What is Product Data Science?

In the changing world of pervasive commerce, we talk about how businesses need to be able to deliver targeted and personalized product offers. They need to deliver the right product offer to the right person at the right place, at the right time, and through the right channel. With millions of products and infinite touch points at which businesses can connect with consumers, surely this task cannot be accomplished with guesswork. There is a definite science behind it.


Along with customer intelligence (deep knowledge about consumer shopping habits, browsing behavior, preferences etc.), the piece that completes this equation is product intelligence, which is mainly informed by product data science. At Indix, that’s our secret sauce.

What is data science? It is the perfect synchronization of knowing what to look for, and then understanding how to get at it. Talking about optimizing “big data” is as ubiquitous as breathing in the business world these days. It requires a deep understanding of data science in order to make sense of all the big data that we are collecting. For instance, let’s consider what a company like Knewton is doing in the education field. They analyze large amounts of data related to students’ progress to generate tests that are tailored to each student’s need. So they are collecting the data, structuring it, organizing it, deriving meaning from it, and making it actionable and user-friendly for teachers.

At Indix, our domain expertise is in the area of any and all product-related information. Hence, product data science. We collect all the product-related information on the Internet that is in the public domain. This includes offers data like price, promotions, availability, channels, and catalog data like attributes, features, tags, etc. Then, by applying patented algorithms, all this data is structured, normalized, and organized into databases.

Of course, our work doesn’t end here. This organized data then needs to be made actionable. By applying another set of patented algorithms, we analyze and draw insights from this data. By doing this, we enable businesses to take informed decisions. Unstructured data is not very usable. Effective analytics extracts insights and drives desirable outcomes, whether it’s revenue, profit, productivity or brand growth.

The structured data is then presented in a visual manner for ready comprehension by any individual in an organization. We look for patterns and compute different permutations and combinations. Insights are personalized based on how they relate to a person’s role. And the icing on the cake? All this is done at scale and in real time. No more scouring the Internet for hours and days. All our data can be accessed through a beautiful cloud-based product intelligence application or API.

Indix is building the world’s largest database of products to help businesses deliver the right product information to the right person at the right place, every time. Our efforts in data science and decision science are informed by a deep understanding of product information and the business outcomes desired by companies that make products or sell products or deal with product information. Product data science makes data usable in a way that actually moves the needle when it comes to business goals.

So, there it is. No more guess work. Companies can now generate delightful product offers that are backed by a solid science.

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2 thoughts on “What is Product Data Science?”

  1. abhishek sakhapariya says:

    Interesting Produce data science Knewton is doing; and Indix providing services to them, maybe. One question though how much data is being processed for each student and how is it actually collected and the right factor identified to derive insights?

    1. Anu Hastings says:

      Hi Abhishek,

      Thanks for your question. We actually don’t provide any services to Knewton. That was just used as an example in the post. Hence, I can’t give you more details on their processes. Let me know if you have any other questions. Thank you!

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