) Actor Compare your own daily product-mention counts with sales to measure historical peak timing and lagged correlations. Uses data you provide; it does not scrape social media or forecast future sales. - **URL**: https://apify.com/rustic_soybean/buzz-to-sales-lag-analyzer.md - **Developed by:** [Chris Johnson](https://apify.com/rustic_soybean) (community) - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event ## What's an Apify Actor? An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md. ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run. For examples already wired to this Actor's own input schema, see the [API](#api) section below. Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`). # README ## Buzz-to-Sales Lag Analyzer Find out whether increases in online mentions tend to come before changes in your own product sales. This Actor compares daily mention counts with daily units sold and reports the observed peak dates, their day offset, and the strongest historical relationship within a lag window. ### Use it when - You already export daily mention counts from a social-listening or analytics tool. - You have daily sales totals for the same product and dates. - You want a repeatable, machine-readable analysis across several products. This Actor analyzes data you provide. It does not collect posts, connect to social accounts, or predict future sales. ### Input Provide 3–366 consecutive daily observations per product. Each observation needs a date, mention count, and units sold. Choose a lag window from 0 to 30 days. ```json { "products": [ { "productId": "sku-123", "observations": [ { "date": "2026-05-01", "mentions": 12, "unitsSold": 8 }, { "date": "2026-05-02", "mentions": 18, "unitsSold": 9 }, { "date": "2026-05-03", "mentions": 21, "unitsSold": 11 } ] } ], "maxLagDays": 7 } ``` In a full run, include at least three consecutive days for each product. Use consistent dates and definitions across both data sources. Do not include post text, usernames, customer details, or other personal information. ### Results The Actor emits one dataset item per product with the largest observed mention and sales peaks, the number of days between those peaks, the strongest lagged correlation between daily changes, the number of paired days, and an evidence status. ### How to interpret the result This is a historical comparison, not a forecast or proof that mentions caused a sales change. Promotions, stockouts, price changes, seasonality, returns, and missing data can affect the relationship. Check the dates against your campaign and inventory records before making a business decision. No external service or paid data source is required to run the analysis. # Actor input Schema ## `products` (type: `array`): Daily social mention counts and your own sales units by product. Use data exported from sources you are authorized to use. ## `maxLagDays` (type: `integer`): Descriptive cross-correlation window. Longer windows need more history and can find coincidental patterns. ## Actor input object example ```json { "products": [ { "productId": "example-product", "observations": [ { "date": "2026-10-01", "mentions": 8, "unitsSold": 3 }, { "date": "2026-10-02", "mentions": 12, "unitsSold": 4 }, { "date": "2026-10-03", "mentions": 20, "unitsSold": 5 }, { "date": "2026-10-04", "mentions": 34, "unitsSold": 7 }, { "date": "2026-10-05", "mentions": 25, "unitsSold": 11 }, { "date": "2026-10-06", "mentions": 15, "unitsSold": 18 }, { "date": "2026-10-07", "mentions": 10, "unitsSold": 14 } ] } ], "maxLagDays": 3 } ``` # Actor output Schema ## `results` (type: `string`): Dataset records containing observed peak timing, descriptive lag correlation, and evidence limitations. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("rustic_soybean/buzz-to-sales-lag-analyzer").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("rustic_soybean/buzz-to-sales-lag-analyzer").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}") for item in client.dataset(run.default_dataset_id).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call rustic_soybean/buzz-to-sales-lag-analyzer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "type": "http", "url": "https://mcp.apify.com/?tools=fetch-actor-details,rustic_soybean/buzz-to-sales-lag-analyzer" } } } ``` The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer ` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations). ## OpenAPI specification Download the OpenAPI definition: https://api.apify.com/v2/actors/wfFH4qha56mTLrx3t/builds/Rf6F7SST5xLIyOzLw/openapi.json