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Returns are not a logistics problem. They are a data problem.
If you want to reduce your return rate, you will probably first look at warehouse processes, shipping costs, or packaging. That's understandable. But it doesn't go far enough.
The actual decision for or against a return is made much earlier. It happens the moment your customer clicks “Add to cart.” If they add two sizes of a garment to their cart, they will send one of them back. The same will happen with similar electronic components. And the decision is significantly influenced by one factor: the quality of your product data.
Unclear images, incomplete attributes, inconsistent variants, or missing contextual information create uncertainty. Uncertainty leads to bad purchases. And bad purchases lead to returns.
If you want to tackle this issue seriously, you have to start where expectations arise: with the product experience.
The economic reality of returns
Returns are expensive. And significantly more expensive than many calculations suggest.
It's not just about postage. There are also:
- Inspection and processing costs
- Restocking
- Loss of value for opened goods
- Depreciation
- Additional customer service
- CO₂ emissions from transport
In high-margin segments such as fashion, return rates can be as high as 30 to 50 percent. In other areas, they are lower, but even 10 percent can have a massive impact on profitability. Every return eats into your margin. And every avoidable return has a direct impact on your contribution margin.
Why customers really return items
Most returns have one thing in common: expectations and reality do not match.
1. Visual differences
Colors appear differently on different displays. Without proper color management and consistent image processing, discrepancies arise. The customer orders “beige” and receives “sand.” That's enough for a return. This is often caused by “print” working in the CMYK color space and converting to RGB for “online.” We strongly recommend working the other way around - RGB first and you'll save yourself a lot of trouble.
2. Lack of haptics
No one can touch a material online. If fabrics, surfaces, or structures are not displayed in high resolution and detail, uncertainty remains. Extreme close-ups and videos help here. Time and again, we see manufacturers providing online retailers with insufficient visual material. Strategy aside, this is one reason for reduced profitability.
3. Size and measurement problems
Without reference objects, model specifications, or clear measurement tables, there is a lack of orientation. This leads to mispurchases, especially in fashion, furniture, or technical products. That is precisely why it is important to create these references, whether through weight and height specifications for models, correct measurements for furniture, or corresponding mood or ambiance photos.
4. Inconsistent product data
Discrepancies in descriptions, bullet points, and technical data destroy trust. This area requires meticulous work and appropriate quality control. Example: If the caption does not match the product shown, the chance of a return increases.
Returns are rarely coincidental. They are often the result of unclear or incomplete product communication.
Starting point 1: Visual clarity instead of mere sales staging
Packshots on a white background are no longer sufficient. They show the product, but not how it is used. But this is where you can start: if you have photos taken with an edge length of 6,000 pixels, why do you only share the images with 1,000 or 1,200 pixels?
Context images and in-use scenarios
Show your product in use. A sofa in a furnished living room. A jacket in motion, or even better, a video of a dress - ASOS does this very successfully, for example. A coffee machine in a realistic kitchen setting - this reduces the scope for interpretation. And please remember: AI can be a great help here. If you have the right photos, you can use AI to show products in different environments. This creates confidence among customers.
Zoom and 360-degree views
Detailed shots and rotating views help to better assess materiality and workmanship. Here, too, image quality is key. If you're taking product photos, you should also take detailed photos that show the material realistically and allow for powerful zooms. Embellishment is the wrong approach here: if the product is presented in an unrealistically favorable light, not only does the likelihood of returns increase, but poor reviews can also result. The same applies to 360-degree views, which are now better created with 3D models than on photo plates: they give customers more confidence. After all, the fewer questions remain unanswered, the lower the risk of returns.
Video depending on market segment
Video does not automatically eliminate returns. It is effective when it is relevant to the purchase decision and fits the context:
- Fashion: Running videos, size comparison, material movement
When buyers know the dimensions, size, and weight of the model and also have a more accurate idea of how the material behaves, this creates confidence. - Furniture: Dimension visualization in the room, augmented reality, assembly videos
Here, buyers need to understand how a piece of furniture will look in their room and how it is assembled. - Technology: Setup process, function demonstration
If users can realistically assess whether they will be able to integrate the product into their technical equipment, this prevents poor reviews and returns. - Beauty: Application and before-and-after effects
Decorative cosmetics in particular require instruction. Videos are ideal for this purpose.
Moving images reduce uncertainty much more effectively than static photos. But they need to be managed in a structured way and clearly linked to product data. EIKONA Media is happy to help with this. PIMs and DAMs are useful tools for keeping things organized at a reasonable cost.
User Generated Content
Real customer images create credibility. They show real-life usage situations and help to calibrate expectations. Provided they are sensibly categorized and assigned to the right product. You're probably familiar with this from Amazon or TikTok.
We can show you how to do this yourself in the best possible way. Structured Digital Asset Management such as TESSA DAM is extremely helpful.
Starting point 2: Data quality as a structural foundation
Good images and videos alone are not enough. Product data must be comprehensive and error-free. Visual material must be correctly described, versioned, and played out consistently across all channels.
Data creation
Unfortunately, we often find that producers are careless when it comes to data creation. Data maintenance clerks sit crammed into noisy rooms. The accounting environment should be different. It should be quiet, with enough space and time to work without errors. You should bear in mind that any error at this point is multiplied for retailers, is difficult to correct, and therefore has a serious impact on your earnings.
Attribute consistency
Color, size, material, weight, technical details. This information must be identical throughout the entire system. Product descriptions must contain the same values as those shown in the attribute values. Discrepancies not only lead to mistrust. If a customer places an order based on the wrong value, a return is inevitable.
Variant logic
A clear structure is crucial, especially for complex product variants. If the red variant suddenly shows the image of the blue version, the likelihood of returns increases immediately. You cannot blame the customer for ordering based on the wrong value.
