Bad product data is costing businesses millions. Each year, companies lose an average of $9.7 million due to inaccurate or incomplete product information. Worse, 40% of consumers return items because of incorrect details, and 86% say they won’t shop again with retailers who provide bad data.
Here’s what poor product data does:
- Lost Sales: Customers abandon purchases when product details are missing or wrong.
- Higher Returns: $890 billion in returns in 2024 alone, with 31% due to misdescribed items.
- Wasted Resources: Employees spend up to 50% of their time fixing data errors.
- Growth Limits: Slow product launches and channel updates stifle business expansion.
The solution? A 4-step plan:
- Set Data Rules: Assign ownership and create clear standards.
- Centralize Data: Use a single source of truth for accuracy.
- Organize Categories: Standardize naming and attributes for clarity.
- Add Quality Checks: Automate error detection and conduct regular audits.
Fixing product data boosts sales, reduces returns, and builds customer trust. Start now to avoid costly mistakes and grow your business.
Real Costs of Poor Product Data
Lost Sales Due to Missing Information
Bad product data directly impacts sales. Some industries report losses reaching up to $9.7 million annually due to data issues. When customers encounter incomplete or conflicting product details, they’re less likely to take a chance and more likely to shop elsewhere.
Take ABC Inc., for example. The company saw a drop in sales because of inconsistent drill press specifications – listing a 1.5-inch drilling capacity online while catalogs claimed 2 inches. Beyond losing immediate sales, such errors also damage long-term customer trust.
Customer Trust and Return Rates
Incorrect product information not only causes confusion but also leads to higher return rates and erodes trust. In 2024, U.S. consumers returned $890 billion worth of products, with 31% of those returns attributed to misdescribed items.
"What ends up happening is, a customer orders a product, and when it arrives, it’s the wrong color or size or compatibility, because there was incorrect or missing information online. It’s an immediate return, and can also result in low ratings and reviews, which impact future sales." – Josh Wayne, VP of commerce products at TrueCommerce
The consequences don’t stop with a single return. A negative experience can have lasting effects – 67% of shoppers say they avoid buying from the same company again after a bad return experience.
Staff Time and Resource Waste
Flawed data drains company resources. Employees often spend up to half their time fixing errors and tracking down missing information. This inefficiency not only drives up labor costs but also delays critical projects like product launches.
For example, a Harvard Business School study revealed that managers manually corrected 84% of 99 million retail shifts due to bad data. Such inefficiencies can also slow down efforts to expand into new markets.
Growth and Market Expansion Limits
The ripple effect of poor data management stifles growth. Studies show:
- 45% of businesses take 6–11 months to update product details for new sales channels.
- 21% need 1–2 years to complete these updates.
- 70% spend two weeks just collecting supplier product data.
"The entire product information supply chain is ripe with inefficiencies and errors. For companies in highly regulated industries, this could be a huge cost, but even for others, the financial impact is real. If you send product to a Home Depot and you’ve mischaracterized the shelf specifications, that product is not only not sitting on the shelf, but Home Depot is going to turn around and fine you for submitting incorrect product specifications." – Steve Gershik, CMO at inRiver
Compounding the issue, 41% of B2B leaders report a lack of skilled data professionals, and 34% cite limited tech budgets as a barrier. These challenges make scaling operations even harder.
Why Product Data is a Distributor’s Biggest Problem with …
Warning Signs of Data Problems
Issues with product data often reveal themselves through clear warning signs. Spotting these early can help avoid revenue losses and operational headaches.
High Returns and Low Sales
High return rates often point to data problems. In 2023, U.S. consumers returned $743 billion worth of merchandise, accounting for 14.5% of total sales. Additionally, 40% of online shoppers have returned products due to incorrect or incomplete product information.
Poor-quality data can also hurt performance metrics:
- Click-through rates drop by 23%
- Conversion rates fall by 14%
- Cart abandonment increases by 30%
These numbers highlight how data issues can ripple through your entire sales process.
Common Customer Questions
Frequent customer inquiries about basic product details suggest gaps in your data. Research shows 42% of shoppers abandon purchases when product information is incomplete.
