Product Data Quality: Moving from a Chaotic to a Preventative State

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Product Data Quality: Moving from a Chaotic to a Preventative State

By Susan Pichoff, GS1 US - 07/24/2019

With a properly set up product information framework, retail brands can start translating data quality actions into tangible revenue gains and cost savings. If you’re Target, that can add up to millions. 

In recent years, there has been a noticeable shift in the way the retail industry values product image and attribute data, all driven by the empowered consumer. Many companies have learned the hard way that consumers will abandon loyalty much sooner in the digital age — so the idea of enhancing product listings to be more accurate, complete and consistent evolved from “it’s part of our future plans” to “it’s a priority right now.”

Accurate product information has become a strategic asset — even a growth driver in some respects. Improving this data has a ripple effect that can be felt from the manufacturing site all the way through to accounts payable. Not only are supply chain efficiencies improved, but solid ROI can be gained from more efficient truck and warehouse loading, not to mention increased consumer loyalty and the reduction of costly returns.

Think about it — how does a consumer feel when they search for a product they want and find the most obvious errors in the online listing? One customer commented in a product review for a watch “Check the ridiculous dimensions you have listed for this watch. 32.2" wide?? Come on. What is the diameter of the watch face?”

In today’s highly competitive retail market, retailers and their partners need to present their absolute best product information to the consumer to win the sale. Make no mistake — these savvy shoppers now have the ability to move on quickly to compare prices and product details. Plus, they won’t hesitate to share bad experiences in product reviews, and provide details on the many ways a product did not meet their expectations.

Now is the time for the prioritization of product data quality programs before consumer trust further erodes. Through industry collaboration based on GS1 Standards, retailers can trust the data they receive from their suppliers and use it to fuel sales growth. Additionally, suppliers can leverage standards-based data management systems to consolidate efforts and use one universal way to communicate master product data with their retailer partners.

Participants in the GS1 US National Data Quality Program have found standards are key to lifting their data management out of an unproductive chaotic state. Here are some lessons from those who have had success.

The data quality maturity curve

If you are a retail brand just starting out on your journey toward improved data quality, there are four stages of maturity of which you should stay aware: Chaos, Reactive, Proactive, and Preventative.

The stages are as they sound — the chaotic end of the spectrum means product information is fragmented and incomplete. The exchange of data is inconsistent by trading partner, leaving too much room for manual errors. Moving through the stages, product data becomes more organized, with data governance in place to ensure repeatable, standardized processes yield better outcomes. The ideal stage — Preventative — is where retailers are no longer “cleaning up” data but working to get ahead of any potential issues before they happen.

Retailers participating in the GS1 US National Data Quality Program have found it easier to manage their movement through these stages when they focus on a key data quality metric, such as issues per item. As a microcosm of the entire product line, this metric helps the retailer see the scope and type of product data quality issues that need attention. They can then share these learnings with their suppliers for a more collaborative approach to maintaining quality.

Root causes and repeat offenders

Some GS1 US National Data Quality Program retail participants recently shared a set of commonly found product information errors, including:

·       Item dimensions: As stated in the previous watch diameter example, measurements that are erroneous, or not based on real product (only product specs), are likely to wreak havoc not only on customer experience, but also on logistics. Accurate freight bid calculations depend on correct product data in order to optimize shipment handling and costs savings.

·       Apparel material percentages: It seems like simple math, but some retailers have reportedly struggled with labels that show material breakdowns that do not equal 100 percent. For example, a 60 percent cotton and 30 percent polyester blend could be a problem flagged in data quality program systems, where rules are set to signal 100 percent material sums.

·       Import designation: Retailer systems ensure that a “Made in the USA” label matches that “United States” listing in the Country of Origin product data field.

Errors such as these can slow down the retail supply chain and cause retailers and brands to lose out on sales. GS1 US has resources to help the industry standardize how product details are defined and shared, which helps forward-thinking companies move closer to eliminating the manual and disjointed processes that are the root cause of product information inaccuracies.  

The ROI of data quality programs

With a properly set up product information framework, retail brands can start translating data quality actions into tangible revenue gains and cost savings.

“We have experienced increased conversion, meaning our customers looked at a product online — a product on which we had improved data quality — and they bought it at a higher rate,” said Andy Nash, lead product owner, Target. “That goes into the bank, making it about real dollars, not just metrics.”

In a recent case study, the team from Target explained how they quantified the cost of fixing a data issue when it arises, including the time it requires to find a ticket, research it, and then fix it. Based on the number of issue incidents and the time it takes to resolve them, the cost of fixing data quality issues can easily run into the millions of dollars, not just in labor costs, but also in lost sales.

“We’re actually correcting less data, which is really good. We don’t have fewer items. We actually have more, but we’re having to correct less data. It’s metrics like this that give a time value, a dollar value to it as well,” Nash says.

As consumers become more empowered by access to information, the retail community should similarly be empowered to clean up product attribute data to support sales growth. Without standards-based processes to correct basic master product data, retailers will likely never be able to get to the higher value data analytics that help them adapt quickly to meet customers’ preferences and expectations. Looking into the future of retail, as the demand for Internet of Things (IoT) replenishment increases, having a formal GS1 Standards-based data quality program must be prioritized so that accurate and complete product information can fuel many disparate, autonomous systems. What the consumer sets as their preference must be what they receive on their doorsteps or retailers and brands will lose out to competitors that have perfected this process.  

Take advantage now of the consistency that standards-based systems offer to gain valuable ROI, win customer loyalty, and get the highest level of operational effectiveness. 

Susan Pichoff leads the GS1 US Apparel and General Merchandise Initiative — a collaborative industry group that works to solve supply chain and e-commerce challenges through trading partner adherence to GS1 Standards. She guides the implementation of solutions that help industry improve data accuracy, inventory visibility and supply chain efficiency to support omnichannel success and other emerging retail innovations.