DEV Community: Muhammad Irfan Khalid

How can I identify winning TikTok Shop products using sales data?

Muhammad Irfan Khalid — Sun, 16 Aug 2026 08:34:16 +0000

If you're building an e-commerce tool, one of the more interesting problems is determining whether a TikTok Shop product is actually gaining momentum.

A simple query such as:

ORDER BY total_sales DESC

isn't enough.

The products with the highest lifetime sales may already be mature and highly competitive. For trend detection, I'd rather combine recent sales, sales velocity, reviews, pricing, competition, inventory, and market data.

1. Start With Recent Sales

Lifetime sales are useful, but recent sales can reveal momentum.

For example:

Product A
Lifetime sales: 100,000
24h sales: 300

Product B
Lifetime sales: 8,000
24h sales: 1,200

Product A is more established, but Product B may deserve more attention if the goal is identifying emerging products.

A useful data model could store periodic snapshots:

{
  "product_id": "12345",
  "country": "US",
  "timestamp": "2026-08-16T08:00:00Z",
  "total_sales": 8000,
  "price": 29.99,
  "reviews": 320
}

Store another snapshot later and you can calculate changes over time.

2. Calculate Sales Velocity

A basic sales velocity calculation could be:

sales_velocity = units_sold / time_period

For example:

1,200 units / 24 hours = 50 units/hour

But the more interesting metric is change in velocity.

If a product moves from:

20 units/hour
       ↓
35 units/hour
       ↓
50 units/hour

then demand appears to be accelerating.

This is more useful for trend detection than simply sorting by lifetime sales.

3. Compare Sales With Reviews

Reviews provide additional context.

For example:

Product A
Sales: 20,000
Reviews: 5,000

Product B
Sales: 7,000
Reviews: 250

Product B might be worth investigating because it has meaningful sales with comparatively few reviews.

I wouldn't use this as a standalone scoring mechanism, though.

It's better treated as one feature in a larger model.

4. Add Competitor Signals

Product-level data becomes more useful when combined with shop-level data.

For example:

Product
   ↓
Find shops selling product
   ↓
Count successful shops
   ↓
Track product adoption
   ↓
Monitor changes

If a product begins appearing across several successful stores, that could be another indication of increasing demand.

You could store something like:

{
  "product_id": "12345",
  "shops_selling": 27,
  "top_shops_selling": 8,
  "shops_added_last_7d": 6
}

Now you have another measurable signal.

5. Include Country-Level Data

Cross-market analysis is particularly interesting.

A product might be mature in the US but relatively early in another market.

Instead of:

product_id → sales

use:

product_id
    ↓
country
    ↓
sales
    ↓
velocity
    ↓
historical trend

This allows you to compare markets independently.

For example:

US       → High sales / Low growth
UK       → Medium sales / High growth
Germany  → Low sales / High growth

The German market might deserve additional research even though its absolute sales are lower.

6. Track Price and Inventory

Sales aren't enough to determine whether a product is commercially interesting.

I'd also store:

price
price_change
stock
sku_count
shipping
delivery_estimate

This allows you to distinguish:

High demand + healthy economics

from:

High demand + poor margins

or:

High demand + insufficient inventory

7. Build a Product Scoring Model

Once you have these features, you can build a basic scoring system.

For example:

score =
    sales_growth * 0.30
  + sales_velocity * 0.25
  + review_signal * 0.10
  + competitor_growth * 0.15
  + market_growth * 0.10
  + inventory_signal * 0.05
  + price_signal * 0.05

The weights shouldn't be considered universal.

They should be adjusted based on your use case and historical results.

For a trend-detection application, sales growth might deserve more weight.

For a sourcing application, margin and inventory might matter more.

8. Store Historical Data

This is probably the most important architectural decision.

A current snapshot tells you:

"This product has 8,000 sales."

Historical data tells you:

"This product had 3,000 sales last week and now has 8,000."

The second piece of information is much more useful for detecting momentum.

