# Emerging Product Demand Detector (`tuhin/emerging-product-demand-detector`) Actor

Discover products that people are starting to want before everyone else does. Scores demand, momentum, purchase intent, competition and sentiment from public signals.

- **URL**: https://apify.com/tuhin/emerging-product-demand-detector.md
- **Developed by:** [Tuhin](https://apify.com/tuhin) (community)
- **Categories:**
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.01 / 1,000 results

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.
Actors are written with capital "A".

## 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.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Emerging Product Demand Detector

**Discover products that people are starting to want — before everyone else does.**

Give it a product, niche, or keyword (e.g. `portable ice maker`, `home gym`, `robot vacuum`) and it discovers the products people are actually researching and buying, then ranks them by a transparent **Opportunity Score** built from real public demand signals.

> **How it works / responsible use:** This Actor uses only *public, no-login* demand signals — YouTube search (content volume + view counts), and Google, YouTube & Amazon **autocomplete** (what people are actively searching). It does **not** log in, scrape private accounts, bypass CAPTCHAs/paywalls, or access anything gated. Every score is explainable.

***

### Who it's for

Amazon affiliates, e-commerce sellers, product researchers, brands, agencies, and content creators who want an **early-warning system for product demand**.

### The scores (all 0–100, transparent)

| Score | What it measures |
|-------|------------------|
| **Demand** | Total YouTube view volume + mentions + Amazon-search corroboration |
| **Momentum** | Peak recent view velocity + share of fresh content (acceleration) |
| **Purchase Intent** | Presence in Amazon search suggestions (buyers) + intent language ("best", "vs", "review") |
| **Competition** | Content saturation for the product + how crowded the niche is |
| **Sentiment** | Positive vs negative language in titles |
| **Opportunity** | Weighted blend favouring momentum, intent, demand, low competition, positive sentiment, multi-source |

**Opportunity weights** (exposed in `scoreComponents.weights`): momentum 0.25 · purchase-intent 0.20 · demand 0.18 · low-competition 0.15 · sentiment 0.12 · source-diversity 0.10.

Every product ships a `scoreComponents` object showing the raw numbers behind each score — no black-box ranking.

### Emerging detection

Each product is classified **rising / stable / peaking / declining** from within-run momentum, and — when you pass a previous run's dataset — from real growth across runs.

### Input

| Field | Type | Default | Description |
|-------|------|---------|-------------|
| `keywords` | array | – | Products/niches/keywords to research. One or many. |
| `expansionDepth` | integer | `1` | Autocomplete expansion: 0 = seed only, 1 = seed + a–z (recommended), 2 = deeper. |
| `maxProducts` | integer | `40` | Max ranked products per keyword. |
| `affiliateMode` | boolean | `true` | Flag affiliate-suitable products + generate content angles. |
| `minOpportunityScore` | integer | `0` | Drop products below this Opportunity Score. |
| `previousDatasetId` | string | – | Prior run's dataset ID → mention growth + acceleration across runs. |
| `proxyConfiguration` | object | Apify Proxy | Datacenter proxy is fine; no residential needed. |

#### Example inputs

```json
{ "keywords": ["portable ice maker"] }
```

```json
{ "keywords": ["robot vacuum", "standing desk"], "maxProducts": 25, "affiliateMode": true }
```

```json
{ "keywords": ["home gym"], "minOpportunityScore": 60, "previousDatasetId": "<PREVIOUS_RUN_DATASET_ID>" }
```

### Output

#### 1) Emerging products (default dataset, ranked by Opportunity Score)

```json
{
  "productName": "Roborock",
  "brand": "Roborock",
  "category": "robot vacuum",
  "asin": null,
  "productUrl": "https://www.amazon.com/s?k=Roborock",
  "sourceUrls": ["https://www.youtube.com/watch?v=..."],
  "demandScore": 74, "momentumScore": 91, "purchaseIntentScore": 100,
  "competitionScore": 62, "sentimentScore": 100, "opportunityScore": 81,
  "trendDirection": "rising", "mentionGrowth": null, "mentionGrowthPercent": null,
  "sourceCount": 3, "platformsDetected": ["youtube","amazon","google"],
  "keywords": ["roborock qrevo", "roborock s8"],
  "commonUseCases": ["pet hair", "hardwood floors"],
  "pros": [], "cons": ["robot vacuum getting stuck"], "complaints": ["..."],
  "competingProducts": ["Ecovacs","Eufy","Deebot"],
  "scoreComponents": { "demand": {...}, "momentum": {...}, "weights": {...} },
  "affiliate": {
    "affiliateSuitable": true,
    "contentAngles": ["best Roborock","Roborock review","Roborock vs Ecovacs","best Roborock under $100","Roborock alternatives"],
    "amazonSearchUrl": "https://www.amazon.com/s?k=Roborock"
  },
  "observedAt": "2026-08-30T18:00:00.000Z"
}
```

