# DTC Brand Teardown — Ecommerce Battlecard (AI) (`noniner/dtc-brand-teardown`) Actor

Give a DTC/ecommerce brand domain; get a full AI teardown — store platform, martech stack, catalog breadth, price strategy, growth signals, strengths, weaknesses and how to compete.

- **URL**: https://apify.com/noniner/dtc-brand-teardown.md
- **Developed by:** [Fredrik Hansson](https://apify.com/noniner) (community)
- **Categories:** E-commerce, AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $300.00 / 1,000 brand teardowns

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

## DTC Brand Teardown — Ecommerce Battlecard (AI)

**Give it a DTC brand's domain, get back a one-page competitive teardown.** This Actor is an ecommerce competitive-intelligence super-actor: for every brand domain you hand it, it orchestrates five research organs — store platform, martech stack, catalog sample, firmographics and recent news — and uses OpenAI to write a sharp **ecommerce battlecard**. Store platform and installed apps, martech and pixels, catalog breadth and price tiers, growth signals, strengths, weaknesses, recent moves, and **exactly how to compete** with them.

You bring the domain. It hands back a finished battlecard you can paste straight into a deck, a sales call sheet, or a pitch.

***

### The flow

```
                      ┌─────────────────────────────┐
                      │  noniner/shopify-store-analyzer │ → platform · apps · maturity
                      ├─────────────────────────────┤
                      │ noniner/b2b-tech-stack-detector │ → martech · analytics · pixels
  brand domain  ──────┼─────────────────────────────┼──────▶  AI teardown
  (gymshark.com)      │ noniner/product-catalog-extract.│ → price range · breadth       (one-page
                      ├─────────────────────────────┤                                 battlecard +
                      │  noniner/ai-company-enrichment  │ → brand · who they target       markdown)
                      ├─────────────────────────────┤
                      │   noniner/google-news-scraper   │ → recent moves
                      └─────────────────────────────┘
```

1. **Paste one or more brand domains** (e.g. `gymshark.com`, `allbirds.com`).
2. **The Actor fans out to five organs** — each organ is run once across all your domains, so adding brands stays cheap.
3. **OpenAI synthesizes one grounded teardown per brand** — strictly from what the organs returned. If an organ has no data, that section is simply left out; nothing is invented.

Organs degrade gracefully: if a sub-actor is unavailable or returns nothing, the teardown is still written from whatever organs did return, and `sourcesUsed` tells you exactly what was used.

***

### What you get per brand

For every brand domain, the Actor returns:

1. **`brandOverview`** — who the brand is and what it sells, 1–2 factual sentences.
2. **`category`** + **`positioning`** — the vertical and how they position themselves.
3. **`productStrategy`** — catalog breadth and price tiers, inferred from a product sample.
4. **`pricingStrategy`** — one sentence summarising their price strategy from the sampled price range.
5. **`martechStack`** — the store apps plus martech/analytics/pixels, summarised.
6. **`growthSignals`** — concrete signals of scale (growth apps, retargeting pixels, broad catalog, recent moves).
7. **`strengths`** / **`weaknesses`** — evidence-based bullets about them as an ecommerce operator.
8. **`recentMoves`** — recent headlines from the news organ (`[]` if none).
9. **`howToCompete`** — concrete angles to beat or sell against them (framed to **your** offering when you provide one).
10. **`opportunities`** — gaps a competitor or vendor could exploit (a missing app category, a weak price tier, a stack they lack).
11. **`oneLineSummary`** — one punchy sentence capturing the brand's ecommerce angle.
12. **`teardownMarkdown`** — the whole teardown as a clean one-page markdown doc, ready to paste.

***

### Three ways people use it

- **DTC competitive analysis** — tear down the brands you compete with: store platform, installed apps, catalog breadth, price tiers and growth signals, so you know where you out-execute them and where they out-execute you.
- **Ecommerce agency pitches** — walk into a pitch with a finished battlecard on the prospect *and* their rivals: "here's your competitor's stack, here's the gap, here's how we close it."
- **Shopify app & tool vendor battlecards** — see which apps a brand already runs, spot the missing category, and build a "how to compete with `<brand>`" / market-positioning one-pager for every target account.

Whether you're doing DTC competitive analysis, pulling together ecommerce agency pitches, building Shopify app vendor battlecards, or just need to know **how to compete with a specific brand**, this Actor turns a bare domain into market positioning you can act on.