Some common areas where data falls short include:
- Missing specifications
- Unclear compatibility details
- Incomplete sizing information
- Undefined material composition
These gaps undermine trust and make it harder for customers to complete their purchases.
Data Mismatches Between Channels
Consistency is key. When product information varies across sales channels, it can erode customer trust. In fact, 87% of shoppers are unlikely to return to retailers with inconsistent data.
Here’s the impact of consistent data:
- Companies see a 36% boost in customer retention
- 39% of shoppers avoid retailers after encountering inconsistent information
- 6.75% of active customers disengage due to data-related errors
Aligning product data across channels is critical for maintaining customer loyalty.
Slow Product Launch Times
Delays in launching new products often tie back to poor data management. For example, a home and living manufacturer that centralized its product information saw dramatic improvements:
- 55% faster time-to-market
- 30% reduction in return rates
- 20% higher customer satisfaction scores
Common causes of launch delays include:
- Manual data entry errors
- Inconsistent formatting across systems
- Missing product attributes
- Lack of standardized processes
Streamlining these processes can speed up launches, improve efficiency, and drive revenue growth.
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4-Step Plan to Fix Product Data
Bad product data won’t fix itself. Here’s a straightforward plan to clean your data and keep it accurate over time.
Set Data Rules and Assign Ownership
Improving data quality starts with accountability. Clearly define who is responsible for maintaining each piece of data and set strict standards for how it should be handled.
Here’s what you need for effective data management:
- Assign dedicated data stewards for each product category.
- Develop clear, detailed guidelines for data entry.
- Specify required fields for every product.
- Establish quality standards to ensure accuracy, consistency, and timeliness.
Good data governance is about combining precise record-keeping with clear policies that outline roles and responsibilities.
Create One Data Source
Managing multiple versions of product data leads to errors and confusion. A single source of truth (SSoT) is essential for keeping data consistent and reliable.
"Single source of truth (SSoT) is the data management philosophy of aggregating a company’s data into a centralized and consistent repository. It should be a single, authoritative source that holds the most up-to-date and accurate information." – BluestonePIM.com
Take Starboard, a water sports brand, as an example. In Q3 2023, they centralized their product data using a PIM system. This move eliminated inconsistencies across sales channels and drastically cut the time spent managing data.
Poor data quality can have a real business impact:
| Impact Area | Performance Drop |
|---|---|
| Click-through rates | 23% decrease |
| Conversion rates | 14% decrease |
| Customer reach | 6.75% loss |
| Database accuracy | 13% error rate |
Organize Product Categories
Keep your product information structured with a clear hierarchy and consistent attributes. This makes it easier for customers and teams to find what they need. A well-organized system should:
- Logically group similar products.
- Use standardized naming conventions.
- Apply consistent attribute labels.
- Maintain clear parent-child relationships.
- Support easy navigation and filtering.
Avoid abbreviations or vague terms in product names. Instead, include clear details like product type, brand, size, and unique features.
Add Data Quality Checks
Set up automated validation checks to catch errors before they become problems. For example, ensure all required fields are filled, formats are consistent, and units are standardized. Regular audits are also key to keeping your data accurate and complete over time.
Using Better Data to Increase Sales
Fixing data issues is just the beginning. High-quality product information can directly boost sales by improving the customer experience and simplifying operations.
Add Detailed Product Information
Detailed product data plays a big role in driving purchases. Shoppers often decide whether to buy within just eight seconds when browsing products online.
"Providing complete product information has this crucial purpose: It should help buyers make purchase decisions" – April Dunford, Founder of Ambient Strategy
To make your product information more effective, include:
- Multiple high-resolution images
- Clear, detailed specifications with consistent measurements
- Size charts for easy reference
- Transparent pricing details
These details not only help customers make informed decisions but also allow for more personalized shopping experiences.