Dynamic Imaging
Blurred or incorrectly scaled images on high-resolution displays look unprofessional and increase uncertainty. In such cases, you will sell less and have to deal with more returns. Proper management in a DAM can help prevent this from happening. With our TESSA DAM, we can create device-optimized playback via CDN, ensuring clarity.
Versioning
Incorrect, outdated product images or old packaging designs in the shop create false expectations and cause confusion. Clean asset management for versions prevents such errors. A DAM also helps here, ensuring fewer returns. Data quality is not a nice-to-have. It is the basis for trust.
More about functions and features
The future: AI, personalization, and predictive content
The issue of return prevention will continue to evolve in the coming years. There are new opportunities that you should take advantage of. Data quality will become even more important. Focus on raising the quality of your data to the next level as soon as it is created. This will generate more sales and reduce returns at the same time.
AI analysis of reasons for returns
Until now, evaluating customer feedback has been relatively time-consuming. In principle, all customer comments relating to returns can be analyzed in terms of content. Until now, this required manual analysis. And very few companies have a structured questionnaire like the one Booking uses for its cancellations. This is where AI comes in: if customers frequently mention “color too dark” or “runs small,” AI can recognize patterns in customer feedback. These insights flow directly into content optimization and product presentation.
Predictive Analytics
AI can now even identify products with a high probability of being returned before actual returns occur. The AI then informs you about what data you still need on your product detail pages to reduce the probability of returns. You can then add additional content such as videos or detailed images in a targeted manner.
Personalized representation
Not every customer needs the same information. Until now, the approach has been to pack as much information as possible into the product detail pages. However, this can also lead to customers not finding or overlooking information. AI can prioritize content depending on the target group. A tech-savvy user gets more specifications. A design-oriented buyer will see more contextual images. Nevertheless, this means that you must - of course - work with a high level of information depth for your products, which you make available via PIM and DAM. This also allows you to create automated content variants. This only works if product data is structured. AI does not replace clean data. It enhances its effect. Without structured PIM, AI remains superficial.
PIM and DAM as the operational backbone
If you want to systematically reduce returns, you need more than good intentions, careful work in the warehouse, and products that meet requirements. Customers need to be sure that they are getting what they are looking for.
PIM as a Single Source of Truth
A Product Information Management System (PIM) can ensure that all attributes are maintained centrally and displayed consistently, eliminating conflicting information between your shop, marketplace, and print media. However, you must ensure that the PIM functions correctly and that your product data team has the opportunity to maintain the data carefully. Peace and quiet and time are two important factors here that are often underestimated and that we cannot take off your hands. EIKONA Media can help you develop suitable data models and configure your product information management system.
DAM as an asset hub
In a digital asset management system, you organize images, videos, documents (e.g., data sheets, certificates, instructions, etc.), and other files that are relevant to customers (e.g., lighting design files, BIM files, etc.). The DAM should - like the TESSA DAM - automatically ensure that the files are correctly linked to your products and that the right version always appears in the right place.
Closed feedback loops
You may have heard of Kaizen. In Japan, people like to work according to the principle of continuous improvement. It's about optimizing processes and products in a sustainable way through many small, steady steps. If we apply this to returns, the reasons for returns from customer service must also flow back into the PIM. Marketing must then adjust any insufficient or incorrect content and import new or corrected assets into the DAM. The updated information is then published again. In addition, the effects of the corrections should be monitored - whether positive or negative.
This creates a learning system. What is learned in this way can be transferred to other products and their data.
From reason for return to content strategy
Imagine you are systematically analyzing your return data.
- 18 percent say: “Color does not meet expectations.”
- 12 percent say: “Size does not fit.”
- 9 percent say: “Material feels different than expected.”
Of course, logistical problems are often responsible for returns. You need to work on that, too. However, these cases are due to deficiencies in the product data. This should be taken seriously.
If you use this data, you can:
- Optimize color fidelity
- Make size charts more precise
- Produce additional detailed photos
- Incorporate videos to show the materials
Returns analysis optimizes the briefing for your next photo shoot. A little Kaizen helps, and it's not that difficult. It requires data analysis, which AI can also help with, and you will definitely increase the gross profit of your product sales.
Checklist: Are you actively reducing your returns or just managing them?
Here is a brief checklist for you - just the most important points:
- Does your product data team have enough time and peace and quiet?
- Is your product data complete and consistent?
- Are there clear variant logics?
- Do you use videos where they reduce purchase uncertainty?
- Are reasons for returns systematically evaluated?
- Is there a connection between customer service, the content team, and product data maintenance?
- Do you use a DAM that automatically links assets to the corresponding attributes in the PIM?
If you answer “no” to several of these questions, your greatest leverage is probably not in the warehouse, but in the data model.
Conclusion
Fewer returns start before shipping
Returns sometimes occur in the warehouse, that much is clear. However, they also occur in the minds of your customers – the moment expectations are built up. And these expectations are largely shaped by your product data.
When images, videos, attributes, and variant logic are precise, consistent, and rich in context, uncertainty decreases and so does the rate of bad purchases. And every return avoided has a direct impact on margins, brand perception, and sustainability.
Product data quality is therefore not an operational detail or purely an IT issue. It is a strategic competitive factor. Those who organize their data in a structured manner in a powerful PIM, manage assets cleanly in the DAM, and systematically feed return reasons back into content optimization create a learning system with measurable ROI.
AI, personalization, and predictive analytics will amplify this effect in the future. But they only work on the basis of cleanly structured, complete, and consistent data.
If you use this lever consistently, you not only reduce costs. You increase trust, conversion, and contribution margin - and turn product data quality into your real competitive advantage.
