# Digital Product Opportunity Radar (`herazur/digital-product-opportunity-radar`) Actor

Find digital product opportunities using live Etsy market evidence and Pinterest trend validation. Rank niches by demand, competition, digital fit, margin, freshness, and trend momentum with transparent TEST / WATCH / SKIP decisions.

- **URL**: https://apify.com/herazur/digital-product-opportunity-radar.md
- **Developed by:** [Furkan Toluç](https://apify.com/herazur) (community)
- **Categories:** E-commerce, SEO tools, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $490.00 / 1,000 opportunity reports

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/platform/actors/running/actors-in-store#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

## Digital Product Opportunity Radar — Etsy + Pinterest

Find evidence-backed digital product opportunities using live Etsy market signals and Pinterest trend validation.

This Apify Actor is a focused product opportunity finder for creators deciding which digital-product niche to test next. It is not a generic scraper or an AI idea generator. It turns normalized marketplace evidence into transparent rankings, confidence scores, and `TEST` / `WATCH` / `SKIP` decisions.

### 1. What this Actor does

Give the Actor up to five seed niches, such as `ADHD planner`. It discovers related Etsy keyword opportunities, shortlists candidates using Etsy commercial evidence, validates those candidates against Pinterest trends, and returns one Dataset row per ranked opportunity.

Example result:

```text
ADHD Morning Routine Cards

Opportunity: 82/100
Confidence: 89/100
Decision: TEST

Why:
- strong Etsy demand
- manageable entry headroom
- positive Pinterest validation
```

Numbers above are illustrative. Production scores are calculated only from normalized upstream evidence; missing values remain `null`.

Use it for Etsy product research, Etsy keyword research, printable product research, Etsy niche research, market validation, and prioritizing digital downloads. It can surface stronger and weaker candidates related to a seed; it does not promise “low competition Etsy niches” or guaranteed sales.

### 2. Who it is for

- Etsy digital-product and printable sellers
- Designers researching editable templates, planners, worksheets, trackers, and bundles
- Product researchers comparing digital product ideas with current evidence
- Agencies or automation builders that need structured niche-research output through the Apify API

### 3. How it works

```text
Seed niche
  → Etsy keyword discovery
  → Etsy adapter and normalization
  → evidence-based candidate shortlist
  → Pinterest trend validation
  → Pinterest adapter and normalization
  → transparent opportunity + confidence scoring
  → ranked Dataset rows and full reports
```

Etsy is the primary commercial evidence source. Pinterest trends are secondary validation and cannot overpower Etsy in the score.

The default upstream Actors are configurable without changing business logic:

- Etsy: `yumitori/etsy-keyword-tool`
- Pinterest: `yumitori/pinterest-trends-scraper` in keyword-research mode

### 4. Input

```json
{
  "seeds": [
    "ADHD planner",
    "kids screen free activities"
  ],
  "country": "US",
  "maxOpportunities": 10,
  "candidateLimitPerSeed": 20,
  "etsyExpansionDepth": 1,
  "debug": false
}
```

| Field | Required | Default | Limits |
|---|---:|---:|---|
| `seeds` | Yes | — | 1–5 strings, each 2–80 characters |
| `country` | No | `US` | `US`, `GB`, `CA`, `DE`, `FR`, `IT`, `ES`, `BR`, `MX`, `AR`, `IN` |
| `maxOpportunities` | No | `10` | 1–20 |
| `candidateLimitPerSeed` | No | `20` | 1–40 |
| `etsyExpansionDepth` | No | `1` | 1 or 2 |
| `debug` | No | `false` | Includes safe normalization/scoring metadata; never secrets |

### 5. Output

The default Dataset receives one item per opportunity. Each record contains:

- the original seed, final rank, normalized keyword, suggested product, and conservative product format
- Opportunity Score, Confidence Score, and decision
- all normalized score components
- Etsy sample size, listings, average price, carts, recent-sales/listing shares, discount density, and digital-product share
- qualified Pinterest term, match quality, search volumes, multi-week momentum windows, trend evidence, and `FULL` / `PARTIAL` / `NONE` evidence quality
- available Etsy and Pinterest evidence; missing or low-quality evidence is `null`
- deterministic reasons and signal-supported risks
- real source URLs when upstream data supplies them

The default Key-Value Store also contains:

- `OUTPUT.json`: the full machine-readable report
- `REPORT.md`: a concise human-readable report

### 6. Opportunity Score

The 0–100 Opportunity Score uses these components:

| Component | Weight |
|---|---:|
| Etsy demand | 30% |
| Entry headroom (`100 - competition`) | 25% |
| Digital fit (`digital_pct`) | 15% |
| Pinterest validation | 15% |
| Margin potential | 10% |
| Freshness (`recent_listings_pct`) | 5% |

Missing components are ignored and available weights are re-normalized. Missing Pinterest data is not converted to zero. A normal score requires Etsy demand plus additional Etsy evidence; otherwise the result is `INSUFFICIENT_EVIDENCE`.

### 7. Confidence Score

Confidence is separate from opportunity quality. A promising niche can have a high Opportunity Score and low Confidence Score when evidence coverage is thin.

| Confidence component | Weight |
|---|---:|
| Etsy core-evidence completeness | 60% |
| Etsy sample credibility | 25% |
| Qualified cross-market confirmation | 15% |

Rich Etsy evidence can establish meaningful baseline confidence on its own. A qualified Pinterest match increases confidence; Pinterest remains optional and does not automatically lower the Etsy-based Opportunity Score when unavailable.