A simple architecture could be:

Marketplace Data
       ↓
Data Collection
       ↓
Normalization
       ↓
Database
       ↓
Historical Snapshots
       ↓
Feature Calculation
       ↓
Scoring
       ↓
Dashboard / API / Alerts

9. Automate the Research

If you're building an e-commerce SaaS product, manually checking products doesn't scale.

A scheduled process could:

Retrieve products by country/category.
Store the latest snapshot.
Compare it with previous snapshots.
Calculate sales velocity.
Calculate growth rates.
Update product scores.
Flag products with unusual momentum.
Send results to a dashboard or alert system.

For example:

Every 6 hours
      ↓
Fetch product data
      ↓
Calculate changes
      ↓
Update database
      ↓
Recalculate scores
      ↓
Trigger alerts

This turns product research from a manual task into a repeatable data pipeline.

10. API-Based Implementation

I've been working on this problem from the API side as well.

The TikTok Shop Product Ecommerce Data API I'm building provides structured product and shop data that can be used for this type of application.

The available data includes areas such as:

Rising products
Untapped products
Sales velocity
Price drops
Competitor shop analysis
Shop product catalogs
Shop statistics
SKU stock
Product variants
Logistics information

The country parameter also makes it possible to build country-specific research workflows.

The interesting part isn't simply exposing an endpoint.

It's what developers can build on top of the data.

For example:

API
 ↓
PostgreSQL
 ↓
Historical snapshots
 ↓
Python/Node processing
 ↓
Trend scoring
 ↓
React dashboard
 ↓
Alerts

That could become a product research platform, competitor monitoring system, pricing tracker, or internal analytics tool.

A Practical Example

Suppose your database contains:

Product A
24h sales: 300
48h sales: 500

Product B
24h sales: 1,200
48h sales: 1,500

Product C
24h sales: 2,000
48h sales: 2,100

Simply sorting by 24h sales gives:

C > B > A

But looking at the rate of change tells a different story.

Product B has increased much more sharply relative to its previous period.

That could make it more interesting for an emerging-trend detector, even though Product C has higher absolute sales.

This is why ranking products requires context.

What I Would Track

If I were building a product intelligence system from scratch, my core dataset would include:

Product ID
Country
Category
Timestamp
Total Sales
Recent Sales
Sales Velocity
Reviews
Price
Price Change
Shop Count
Shop Growth
SKU Count
Stock
Shipping

Then I'd create derived features such as:

sales_growth
velocity_growth
sales_review_ratio
shop_adoption_growth
price_change
market_growth
inventory_change

Those features provide much more useful information than a simple "total sales" field.

Final Thoughts

There isn't a single formula that can reliably identify a winning TikTok Shop product.

The better approach is to combine multiple signals and monitor how they change over time.

A useful pipeline is:

Collect data
     ↓
Store historical snapshots
     ↓
Calculate sales velocity
     ↓
Measure growth
     ↓
Analyze competitors
     ↓
Compare markets
     ↓
Check pricing/inventory
     ↓
Score products
     ↓
Review promising candidates

The goal isn't to predict the next viral product with certainty.

It's to give developers and e-commerce teams a repeatable, data-driven way to identify products worth investigating before committing significant resources to them.

I'd be interested to hear how other developers approach this problem.

What signals would you add to a product-trend scoring system?

Building a TikTok Shop Product Research Workflow With Real-Time Data

Muhammad Irfan Khalid — Fri, 07 Aug 2026 19:27:52 +0000

Finding promising products on TikTok Shop sounds simple until you try to automate the process.

A basic product research workflow might look like this:

Find products with increasing sales
Compare sales against review volume
Analyze competitor stores
Check pricing
Check inventory
Compare different markets
Decide whether the product is worth testing

The difficult part isn't necessarily the analysis.

It's getting consistent, structured data into your application.

What Should a Product Research System Track?

A useful product research system should collect more than product names and prices.

For each product, I would want something like:

Product
├── Country
├── Category
├── Sales
├── Sales velocity
├── Reviews
├── Price
├── Variants
├── Inventory
└── Logistics

Once this information is structured, it becomes possible to build automated scoring and monitoring systems.

Finding Products With Increasing Momentum

One mistake in product research is focusing entirely on lifetime sales.

