Discussing the finer points of barcode philosophy.

Ecommerce data is a woefully misunderstood industry.

Product data companies have been pigeonholed. And we want to set that right.

Conversations with well-wishers and unfamiliar parties often go along these lines—

Interested party — “So what do you do?”Us  “We provide ecommerce data. We curate a comprehensive database of ecommerce products spanning all the top retailers across the globe. And we have some of the best AI-tech in retail.”
Party — “Hmmm.. so basically your clients are retailers.”
Us — “No! Not JUST retailers”, trails off into a long winded explanation…

This is the attempt to shorten that explanation into 2 sentences.

Try removing the ‘e’ from ecommerce.


Now let me try to rephrase our utility in this chaotic world.

We are rocket fuel for companies engaged in commerce.

Well, turns out I only needed one sentence to sum it up.

First, let’s get the definition out of the way.

The Oxford University Press defines commerce asa term which means all the activities which start from production and end at consumption.”

Now let’s look at the industries that are directly involved in this whole process — retail trade, wholesale trade, manufacturing (durable and non-durable), transportation and warehousing. According to U.S. Department of Commerce, these industries alone account for more than 26% of the total GDP of the United States.

And these are all industries that could do with the rocket fuel that is good product data.

I’m not even factoring in niche use cases like finance companies looking for investment insights from retail data trends or insurance companies using this data to get the latest pricing information.

We don't own our home so we insured our Xbox - The rise of on-demand insurance / Semantics3
A look at the future of ecommerce and retail tech, based on our vast stockpile of product data and price intelligence.www.semantics3.com

I’m five years old. Why would I care about your UPC lookups and barcodes?

Okay so this question might have been rigged, but allow me to explain.

If you’re a five year old, odds are you’ve heard about Frozen and love it.

You’ve gone on and on about it.

You’ve bought Princess Anna’s dress and your blankie has Queen Elsa all over it.

You’ve also watched that movie at least thrice every week for a few weeks.

But there’s a tiny problem.

That blankie you love is a bit itchy. You’ve considered telling your parents but you’re worried they’ll take it away. And you continue using it till the itchiness gets worse, you get all red and jumpy, your parents notice and bundle you over to a doctor.

It’s very hard for buyers to differentiate fake and authentic goods.

It was a knock-off and your parents bought it because they didn’t know and because they trusted the Disney brand. You of course trusted it because it’s a Queen Elsa blanket, and you are five.

Where are the UPCs at?

A movie studio like Disney needs to monitor their brand in order to protect themselves and preserve their brand. With millions of listings for various franchises spanning many different retailers how can they keep a tab on everything being sold everywhere?

Enter Semantics3’s Match&Merged data.
Credits to Oracle.

Match&Merged is a data type that provides you product data and product listings merged from all the different retailers.

For example, suppose Disney wanted to monitor all the listings for Frozen’s Queen Elsa blankets and ensure that only authorized sellers are able to sell them; Disney could query the Semantics3 database to get the thousands of listings across the web in a standardized and structured format. Our product matching AI could further ensure that partial or low quality listing are evaluated and matched with a high confidence interval.

Over 100,000 results. Frozen sure was popular.

This central source of unified product metadata is essentially what can ensure that retailers are authorized, don’t violate minimum pricing, always have stock, and are not engaging in price inflation. They can query our Match&Merged data (available via all APIs) to keep track of all their products across all their retailers in the country.

APIs used:Match&Merged data using either UPC or Search API.
Maybe also the enterprise level Product Categorization AI.
That’s how powered up Disney could be.

It’s very reasonable to want to sleep without itchiness.

Five-year-old you would care about UPC codes (without knowing it) because it can get you a real Elsa blankie to sleep with instead of a cheap and possibly dangerous knock-off. And Disney can rest a bit easier knowing that they won’t get sued by irate parents.

Next up in the series:

How FedEx and UPS could make their customs brokers jump for joy.

Liked what you read?Checkout our retail data solutions at semantics3.com.