Performance in the retail industry heavily depends on product data. On the consumer side, it’s what they see and research before buying a product, and what impacts their experience on a D2C retailer website or another channel. In fact, product information can make or break a purchase decision. On the retailer side, it’s about collecting the data from hundreds or thousands of suppliers, and vice versa suppliers dealing with different retailer requirements to provide their product data in the right format. 

Even though product data is central to the collaboration between retailers and suppliers, data quality is often poor. This phenomenon is linked to various factors: the data may be erroneous, incomplete, inconsistent or obsolete. 

Poor data quality has numerous consequences.  Some of these can be quantified, for example with fines and penalties a retailer might be subject to if they do not comply with regulations, or lost time and cost due to the time spent by a team correcting or completing product data manually. 

However, other costs are more complex to measure, like the number of lost sales caused by incorrect or incomplete information on a product page. According to Salsify’s 2021 consumer survey, 70% of shoppers indicated that lacking product information was the main reason for leaving a product page. Other consequences are directly attributable to poor quality product data: the increase in returned products, the time spent by teams checking product data and their dissatisfaction.

The financial cost of non-compliance

So what does bad data cost? According to Harvard Business Review, a task performed with erroneous data incurs a cost 100 times greater than if the data had been correct. This study is based on the 1-10-100 rule, developed by George Labovitz and Yu Sang Chang in 1992: applied to the problem of data quality, this rule follows a relentless logic illustrating the importance of maintaining a high level of data quality, on a continuous basis and not occasionally. It costs the company $1 to check the quality of a piece of data – this is the cost of prevention. Cleaning up the data (e.g. removing a duplicate) costs $10 – that is the cost of correction. Unverified data costs $100 – this is the cost of failure. 

According to Gartner, poor data quality costs companies in all industries an average of $12.9 million, every year. 

Non-compliance can be highly damaging. As non-compliance of product data represents a costly risk for consumer goods companies, it mobilises entire teams within an organisation, sometimes cross-functionally. Forrester estimates that a company saves €112,000 over 5 years by avoiding non-compliance penalties. Although the supplier is the product expert and therefore the master of its own product data, the risk lies with the retailer who is considered as the distributor of the information. Penalties for non-compliant labelling can quickly eat into their margins: the European INCO regulation for example includes criminal sanctions and fines of several hundred euros per outlet for each non-compliant label.

Dissatisfied teams

The product data management process involves different teams on the retailer and the supplier side. Controlling the data quality involves these same teams, or at least a large part of them. The following teams at retailers have their role to play in terms of handling product data, and more specifically in collecting it:

• The supply chain teams collect logistical information: stacking, dimensions, weights, crushing indicators, etc.
• Purchasing teams collect prices, discounts, referencing data, etc.
• Ecommerce teams collect product information in compliance with regulations.
• Marketing teams need content dedicated to promotions, leaflets, sales highlights and other attractive content on the product and its use (recipes, product tutorials, etc.).
• The quality teams monitor compliance and follow the various regulatory developments: scores, commitments on working conditions, information on materials and recyclability of packaging, BCorp or PME+ social and environmental performance certification, etc.
• Category managers collect data for product launches.
• Local referencing teams meet product demands, and also provide information to shops, which deploy screens, electronic labels and more recently tablets, i.e. solutions that are hungry for product content (instructions for use, advice, etc.) to better inform employees and consumers.
• Shop teams look for millimetre-accurate dimensional data and good quality visuals to produce planograms automatically

These siloed organisations lead to many fragmented exchanges between retailers and suppliers, or within the different retailer teams. These exchanges are often manual, time-consuming and error-prone, and the use of a multitude of tools duplicates effort and makes control processes inefficient. 

This is the case, for example, when there are different control and verification tools in place, with controls performed in different places at different times (PIM, emails, Excel files, own systems, dedicated media tools, etc.). 

Such archaic tools require regular updates to support the increasing data volume and sometimes maintenance operations. These factors have a significant negative impact on team productivity, and can even affect team motivation. As internal Salsify data shows, there is a 56% annual increase in the volume of data produced. This means that every two years, the volume of data doubles. This constant growth in the data volume means that more time and control tasks are needed, being an additional burden for the teams.

The disappointed end consumer

Incorrect information on a product can have different consequences on the consumer side: a dissatisfied customer may return the product because the dimensions of the furniture he or she  ordered were incorrect – or a customer may be unhappy and upset because the presence of an allergen was not indicated. In this case, the error may even have consequences that could affect the health of the consumer and bring along complaints or even lawsuits. 

Incomplete information will be directly sanctioned by the consumer. Data from Salsify's 2022 Consumer Research indicates that 46% of British consumers abandon their online shopping cart due to a lack of relevant product information. 

Moreover, today's consumers are more demanding: in an omnichannel mode, whose codes they have perfectly adapted, they follow a non-linear and discontinuous path. They expect to find the same information about a product, whether they are searching on a social network, on a brand's application or in a shop. According to Glady, only 29% of consumers consider that their experience is consistent across all channels. 

Finally, incorrect information leads to more product returns. According to Statista, between 5% and 15% of purchased items are returned by shoppers in the US, 25% for clothing, followed by shoes (15%). By 2021, the costs associated with returns alone have been estimated at $550 billion, a 75% increase over 2016. 

Missing or incorrect product information will not only result in a lost sale. It will undermine the trust that the consumer has in the retailer or the brand. This trust is essential to amplify or even create growth. 

If you wish to learn more about how to reduce the costs of poor product data quality, the Salsify team is happy to answer your questions: just click on the ‘request a demo’ button on the Salsify website.