Pinterest evidence is `FULL` when a qualified match includes enough weekly history for stable multi-week comparison, `PARTIAL` when a qualified match has current-interest/trend evidence but no usable history, and `NONE` when no sufficiently related validation is available. Partial validation is deliberately discounted. Separately, `matchType` reports semantic specificity as `EXACT`, `CLOSE`, `BROAD`, or `NONE`; broad matches retain their raw trend evidence but receive a capped validation contribution because an important niche token was dropped.

### 8. TEST / WATCH / SKIP

- `TEST`: Opportunity ≥ 75 and Confidence ≥ 60
- `WATCH`: Opportunity ≥ 60 but the `TEST` rule is not met
- `SKIP`: Opportunity < 60
- `INSUFFICIENT_EVIDENCE`: minimum Etsy evidence is unavailable

These decisions prioritize small, inexpensive product tests. They are not predictions or guarantees.

### 9. Example output

```json
{
  "recordType": "opportunity",
  "seed": "ADHD planner",
  "rank": 1,
  "keyword": "adhd morning routine cards",
  "suggestedProduct": "ADHD Morning Routine Cards",
  "suggestedFormat": "Printable cards",
  "opportunityScore": 82,
  "confidenceScore": 87,
  "decision": "TEST",
  "scores": {
    "etsyDemand": 88,
    "entryHeadroom": 72,
    "digitalFit": 100,
    "pinterestValidation": 81,
    "marginPotential": 70,
    "freshness": 78
  }
}
```

This example is illustrative, not hardcoded production data.

### 10. Data sources

The Actor orchestrates existing Apify Store Actors rather than maintaining its own Etsy or Pinterest crawlers. Adapters isolate upstream schemas from the scoring engine. Override providers with `ETSY_ACTOR_ID` and `PINTEREST_ACTOR_ID` when a compatible adapter mapping is available.

Cost protection defaults to `$1.00` total across Etsy seed calls and `$0.25` for the Pinterest call. Configure:

```text
ETSY_MAX_TOTAL_CHARGE_USD=1.00
PINTEREST_MAX_TOTAL_CHARGE_USD=0.25
ETSY_TIMEOUT_SECONDS=300
PINTEREST_TIMEOUT_SECONDS=300
```

The SDK passes these limits to child Actor runs. Etsy's configured budget is divided across the 1–5 seed calls so the run-level cap remains conservative.

### 11. Limitations

- Scores are research signals, not revenue guarantees.
- Etsy is the primary commercial evidence source.
- Pinterest is used as secondary trend validation.
- Pinterest attention does not necessarily equal purchasing demand.
- Missing Pinterest data is not treated as zero demand.
- Marketplace conditions change over time.
- Upstream Actor schemas and field coverage can change; unknown fields are not guessed.
- Results should prioritize small product tests, not justify large upfront investment.
- Upstream Actors can have their own pricing, availability, geographic coverage, and usage limits.

### 12. API / automation usage

Run locally after providing Apify credentials:

```bash
npm install
npm run build
apify run
```

Deploy from this directory:

```bash
npm install -g apify-cli
apify login
apify validate-schema
apify push
```

Use the Apify API after deployment:

```bash
curl -X POST \
  "https://api.apify.com/v2/acts/YOUR_USERNAME~digital-product-opportunity-radar/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"seeds":["meal planner"],"country":"US","maxOpportunities":3,"candidateLimitPerSeed":5,"etsyExpansionDepth":1}'
```

#### Development and verification

Tests use sanitized fixtures and never call paid Actors:

```bash
npm run typecheck
npm test
npm run build
```

Report charging is scaffolded but disabled by default. Enable it only after configuring Pay-Per-Event pricing in Apify Console:

```text
ENABLE_REPORT_CHARGING=true
ACTOR_TEST_PAY_PER_EVENT=true
```

The only future charge event in code is `report_generated`, issued after output is written and only when the effective pricing model is Pay-Per-Event.

# Actor input Schema

## `seeds` (type: `array`):

Enter 1–5 product niches to investigate.

## `country` (type: `string`):

Market used for Pinterest trend validation.

## `maxOpportunities` (type: `integer`):

Maximum ranked opportunities returned across all seeds.

## `candidateLimitPerSeed` (type: `integer`):

Maximum Etsy candidates shortlisted per seed before Pinterest validation.

## `etsyExpansionDepth` (type: `integer`):

Keyword expansion depth. Depth 2 costs more and can take longer.

## `debug` (type: `boolean`):

Include safe adapter and scoring diagnostics. Secrets are never included.

## Actor input object example

```json
{
  "seeds": [
    "ADHD planner",
    "kids screen free activities"
  ],
  "country": "US",
  "maxOpportunities": 10,
  "candidateLimitPerSeed": 20,
  "etsyExpansionDepth": 1,
  "debug": false
}
```

# Actor output Schema

## `opportunities` (type: `string`):

Ranked digital product opportunities with scores, evidence, risks, and TEST / WATCH / SKIP decisions.

## `jsonReport` (type: `string`):

Complete structured opportunity report.

## `markdownReport` (type: `string`):

Human-readable opportunity report.

# 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 = {
    "seeds": [
        "ADHD planner",
        "kids screen free activities"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("herazur/digital-product-opportunity-radar").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 = { "seeds": [
        "ADHD planner",
        "kids screen free activities",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("herazur/digital-product-opportunity-radar").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 '{
  "seeds": [
    "ADHD planner",
    "kids screen free activities"
  ]
}' |
apify call herazur/digital-product-opportunity-radar --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,herazur/digital-product-opportunity-radar"
        }
    }
}

```

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/CSqmDlFQZim7ejJ5Q/builds/xDqPR9MKVgMi6dnxw/openapi.json