***

### How the orchestration works

Each organ is a proven public Actor on the `No Niner` account. This super-actor calls them and fuses the result:

| Organ | Actor | What it contributes |
|---|---|---|
| Store platform | [`noniner/shopify-store-analyzer`](https://apify.com/noniner/shopify-store-analyzer) | Ecommerce platform, installed apps, catalog size, store maturity. |
| Technographics | [`noniner/b2b-tech-stack-detector`](https://apify.com/noniner/b2b-tech-stack-detector) | Martech, analytics and tracking pixels on the storefront. |
| Catalog sample | [`noniner/product-catalog-extractor`](https://apify.com/noniner/product-catalog-extractor) | A small product sample → price range and breadth signal (kept low to bound cost). |
| Firmographics | [`noniner/ai-company-enrichment`](https://apify.com/noniner/ai-company-enrichment) | Brand description, who they target, size signal. |
| Recent moves | [`noniner/google-news-scraper`](https://apify.com/noniner/google-news-scraper) | Recent brand headlines and snippets. |

Organs can return a redirected domain (for example `brand.myshopify.com` instead of `brand.com`); results are matched back on the **registrable root**, not the exact string, so the right data always lands on the right brand.

***

### Input

| Field | Type | Notes |
|---|---|---|
| `domains` | array | **Required.** Brand domains, store URLs or emails — the domain is extracted. One per line. |
| `yourOffering` | string | What YOU sell. If set, `howToCompete` and `opportunities` are framed to beat each brand relative to your offering. |
| `maxResults` | integer | Max brands to tear down. Default `25`, max `300`. One charge per brand. |
| `maxConcurrency` | integer | How many brands to synthesize in parallel. Default `3`, max `8`. |
| `model` | string | OpenAI model. Default `gpt-4o-mini`. |
| `useStore` | boolean | Call the store-platform organ. Default `true`. |
| `useTech` | boolean | Call the technographic organ. Default `true`. |
| `useCatalog` | boolean | Call the catalog-sample organ. Default `true`. |
| `useEnrichment` | boolean | Call the firmographic enrichment organ. Default `true`. |
| `useNews` | boolean | Call the recent-news organ. Default `true`. |

#### Minimal input example

```json
{
  "domains": ["gymshark.com", "allbirds.com"],
  "yourOffering": "We sell a Shopify retention and subscriptions app"
}
```

***

### Output example

```json
{
  "domain": "gymshark.com",
  "brandName": "Gymshark",
  "oneLineSummary": "A fitness-apparel juggernaut that turned influencer-led community into a global activewear brand.",
  "category": "activewear / fitness apparel",
  "positioning": "Positions itself as the community-first training-apparel brand built with and for a young fitness audience.",
  "productStrategy": "Broad mid-market activewear range spanning leggings, tops and accessories across a wide price band.",
  "pricingStrategy": "Mid-market pricing that undercuts premium athleisure while staying above fast-fashion.",
  "martechStack": "Runs a mature storefront with analytics and retargeting pixels plus conversion and email/retention apps.",
  "growthSignals": ["Retargeting pixels installed", "Broad multi-category catalog", "Active product-launch cadence"],
  "strengths": ["Strong brand community", "Broad, frequently refreshed catalog"],
  "weaknesses": ["Heavy reliance on discounting", "Crowded activewear category"],
  "recentMoves": ["Expanded into new retail and market segments"],
  "howToCompete": ["Lead with superior retention economics", "Target the post-purchase experience they underinvest in"],
  "opportunities": ["No dedicated subscription/replenishment motion detected — an opening for a retention app"],
  "teardownMarkdown": "# DTC Brand Teardown — Gymshark\n**Domain:** gymshark.com\n**Category:** activewear\n\n> A fitness-apparel juggernaut...\n\n## Overview\n...",
  "sourcesUsed": ["shopify-store-analyzer", "b2b-tech-stack-detector", "product-catalog-extractor", "ai-company-enrichment", "google-news-scraper"]
}
```

`sourcesUsed` always reflects reality — if an organ was unavailable, it simply won't appear, and the teardown is written from the rest.

