How does your data model impact time to market?


December 1, 2021

Incorrect or poor product information can cause buyers to click away, so make sure your data model can scale and get things right.

One of COVID-19’s legacies is that the online shift is here to stay. With the acceleration of e-commerce adoption comes truly global competition. Being first to list a product on marketplaces like Amazon can make or break a product. The faster distributors are to market, the higher the likelihood their product will get the top listing. However, distributors and manufacturers can’t afford to have poor product information, even if they are first to market. Customers have more choice than ever before and if they can’t find what they’re looking for, or if the information is incorrect, even missing – they’re gone, clicked to another site. In this always-on world, digital speed to market with engaging, accurate product information is critical to driving revenue and delivering on customer expectations.

what’s the secret to moving at digital speed?

It may not be what you first think. It’s actually your data model. In his blog, “How your data model can impact your brand uniqueness and marketability”, the author provides a great high-level introduction into the two types of data models: best-practice and elastic data model.

All PIM tools today have some sort of data model, with most vendors needing developers or some form of technical input to make their data model work. Why? Unlike inriver, those solutions don’t have an intuitive user interface (UI). One of the core benefits of our elastic data model is configuration, not customization. As well as touching on what that means for the business user, we’ll also look at how the elastic data model enforces data quality and simplifies scalability. All three combined allow you to provide a stellar customer experience at digital speed.  

breaking down the terms

Before we get started, as this is a more technical-oriented post, it’s worth just defining the terms so we’re all on the same page.

enforcing data quality

In a recent inriver survey of 6,000 online consumers, Inside the mind of an online shopper, we found that bad or inaccurate product information significantly impacted B2C shoppers. When asked to select multiple answers, 45% said it left them frustrated, while 51% reported they would shop elsewhere. So how do you prevent inadequate product information from having that effect on your customers?

This is where inheritance in your data model makes a difference. Without inheritance, you’d have to maintain all data at an item (or SKU) level. Not only is that a lot of time-consuming work, but it’s also hugely error-prone. Inheritance gives you better control. How? The elastic data model ensures that like data doesn’t have to be retyped or copy and pasted. You have consistency across the hierarchy depending on where you set the parent.

What makes the elastic data model different is that we transform the standard parent/child structure into limitless possibilities. For example, inriver has a product group item, product – item- features, and product -item – component.

how does that translate into practice?

One of our customers sells bathtubs. They have 500 different models. When they had to roll out a change to the material composition, it wasn’t a problem. Using the elastic data model’s inheritance, they could quickly change the product information for the parent models, and it was inherited down to all 500 different tub types. In addition, with the elastic data model, the customer can also make changes at a feature level, which might only affect 250 tub types. Any text, image, or video details that need to be changed is done in one place, and all those tubs associated with that feature are updated automatically across all touchpoints. Not SKU by SKU and channel by channel. Accurate product information, fast!

Not only does engaging, accurate product information mean your customers aren’t frustrated, it can also reduce returns. Inriver found that 58% of businesses report returns or customer dissatisfaction due to outdated or inconsistent product information. And according to technology company, Newmine, lost revenue for retailers, due to returns, is set to increase from USD 205 billion to USD 290 billion in 2022.

taking the stress out of scalability

With such fierce competition, for many brands, scalability is the answer to sustaining their business. Quickly adapting to the changing customer behavior by either entering new markets or expanding to new marketplaces.

For organizations growing their reach by entering new markets, this can often mean overcoming the challenge of managing product information in multiple languages. The elastic data model makes short work of languages. Simply manage all languages at an attribution level, linking the required language to the relevant product information asset. Need to add Spanish to reach your Hispanic customers? Not a problem. Add it to the list. Refresh your screen, and it’s there – no need to ask IT or developers.

how does the elastic data model help?

The elastic data model simplifies the localization that often comes with scalability. Let’s use packaging as an example. What’s in the box doesn’t necessarily change when you enter a new market or region. But the packaging, on the other hand, does vary, be it language, different images, complying to food rules or regulations across your markets, or even ensuring the warranty pdf is suitable for that country. With inriver’s elastic data model, there’s no need to make a new SKU; just regionalize the text. For example, if the weight changes, you must update the packaging. Change the weight in one place, and with SKU reconciliation, that update is inherited down. The elastic data model means you can go global at digital speed.

What about expanding into new marketplaces, be it Amazon, Lowes, or For some companies, the channel team need to wait for the product information to be published on their own website. Using that information, the channel manager then emails product management to see if they need to add specific details for that e-marketplace. Only after that, can they push the product out. When time is of the essence, having all the product information in a single source of truth speeds up time to market significantly. In addition, the elastic data model lets you set completeness rules. What does that mean? If one of the e-marketplaces is less stringent in its requirements, it is automatically shared once the product information ticks those boxes – no need to wait. With completeness rules, you’re confident that product information is only distributed if and when it meets the specific marketplace’s requirements.

putting the business user in the driver’s seat

Inriver’s elastic data model is designed with the business user in mind. There’s no customization needed. No IT or developers. It’s just configuration. What’s the benefit? Not only does it mean that you can adapt to the changing business situations in real-time, but it’s more efficient. You don’t need to wait on IT resources to test any new attribute or relationship they added to the data model. Configure, and it’s ready for use.

moving at digital speed is simple with the elastic data model

The flexibility of the inriver elastic data model means you have limitless possibilities to organize your specific product data into entities and relations, enhancing the enrichment process. As a result, companies can handle stronger, more complex product relationships and hierarchies. No matter how complex your product, our flexible entity structure means you can quickly adapt to ever-changing business and customer behavior. With inriver’s elastic data model, you have the confidence to move at digital speed, knowing that your product information is accurate and consistent across all your touchpoints. 

Want to know more about what online shoppers think about poor product information, availability, and findability? Download our latest e-book “Inside the mind of an online shopper” now.


Dan O’Connor

Senior Enterprise Solutions Architect

Dan O’Connor has been involved in product data for over 15 years, working in the retail, industrial manufacturing, and distribution verticals. During that time he has provided businesses the insight they required to enable their Product Information, Data Governance, and Taxonomy programs, as well as ensuring proper data management and integrations between data systems. His current role allows him to find creative methods to solve tough business challenges to ensure speed to market for products and capitalizing on the return on investment of product data projects.