It’s undeniable that bad quality data costs business money. In fact, according to Gartner, poor data quality costs organizations an average $12.9 million each year.
The reverse is also true: good quality, optimized 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 capitalization 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. In this article, we explore some of the ways in which bad retail data integrity leads to a poor customer experience and lower customer satisfaction. We also consider strategies for improving retail data management and solving retail data problems that help retailers embrace the benefits of optimized 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 synchronization issues can prevent these changes from being updated across all a retailer’s systems, leading to pricing inaccuracies. The issue is significant. For example, industry magazine RN reports that nearly half of convenience stores and newsagents have inaccurate and misleading pricing.
Inaccurate or misleading prices leave retailers open to criminal prosecution, and the associated reputational damage, of course.
But there are consumer satisfaction issues too when a consumer sees one price on the shelf-edge but is charged another price 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.
IHL Group, a global research and advisory firm for the retail and hospitality industries, estimates that inventory distortion cost the industry $1.77 trillion in 2023. Out-of-stocks are estimated to account for $1.2 trillion in lost sales, representing the value of items customers intended to purchase but couldn’t because they were unavailable. Overstocks cost $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 correctly synchronize 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’ favor can significantly impact retailer profitability.
The problems go beyond pricing and discounts too. Over a quarter of consumers want their loyalty card membership to unlock personalized 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 Check-Out Issues
All the issues above reveal themselves at check-out. They’re also just the start of the data issues that might not be obvious upstream but will manifest themselves when customers reach the check-out 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 unrecognized barcodes.
All these issues damage customer satisfaction because they require staff intervention that slows the process down.
It matters because research shows 88% of shoppers want a faster checkout experience and seven minutes is the longest many shoppers will wait before abandoning their purchase and walking out.
It is also true that in a sector built on wafer-thin margins, even the smallest dent in productivity at check-out can have outsize consequences when totaled up across a retailers’ stores.
What Holds Retailers Back from Improving Data Quality
Of course, the industry is aware of all these data issues. Retailers tell researchers that their biggest problem is data quality and data management. They recognize that information is siloed and not managed in an organized way.
A retailer may have 700 or 800 systems embedded in their setup, and they generate a lot of data. It’s estimated the retail industry will generate 149 zettabytes of data in 2024. (One zettabyte equals one billion terabytes.) Worse, research suggests retail data accuracy is low. On average, 47% of newly-created data records will have at least one critical error. And 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.
Our own experience of working with retailers shows us there are several factors that influence how successful a retailer is at improving retail data accuracy and therefore customer satisfaction levels.
Better data governance is a fundamental requirement, with everyone in an organization understanding the role they have to play in optimizing and enhancing the quality of the data that is produced. Our latest whitepaper, Where Does the Responsibility for Quality Sit?, explores this subject in more detail.
At the same time, it’s essential to recognize 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.
Indeed, Eggplant’s AI-driven tools provide coverage that a manual approach can never realistically achieve.
For example, Eggplant can perform a comprehensive regression test 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 the expertise of Keysight’s subject matter experts, who will work closely with their teams to design, execute, and optimize test strategies that support data optimization and drive the improvements required.
The strategic imperative for modern retailers
Optimizing retail data quality is no longer just a technical necessity—it’s a strategic imperative for enhancing customer satisfaction and driving profitability. From pricing accuracy to inventory management and loyalty program effectiveness, the impacts of bad data are profound and far-reaching. Retailers who invest in advanced data management strategies and leverage tools like Eggplant’s AI-driven testing solutions can tackle these challenges head-on.
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 prioritize data accuracy and integrity will not only improve operational outcomes but also build lasting customer trust and loyalty in an increasingly competitive market