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The Who and Why of Product Data Costs

In my last post about the costs associated with product data, I outlined various operations related to managing product data (like gathering, structuring, distribution etc.) that impact costs.

Despite the reality of these costs and the fact that they can be minimized through a collective approach, many businesses continue to operate individually when it comes to their product data needs.

In order to begin addressing this issue, I suggested that we must first examine who these businesses are and why they individually continue to extract, structure, and process product data themselves as opposed to tapping into a common well.

To get an idea of who the companies are, let’s look at the most typical uses for product data:

To Power Product Search, Discovery, and Purchase

For search engines like Google and Bing, retailers like Walmart and Amazon, or any shopping-oriented websites, devices and apps looking to expose products for sale, having or providing rich product data is a must.

Businesses with this need often default to creating their own feed management platforms, or seeding, crawling, and parsing infrastructure combined with algorithms to structure the data further such as classifying products to their taxonomy, matching identical products or enriching attributes that improve discoverability of products.

And for merchants or brands who want their products discovered and purchased, they have to deal with an ecosystem of feed management vendors, feed completeness and compliance requirements, integration nuances, and vendor/supplier communications.

To Power Analytics that Enhance One of the 4P’s (Price, Promotion, Placement, Product)

Companies like Nielsen, NPD Group, 1010Data and many others who are in the business of analyzing and providing insights related to product supply and demand, also individually collect, structure, and process product data.

They further develop industry reports or power SaaS applications that enable self-service analytics and insights for end users. Ultimately, they all want to help ensure that the right products may be manufactured or the right price and promotion may be applied to the product or the right sales channels may be chosen for the product. Additionally, people and product information are combined to ensure that the right products are marketed to the right people at the right time and place.

Given the broad range of commerce-oriented analytics, organizations in the financial sector like hedge funds and sell-side research groups also collect and use product information in their offerings.

While there are other related use cases I haven’t explicitly listed, most product data is being used by businesses to ultimately help sell more of that product or sell more of the derivative product created from the use of product information.

Now let’s look at the top three common misconceptions leading these companies to each deploy people, processes, technical infrastructure and operations costing them anywhere from several hundreds of thousands to millions of dollars annually.

  1. It’s cheaper to build it themselves: Several companies I’ve spoken with inaccurately believe that it would be cheaper to establish their own product data extraction/processing engine. What they often ignore and discover later on is that there are significant additional costs associated with maintaining such a system against an information landscape that is extremely dynamic. In addition to that, the opportunity cost of inefficiently deploying resources to areas that are not core to their mission is also not properly measured.
  2. Their data needs are unique: Each of these businesses believes that their data needs require the information to be processed a certain way or updated at a custom frequency or processed from sources that cannot be supported by a common platform. To the contrary, our finding is that most of these businesses have a great deal of overlap in their product data needs and are also inadvertently taxing data sources by each deploying embedded scripts or crawlers to their sites or having them manage the delivery and maintenance of product catalog feeds in several variations.   The combined cost of redundant information exchange is certainly not trivial when you factor in the number of data sources, the number of destinations and the number of times this information is being updated.
  3. It’s more strategically sound to keep it in-house: Relying on a third party for a key business need or participating in open information exchange networks is something many of these companies are still resisting. This may be because they feel that letting a third-party know or manage what data they need and use could be a disadvantage. Again, I would counter that at a time when data of all kinds is pervasive and plentiful, the true strategic advantage lies in how uniquely, efficiently and effectively value can be extracted from the data and not in owning the information exchange platform or the operations for gathering and processing data.

I certainly don’t believe that these reasons for the current mode of operations will fade away immediately. I don’t even believe that many would agree with me that they are eroding value with their current practices.

What I do hope is that a more open dialogue can take place about how we can all enable product information to be exchanged more freely, centrally and with universal contributors and consumers enhancing the value of product information through a collective and networked marketplace.

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