We’ve all experienced the thrill of a perfectly timed Netflix recommendation or the joy of discovering a new favorite song in your Spotify playlist. It almost feels like these platforms know you better than anyone. Well, in a way, they do – thanks to their exceptional data management practices. Netflix’s ability to predict user preferences drives more than 80% of viewer activity and Spotify’s Discover Weekly playlist generates 1.9 billion new listens every day.

The retail landscape is no different.

With research showing that a staggering 75% of shoppers willing to pay more for a personalized shopping experience, retailers can tap into the same magic and create that “wow” factor. How, you may ask? By improving their data quality!

Data is at the heart of it all. It fuels personalization, predicts buying behaviors, and ensures every interaction feels tailored. But if that data is flawed, the results can be disastrous. Clean data is the foundation of a winning retail strategy. Let’s explore how and why it matters more than ever.

The Role of Clean Data in Retail

Today’s consumers have endless choices when making purchases, but they often end up preferring one brand over the other. This can happen due to a number of factors, such as accurate product recommendations, timely delivery, better pricing, product availability, and personalized experiences. And behind these factors lies the data.

Most retailers recognize data as their most important organizational asset and believe it to be the main fuel that drives everything from customer satisfaction to operational efficiency. However, to fully leverage their data assets, retailers must adhere to data quality standards that ensure the information used for decision-making is accurate and consistent.

Let’s dive into how clean data enables retailers to excel in these areas:

1. Offering Accurate Product Recommendations

If you’re a retail brand, you want to sell more to both your new and returning customers. A very common and effective way to do that is by displaying personalized product recommendations. These recommendations are based on factors like:

• Products typically bought together
• Items similar in nature
• Items recently purchased by customers with similar demographics or profiles
• Products previously viewed by the shopper

Despite the clear value of personalized recommendations, fewer than 20% of retailers provide them, according to the 2023 Unified Commerce Benchmark for Specialty Retail study. This shortfall is often due to poor data quality.

Accurate product recommendations depend on accurate data analysis, which is only possible if you have reliable data, such as the exact number and types of interactions customers have with your brand across multiple touchpoints. Along with it, these recommendations also rely on the accuracy of your product descriptions, since you cannot identify the correct relationship between two products if the data used to compare them is incorrect. Without clean, integrated, accurate, and reliable product and customer data, even the best algorithms can fail. Data consistency plays a key role in ensuring these recommendations make sense across different channels.

2. Ensuring Timely Deliveries

Few things delight a customer more than a package delivered to the right address at the right time without any delay. However, for retailers, this is one of the biggest challenges. A survey conducted in 2023 revealed 70 percent of consumers have experienced a shipping delay in the last six months.

The ambiguity or inaccuracy of a retailer’s customer contact dataset often makes it difficult to deliver packages to the right address. And it happens more often than we think. In a survey, 41% of businesses admitted that poor physical address data causes them problems. The research further revealed that when addresses are incomplete or inaccurate, 41% of deliveries get delayed and 39% simply fail.

Retailers usually treat their address datasets with data governance strategies, like address standardization and address verification techniques to improve data quality and ensure that each customer address specifies a physical, mailable location. Standardized and verified datasets help retailers to ensure products are delivered to the customer’s doorstep at the right time.

3. Predicting Consumer Behavior and Trends for Better Assortment Planning

Assortment planning focuses on choosing the right breadth (product categories) and depth (product variation within each category) for your retail store at a given time, keeping consumer behavior and market trends in mind. In other words, retail assortment planning requires understanding what products should be available at a given time and location. It is not something that is generalized for your entire retail brand, rather it is specific to the store and region of your outlet.

The success of any retail store or brand depends on the strength of their assortment, and assortment is best planned by performing trend analysis on past data about consumer behavior and market demand. However, the effectiveness of these decisions hinges on data usage within the organization. Major retailers use advanced data analytics tools that gather not just their own but their competitors’ data as well at different locations – in-store and online. But data captured in this way – from a number of scattered sources and vendors – is often not present in the most optimal form and shape to be used for analysis. Basing the decisions of assortment on bad data can cause a retailer to lose a lot of time and money. Therefore, the quality of data used to plan inventory is another important aspect to consider.

4. Leveraging Personalized Customer Experiences

Personalized experiences are now the standard. To create them, retailers must understand:

• Who their customers are (including the correct and accurate information about their demographics).
• What they are interested in.
• What they buy and why.

Knowing your customer in terms of their demographics, preferences, and shopping behavior can help a retailer to brand experiences that speak to a specific customer segment. This is possible when data consumers (such as marketing teams or customer experience managers) have access to accurate and unique data about each customer.