Imagine two products:

Product A
Lifetime sales: 100,000
Recent growth: Low

Product B
Lifetime sales: 5,000
Recent growth: High

Product A is clearly more established, but Product B may be more interesting for trend discovery.

This is why I prefer looking at recent sales velocity alongside total sales.

A simple application could periodically collect recent sales data and calculate how quickly a product's sales are changing.

Sales vs. Reviews

Review count provides another useful signal.

Consider:

Product A
Sales:   20,000
Reviews: 4,000

Product B
Sales:   6,000
Reviews: 200

Product B isn't automatically a better opportunity.

However, a relatively high sales volume combined with a low review count can be a useful signal that deserves further investigation.

The important thing is to combine multiple signals rather than relying on a single metric.

Competitor Analysis

Product research becomes much more useful when you look at stores rather than products individually.

For example, a developer could build a system that:

Find competitor shop
        ↓
Retrieve product catalog
        ↓
Sort products by sales
        ↓
Identify top products
        ↓
Track changes over time

This makes it easier to identify products that are becoming important to multiple successful sellers.

Country-Level Analysis

Another interesting dimension is geography.

A product can have strong competition in one country while still being relatively early in another.

Instead of looking at a global dataset, an application can filter by:

country = US
country = UK
country = DE
country = FR
country = ES
country = TH
country = MY
country = ID
country = VN

This allows developers to compare product demand between different markets.

Automating the Workflow

Manually performing this research every day doesn't scale very well.

A simple automated architecture could look like:

TikTok Shop Data
       ↓
Data Collection
       ↓
Normalization
       ↓
Database
       ↓
Product Scoring
       ↓
Dashboard / Alerts

For example, the scoring layer could consider:

Sales momentum
+ Review-to-sales relationship
+ Competitor adoption
+ Price movement
+ Inventory availability
+ Market demand

The result could then be displayed in a dashboard or sent as a daily report.

Using an API Instead of Maintaining Data Collection Infrastructure

This is one of the reasons I started working on a TikTok Shop Product Ecommerce Data API.

The idea is to provide structured product and shop intelligence through REST endpoints so developers can focus on building their applications instead of maintaining the entire data collection layer.

The API currently covers areas such as:

Rising products
Untapped products
Sales velocity
Price drops
Competitor analysis
Shop catalogs
Shop statistics
SKU inventory
Product variants
Logistics information

For example, a product research application could use a rising-products endpoint to retrieve products showing significant recent sales traction and then filter results by country and category.

A separate sales-velocity endpoint can then be used to investigate individual products in more detail.

What Could You Build With This Data?

There are many possible applications beyond a simple product finder.

For example:

Product Research Dashboard

Country
Category
Sales growth
Reviews
Price
Competition
Inventory

Competitor Monitoring

Competitor shop
      ↓
Product catalog
      ↓
Top-selling products
      ↓
Changes over time
      ↓
Alert

Market Research Tool

Country A
    ↓
Product trends
    ↓
Compare
    ↓
Country B
    ↓
Identify differences

Inventory Monitoring

SKU
 ↓
Stock level
 ↓
Threshold
 ↓
Alert

These are relatively straightforward building blocks for a larger e-commerce SaaS platform.

What I Learned

The biggest lesson for me is that data availability and data analysis are two different problems.

Having thousands of product records isn't particularly useful unless the application can turn them into actionable information.

For example:

Raw data
   ↓
Structured data
   ↓
Historical data
   ↓
Trend detection
   ↓
Decision support

The historical layer is particularly important.

A snapshot tells you what is happening now.

A time series can tell you what is changing.

And for product trend discovery, that distinction matters.

Final Thoughts

A useful TikTok Shop research system doesn't need to be complicated initially.

Start with a few reliable signals:

Recent sales
Sales velocity
Review count
Competitor products
Price
Inventory
Country

Then store the data over time and look for changes rather than relying on a single snapshot.

That's where a basic product database can evolve into a real product intelligence system.

I'm currently continuing to develop this type of API and experimenting with different ways developers can use structured e-commerce data for product research, competitor monitoring, and market analysis.