#### 2) Source evidence (named dataset `source-evidence`)

One row per supporting signal — which platform it came from and the URL/what it was:

```json
{ "seed": "robot vacuum", "productName": "Roborock", "source": "youtube", "signalType": "video-mention", "url": "https://www.youtube.com/watch?v=...", "observedAt": "..." }
```

A **run summary** is written to the key-value store record `OUTPUT`.

### Scheduling & historical acceleration

Schedule the Actor and pass the previous run's dataset ID as `previousDatasetId`. It then computes `mentionGrowth` / `mentionGrowthPercent` and upgrades trend detection to real cross-run acceleration — so you catch products that are *suddenly* taking off.

### Scoring methodology (summary)

Signals are normalized within each keyword's candidate set (relative min-max for demand/momentum/competition; absolute lexicon scores for intent/sentiment; source count for diversity), then combined with the fixed Opportunity weights above. Full per-product math is in `scoreComponents`.

### Limitations (honest)

- **Product extraction is heuristic.** Brand/product names are mined from YouTube titles and cross-checked against Amazon autocomplete. It catches the real brands in a niche well, but an occasional common word can slip through, and pure model-number products may be missed. It is not a trained NER model.
- **ASIN is not resolved** (`asin: null`) — Amazon product pages aren't scraped. `productUrl` is an Amazon search link, which is affiliate-ready.
- Sentiment is title-level (public metadata), so it is a light signal, not deep review analysis.
- Scores are **relative demand intelligence**, not official platform analytics.

### Architecture (modular)

`sources/` (suggest + youtube) · `core/` (text extraction, sentiment, scoring, emerging, affiliate, state) · `main.js` (orchestration/output). New marketplaces or social platforms (Reddit, TikTok, etc.) drop in as additional `sources/*` modules and feed the same scoring engine — they need their own official API keys or residential proxies, which is why this v1 ships the reliably-public sources.

# Actor input Schema

## `keywords` (type: `array`):

Seeds to research, e.g. "portable ice maker", "home gym", "pet products". One or many.

## `expansionDepth` (type: `integer`):

How aggressively to expand each seed via autocomplete. 0 = seed only, 1 = seed + a–z modifiers (recommended), 2 = two-level expansion (slower, broader).

## `maxProducts` (type: `integer`):

Maximum ranked products per seed in the output.

## `affiliateMode` (type: `boolean`):

Flag products suitable for affiliate content and generate content angles ("best X", "X vs Y", "best X under $100", "X alternatives").

## `minOpportunityScore` (type: `integer`):

Only output products at or above this Opportunity Score (0–100). 0 = keep all.

## `previousDatasetId` (type: `string`):

Optional. Dataset ID of a previous run — used to compute mention growth and detect products that are suddenly accelerating.

## `proxyConfiguration` (type: `object`):

Optional. These public endpoints work fine WITHOUT a proxy and are faster direct — leave off unless the run's IP gets rate-limited, then enable Apify Proxy (datacenter).

## Actor input object example

```json
{
  "keywords": [
    "home gym",
    "kitchen gadgets"
  ],
  "expansionDepth": 1,
  "maxProducts": 40,
  "affiliateMode": true,
  "minOpportunityScore": 0,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}
```

# Actor output Schema

## `emergingProducts` (type: `string`):

One record per discovered product. Source-level evidence is written to the named dataset 'source-evidence'; the run summary is in the key-value store record OUTPUT.

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "keywords": [
        "portable ice maker"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("tuhin/emerging-product-demand-detector").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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "keywords": ["portable ice maker"] }

# Run the Actor and wait for it to finish
run = client.actor("tuhin/emerging-product-demand-detector").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 '{
  "keywords": [
    "portable ice maker"
  ]
}' |
apify call tuhin/emerging-product-demand-detector --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,tuhin/emerging-product-demand-detector"
        }
    }
}

```

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 <APIFY_API_TOKEN>` 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/fMfUKW3yB7l8icQSM/builds/BMXshdNBBX8dWoLY9/openapi.json