However, many retailers struggle with fragmented data. Since consumers interact with brands through different channels, retailers mostly have multiple records for the same individual. Such scenarios must be handled by integrating and unifying data at one place and making sure that the entire organization references the single source of truth for all intended purposes. These data initiatives not only allow retailers to offer personalized communication but also enable omnichannel experiences for their customers (across all channels; in-store, ecommerce websites, or social media platforms) – no matter where the customer is on their buying journey.

5. Establishing Competitive Pricing

Competitive pricing is all about collecting the price points set by your competitors for a certain product category and comparing them against your own price points for the same category. This analysis helps you to establish competitively better prices for your products and make sure that you are not losing customers to competitors based on unrealistic price differences.

The data about your customer’s prices is collected from a number of sources, such as surveying consumers or gathering it from resellers, vendors, or digital shelf analytics tools. The quality of this data is mostly unreliable – it might be correct in meaning, but there are inconsistencies in how it is represented (often, there are varying representations for the same type of data).

Misrepresentations in pricing data can lead to incorrect price adjustments. As a result, the retailers may incur losses by selling inventory at inaccurate price rates. Clean, standardized data is essential for accurate competitive pricing analysis.

6. Identifying Upselling and Cross-Selling Opportunities

Upselling involves encouraging customers to buy more advanced or feature-rich versions of the products they’re considering, while cross-selling means suggesting additional products (complementary items) depending on what is usually brought together to enhance their original purchase. Both these techniques are highly effective in helping retailers sell more to existing customers, but they require accurate insights into customer behavior and product relationships.

By analyzing clean sales and product data, retailers can identify patterns and take advantage of usually take advantage of upselling and cross-selling opportunities by offering promotional deals where similar or contrasting products are sold together.

Indicators of Poor Data Quality for Retailers

The role of clean, quality data in the world of retail is fairly obvious. Retailers want to ensure that their customer, product, location, and other datasets have acceptable levels of quality. But the reality is that these datasets mostly have hidden data quality issues that may not be clearly visible. Brands often experience a number of data challenges in retail that occur as a result of poor data quality.

In this section, we will look at some common problems faced by retailers that actually indicate bad data quality.

1. High Product Return Rates

Retailers with poor quality datasets tend to experience higher product return rates. This issue typically arises from two scenarios: incorrect product deliveries or wrong addresses. Both these cases are signs of inaccurate or unreliable data being used to showcase products or delivering them. Clean, validated data ensures that right products are delivered to correct addresses in the very first attempt and thus, helps reduce return rates that cost retailers hundreds of billions every year.

2. Lack of Customer Personalization

Another indicator of poor data quality for retailers is when they struggle to offer personalized experiences to their customers across different channels. This often comes up in different forms, such as the same promotional email being sent multiple times to a customer or being unable to identify a customer’s preferences and suggest products accordingly. A lack of data consistency across customer profiles can lead to fragmented personalization efforts, which alienates customers and diminishes brand loyalty.

3. Phantom Inventory or Stockouts

Phantom inventory refers to the situation when goods appear available in inventory systems but are actually out of stock. Conversely, phantom stockout is when retail goods are listed as out of stock when they are actually available. Both these discrepancies arise from inaccurate product or sales data, which represents stock levels. This leads to poor customer experiences and lost sales opportunities.  

4. Price Inaccuracy

Are you losing customers to competitors just because they offer products at slightly lower rates? Or are you winning customers over by showcasing the same products at considerably lower price rates? Both these situations are a sign of poor pricing strategy and the inability to use data to make better pricing decisions. Inaccurate pricing data makes it difficult to establish competitive, market-aligned prices. This can lead retailers to miss sales opportunities and, ultimately, suffer revenue losses.

5. Inefficient Inventory Planning

Inventory planning depends on multiple factors, such as market demand and customer requirements. When retailers fail to plan their inventory effectively, there is a high chance that the datasets used to forecast and estimate inventory requirements are not reliable and accurate. Clean, well-governed data is essential for making informed decisions about stock levels, minimizing the risks of overstocking or stockouts, and optimizing supply chain efficiency.

6. Reduced Market Share

Are you selling less as compared to your competitors in the same market? There can be many reasons for this, but a common problem faced by retailers in such cases is being unable to leverage data for whitespace analysis or to uncover hidden market opportunities by using reliable data insights. This may be encountered in the form of reduced sales, losing money, or a decreasing market share in the industry.

Wrap up

And there you have it – the most common reasons for investing in better data quality management practices for your retail brand. Our next blog goes into more detail about what exact data in retail looks like, the common data quality issues encountered in retail data and how you can fix them. Take a look at it to learn more or download a free trial of our retail data quality management tool today to see how retail DQM can help improve data quality in retail by assisting you with data governance policies and set you on the path to achieve total brand excellence.