***

### Pay-per-event pricing

This Actor uses **pay-per-event** billing with one headline event:

| Event | Price | Charged when |
|---|---|---|
| `brand-teardown` | $0.30 | Once per brand successfully torn down. Bundles up to five orchestrated sub-actor runs (store, technographic, catalog, firmographic, news) plus the OpenAI teardown synthesis into one finished battlecard. |

The Actor logs its OpenAI token usage and the estimated AI cost at the end of every run, so the event price always stays safely above the model cost.

***

### Setup

The AI synthesis calls the OpenAI API. Set the environment variable / secret **`OPENAI_API_KEY`** on the Actor. Without it, the Actor cannot write the teardown.

***

### Use it from an AI agent (Apify MCP server)

This Actor is available through the [Apify MCP server](https://mcp.apify.com), so AI agents (Claude, ChatGPT, or anything that speaks MCP) can call it as a tool — hand it a list of brand domains and read back the finished teardowns — with no custom integration code.

***

### Run it locally

```bash
npm install
## put your input in storage/key_value_stores/default/INPUT.json
export OPENAI_API_KEY=sk-...   # required for the AI teardown
apify run -p
```

***

### Related actors

- [Shopify Store Analyzer](https://apify.com/noniner/shopify-store-analyzer) — Detect an ecommerce store's platform, installed apps, catalog size and maturity.
- [Product Catalog Extractor](https://apify.com/noniner/product-catalog-extractor) — Pull the product catalog (titles, prices, variants) from an online store.
- [B2B Competitor Teardown](https://apify.com/noniner/b2b-competitor-teardown) — The B2B/SaaS sibling: positioning, tech stack, pricing and how to win.
- [B2B Tech Stack Detector](https://apify.com/noniner/b2b-tech-stack-detector) — Detect the technologies, martech and pixels a website runs on.

***

Built by the No Niner team.

# Actor input Schema

## `domains` (type: `array`):

DTC/ecommerce brand domains, store URLs or emails (the domain is extracted). One per line — these are the brands you want torn down.

## `datasetId` (type: `string`):

Optional. Read the domains from a previous Actor run's dataset — for no-code chaining via the Integrations tab.

## `datasetField` (type: `string`):

Which dataset field holds the domain/URL. Leave blank to auto-detect (domain, website, companyDomain, url, companyUrl).

## `yourOffering` (type: `string`):

Describe YOUR product or tool. If set, the 'how to compete' angles and opportunities are framed to beat each brand relative to your offering.

## `maxResults` (type: `integer`):

Max brands to tear down. One pay-per-event charge per brand analyzed.

## `maxConcurrency` (type: `integer`):

How many brands to synthesize in parallel in the AI teardown stage.

## `model` (type: `string`):

Override the model (default gpt-4o-mini).

## `useStore` (type: `boolean`):

Call noniner/shopify-store-analyzer for the store platform, installed apps, catalog stats and maturity.

## `useTech` (type: `boolean`):

Call noniner/b2b-tech-stack-detector for the martech, analytics and pixel stack.

## `useCatalog` (type: `boolean`):

Call noniner/product-catalog-extractor for a product sample (price range + breadth signal) per store.

## `useEnrichment` (type: `boolean`):

Call noniner/ai-company-enrichment for brand firmographics (what they sell, who they target).

## `useNews` (type: `boolean`):

Call noniner/google-news-scraper for recent brand moves (headlines + snippets).

## Actor input object example

```json
{
  "domains": [
    "gymshark.com",
    "allbirds.com"
  ],
  "maxResults": 25,
  "maxConcurrency": 3,
  "model": "gpt-4o-mini",
  "useStore": true,
  "useTech": true,
  "useCatalog": true,
  "useEnrichment": true,
  "useNews": true
}
```

# Actor output Schema

## `teardowns` (type: `string`):

All brand teardowns in the default dataset.

# 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 = {
    "domains": [
        "gymshark.com",
        "allbirds.com"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("noniner/dtc-brand-teardown").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 = { "domains": [
        "gymshark.com",
        "allbirds.com",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("noniner/dtc-brand-teardown").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 '{
  "domains": [
    "gymshark.com",
    "allbirds.com"
  ]
}' |
apify call noniner/dtc-brand-teardown --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,noniner/dtc-brand-teardown"
        }
    }
}
```

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/FHQsrnPxfA4l2lOQF/builds/7mUXHLMD1fvMMsERu/openapi.json