It’s undeniable that poor quality data costs businesses money. In fact, according to Gartner, poor data quality costs organisations an average of US$12.9 million each year.
The reverse is also true: Good quality, optimised data can make business money. McKinsey reports that the 25 top-performing retailers, most of which have harnessed the power of digital, data, and analytics, represented more than 90% of the sector’s increase in global market capitalisation during the pandemic.
A critical issue that contributes to these figures is the impact that bad retail data has on the customer experience and customer satisfaction, and vice versa. But there are strategies for improving retail data management and solving retail data problems that help retailers embrace the benefits of optimised data.
Bad data leading to retail pricing inaccuracies
Large retailers typically make between 2,000 and 4,000 price and promotion changes each week, increasing and decreasing unit prices or implementing promotions such as buy-one-get-one-free or three-for-the-price-of-two. There are also markdowns for items that are on clearance or close to their use by dates.
Data synchronisation issues can prevent these changes from being updated across all a retailer’s systems, leading to pricing inaccuracies.
The issue is significant. Inaccurate or misleading prices leave retailers open to criminal prosecution and associated reputational damage.
There are also consumer satisfaction issues when a customer sees one price on the shelf-edge but is charged another at checkout. If the price charged is higher, the customer is infuriated. It may lead them to abandon the purchase and will certainly have a negative impact on their perception of the retailer. Even if the price charged is lower, there can still be reputational issues because it raises questions of accuracy.
Bad data contributing to on-shelf availability problems
Bad data undoubtedly leads to retail inventory management issues and creates on-shelf availability problems.
Stock-outs are estimated to account for US$1.2 trillion in lost sales, representing the value of items customers intended to purchase but could not because they were unavailable. Overstocking cost US$562 billion, driven by discounts to clear excess inventory or losses due to spoilage.
Aside from the direct cost of this inventory distortion, there are also customer satisfaction issues. Finding an item out of stock is frustrating for a customer. Too many discounted items can also cause issues, negatively affecting customers’ perception of a retailer.
Bad data resulting in loyalty scheme errors
When loyalty systems fail to synchronise correctly with checkouts, this can lead to loyalty points, relevant discounts, or promotions not being applied. Special loyalty-based pricing may also not work correctly if any of the items have been incorrectly configured within range or pricing systems.
Inaccurate loyalty program data can frustrate customers and lower satisfaction when they are unable to redeem points or use vouchers they believe they have earned. On the flip side, discounts, points, and rewards given incorrectly in customers’ favour can significantly impact retailer profitability.
The problems go beyond pricing and discounts too. According to the study “The State of Brand Loyalty in the U.S. in 2023,” over a quarter of consumers want their loyalty card membership to unlock personalised product recommendations based on their self-reported preferences. If this data is inaccurate or unavailable, retailers miss out on lucrative opportunities to boost customer loyalty and satisfaction.
Bad data causing checkout issues
All the issues above reveal themselves at checkout. They’re also just the start of the data issues that might not be obvious upstream but will manifest themselves when customers reach the checkout stage.
In addition to pricing issues, discrepancies can arise between a store’s systems and the central range management system. These may include incorrect product data or attributes within the range management system or unrecognised barcodes.
All these issues damage customer satisfaction because they require staff intervention that slows the process down.
It matters because, according to mobile scanning company Anyline, 88% of shoppers want a faster checkout experience, and seven minutes is the longest many will wait before abandoning their purchase.
It is also true that in a sector built on wafer-thin margins, even the smallest dent in productivity at checkout can have outsized consequences when totalled up across retailers’ stores.
What holds retailers back from improving data quality
Of course, the industry is aware of all these data issues. Research cited by Harvard Business Review found that retailers’ biggest problem is data quality and data management. They recognise that information is siloed and not managed in an organised way.
A retailer may have 700 or 800 systems embedded in their setup, and they generate a lot of data. According to research cited by EPAM, the retail industry generated 149 zettabytes of data in 2024. (One zettabyte equals one billion terabytes.) Worse, studies suggest retail data accuracy is low.
On average, Harvard Business Review has noted research indicating that 47% of newly created data records contain at least one critical error. When end-to-end workflows that connect multiple systems are commonplace, the problems quickly compound.
If these are the factors that hold retailers back, what needs to change?
Strategies for improving retail data management and solving retail data problems
If retailers want to improve retail data management, tackle their retail data quality issues, and deliver tangible results, they need to rethink the way they work. Better data governance is a fundamental requirement, with everyone in an organisation understanding the role they have to play in optimising and enhancing the quality of the data that is produced.
At the same time, it’s essential to recognise that traditional manual methods for ensuring data quality are no longer viable in a world where there are 800 systems in use and 149 zettabytes of data being generated annually.
AI-driven tools can provide coverage that a manual approach can never realistically achieve. For example, a comprehensive regression test can be performed to verify that changes in the central range management system have been properly reflected in downstream systems. It can also validate updates to loyalty systems to ensure they function as intended and can check data or other environmental factors that affect quality.
Retailers can also draw on external expertise to design, execute, and optimise test strategies that support data management and drive improvements.
The strategic imperative for modern retailers
Optimising retail data quality is no longer just a technical necessity; it’s a strategic imperative for enhancing customer satisfaction and sustaining profitability. From pricing accuracy to inventory management and loyalty program effectiveness, the impacts of bad data are profound and far-reaching. Retailers that invest in advanced data management strategies and adopt modern tools, including AI-based approaches, can tackle these challenges more effectively.
By adopting better data governance practices and modern automation solutions, retailers can ensure data consistency, eliminate inefficiencies, and deliver seamless customer experiences. As the retail landscape continues to evolve, those who prioritise data accuracy and integrity will not only improve operational outcomes but also build lasting customer trust and loyalty in an increasingly competitive market.