Create Custom Shopping Experiences
Personalization is a game-changer in eCommerce. Studies highlight its impact:
| Personalization Impact | Performance Increase |
|---|---|
| Email Open Rates | 29% higher |
| Click-through Rates | 41% higher |
| Call-to-action Conversion | 202% better |
| Product Recommendation Conversion | 320% increase |
Success stories like German retailer engelhorn prove the value of personalization:
- 2.5% boost in online conversion rates
- 1.5% growth in average order value
- 4% rise in revenue per visitor
Similarly, Icebreaker saw impressive results:
- 40% more clicks on recommendations
- 28% increase in revenue from recommended products
- 11% growth in average order value
Personalized experiences don’t just improve sales – they make shopping more engaging. But personalization works best when paired with strong search functionality.
Improve Search Results
Accurate, well-organized product data is essential for better search functionality, making it easier for customers to find what they need.
"Your website’s search solution is only ever going to be as good as the product data that it has to work with. The cleaner and more structured your product data, the more accurate and intuitive the shopping experience will be" – Jessica Farrelly from Searchspring
To optimize your search results:
- Use consistent naming conventions
- Write descriptions in natural, conversational language
- Group products by color families for better filtering
Research shows that 59% of shoppers find personalized recommendations simplify their experience, and 40% are more likely to make extra purchases when a website offers effective personalization.
Conclusion
Every flaw in your product data ripples through sales, customer trust, and operational efficiency. Poor product data costs U.S. companies a staggering $3.1 trillion annually. The quality of your data directly influences how much customers trust you, how smoothly your operations run, and how well you can grow.
Companies that focus on improving their data often see better results. For example, ASOS made strides in data quality, which led to increased conversions and SKU growth.
"A customer will rarely say, ‘I love your data attribution,’ right? She will say, ‘I feel the site gets me and gives me all the things I want when I’m searching for it.’ That’s the consumer mindset goal, and how to achieve it is through digitalization and data discipline."
- Nick Beighton, Former CEO of ASOS
This example reinforces the idea that good data is a must-have for growth. It also highlights the importance of strong data governance. Doug Kachelmuss, Senior Director of Data and AI, puts it clearly:
"Success of a data governance program is achieved when employees consistently incorporate data governance activities into their daily tasks with checks and balances in place"
The risks of ignoring data quality are massive. Verizon Wireless, for instance, once faced a $25 million penalty due to data errors. On the flip side, 56% of customers are more likely to return after a personalized experience. Improving your data quality isn’t just a technical fix – it’s a way to build trust, streamline operations, and drive long-term sales growth.
FAQs
What are the key signs that poor product data might be hurting my eCommerce business?
Poor product data can significantly impact your eCommerce business, and there are several telltale signs to watch for:
- Inconsistent product details across platforms, leading to customer confusion and reduced trust.
- Missing or incomplete information, such as vague descriptions, incorrect dimensions, or absent images.
- Frequent customer complaints or returns, often due to products not meeting expectations based on the provided data.
- Difficulty updating product information across all sales channels, slowing down your ability to adapt to market trends or launch new items.
If you notice these issues, addressing your product data management is essential to protect your sales and customer satisfaction.
How can I set up a reliable single source of truth for my product data to ensure accuracy and consistency across all sales channels?
To establish a reliable single source of truth (SSoT) for your product data, start by centralizing all product information in one system. Consolidate data from different sources and ensure it’s standardized, accurate, and free from duplicates or errors. This creates a unified, trusted database.
Next, implement tools like a product information management (PIM) system or a similar solution to manage and update this data efficiently. Ensure only authorized users or systems can make changes to maintain data integrity. Regular audits and validation processes will help keep the information accurate over time.
Finally, make the data accessible to all relevant teams and systems through APIs or reporting tools, and provide training to your team to ensure proper usage. A well-maintained SSoT improves customer experience, streamlines operations, and supports data-driven decisions, ultimately boosting your business performance.
How can I save time and resources by reducing product data errors in my eCommerce business?
Reducing product data errors can save your business significant time and resources. Start by regularly auditing your product data to catch outdated descriptions, incorrect prices, or missing details before they cause issues. Establish data quality standards to ensure consistency, accuracy, and completeness across all listings.
Invest in automation tools to streamline tasks like updating product information and flagging errors early. Additionally, adopt a data governance process to maintain long-term accuracy and prevent recurring mistakes. By prioritizing clean, reliable product data, you’ll improve efficiency and provide a better experience for your customers.
