If you're building a business case for product data enrichment, you'll likely start by comparing labor and software costs: what you pay people to enrich products today, and what a tool would cost instead.
Beyond those, there are bigger costs that often get overlooked. Missing and wrong product data costs money once products are live, through products shoppers can't find, rejected listings, returns and compliance risk. A complete business case counts both.
What does poor product data cost?
Poor product data costs money after products go live, whichever way they were enriched. In the US, retailers expected shoppers to return $849.9 billion of merchandise in 2025, including 19.3% of online sales (NRF and Happy Returns). Returns have many causes, and products that don't match their listing are one that better data can reduce.
The costs show up in four places.
Products shoppers can't find. If a tent has no packed weight attribute, it drops out of every filter and search for lightweight tents. The same gap can keep it out of marketplace search and AI shopping answers, which match products on their attributes.
Rejected or suppressed listings. Marketplaces can reject or hide listings that are missing required attributes. Each one means rework, and days the product isn't on sale.
Returns and support tickets. If a listing says a jacket is waterproof and it's only water-resistant, shoppers send it back. Returns from products that don't match their description cost shipping and handling, and sometimes the product itself.
Compliance risk. A claim without evidence behind it, such as "PFAS-free", can get a product delisted.
These costs are harder to measure than hours, so they're often left out of business cases. Track them before and after a pilot: rejected listings, returns with reasons like "not as described", support tickets about product details and search and filter traffic to the pilot category.
What does product data enrichment cost per SKU?
There's no single market rate, because each approach charges for different work.
| Approach | What you pay for | What drives the per-SKU cost | Costs that are easy to miss |
|---|---|---|---|
| In-house team | Salaries and tools | Minutes per product, and the number of attributes each category needs | Time spent finding sources, and rework on rejected listings |
| Outsourced team or agency | A rate per product or per hour | How complex each product is, and how much your team checks the output | Quality checks on returned work, and re-sending files when suppliers update |
| AI enrichment software | A license or credits | How much of the output needs review | Setup in the first year, and review time |
When you compare quotes, check what each one includes. An agency's rate may not cover the time your team spends checking its work, and a software price doesn't include setup or review.
What drives the cost of enriching a product?
How complete supplier data is. If a supplier sends a full spec sheet, most values can be copied across. If they send only a model number and a photo, someone has to research every value.
How many attributes each category needs. Some categories need only a few attributes, while others, like tents, may need 20 or more, from season rating to packed weight.
How many channels you sell on. Each marketplace sets its own required attributes and categories, so each extra channel adds mapping and checks.
How often products change. New products, seasonal ranges and supplier updates all need enriching. A catalog that changes every season costs more to keep up to date than one that rarely changes.
How much review each value needs. Safety and compliance claims need a specialist to check them, which takes longer. Values copied from a spec sheet usually need only a quick check.
How many languages you publish in. Each extra language means translating every product, and checking that key terms are translated the same way each time.
How do you calculate your current cost per SKU?
Start with what enrichment costs you today. You need that number to show how much a new approach saves.
- Pick a sample. Take a few dozen recently added products from different categories, including some that arrived with poor supplier data.
- Time each step. Record the minutes spent finding sources, entering attributes, writing copy and reviewing, separately.
- Use a loaded hourly rate. Include benefits and overhead on top of salary. For an agency, add the time your team spends checking its work.
- Add rework. Count listings rejected by marketplaces or corrected after going live, and how long each fix takes.
- Multiply by your monthly volume. Use the new and changed products you handle each month, as well as any backlog.
Take an outdoor and sporting goods retailer that adds 1,500 new products a month. Say its sample averages 25 minutes per product and the loaded rate is $30 an hour. That's $12.50 per product. At 1,500 new products a month, enrichment takes about 625 hours, or $18,750 a month, before rework.
Published estimates from enrichment vendors put manual enrichment at roughly 10 to 45 minutes per product, depending on how many attributes a category needs and how complete the supplier data is. Applied to larger catalogs, the hours add up quickly.
Manual enrichment
| Example | Products a year | Average minutes per product | Hours a year | Cost a year at $30 an hour |
|---|---|---|---|---|
| Fashion retailer | 20,000 | 12 | 4,000 | $120,000 |
| Home and furniture retailer | 12,000 | 25 | 5,000 | $150,000 |
| Electronics retailer | 24,000 | 35 | 14,000 | $420,000 |
| Industrial distributor | 50,000 | 45 | 37,500 | $1,125,000 |
Hours a year = products × average minutes ÷ 60. For the industrial distributor, that's roughly 20 people working full time.
With AI enrichment (review time only)
| Example | Products flagged for review (20%) | Average review minutes per product | Review hours a year | Review cost a year at $30 an hour |
|---|---|---|---|---|
| Fashion retailer | 4,000 | 6 | 400 | $12,000 |
| Home and furniture retailer | 2,400 | 6 | 240 | $7,200 |
| Electronics retailer | 4,800 | 6 | 480 | $14,400 |
| Industrial distributor | 10,000 | 6 | 1,000 | $30,000 |
This table covers review time only, so add software and setup costs on top. Averages vary by category and by how complete your supplier data is, and the 20% review share is an assumption, so use the averages from your own sample and pilot.
How do you estimate the cost with AI enrichment?
With AI enrichment, most of the per-SKU cost moves from data entry to review. The estimate has three parts:
- Software. The license or credits, spread across the products processed in the same period.
- Review time. The share of products with flagged values, multiplied by the minutes it takes to review one, plus a regular spot check of values that weren't flagged.
- Setup. Defining attributes, allowed values and rules, and mapping supplier fields. Most of this happens in the first year, though rules need updating as categories change.
Say 20% of the retailer's 1,500 monthly products have flagged values, and each takes 6 minutes to review. That's 300 products and 30 hours, or $900 a month at the same rate. With a few hours of spot checks on top, the monthly labor cost drops from $18,750 to around $1,000.
That saving depends on the quality of the AI's output. If a tool flags most of its values, your team ends up checking almost everything, and the review time can come close to doing the work by hand. If it gets values wrong without flagging them, the mistakes go live, and fixing them later can cost more than the time you saved. So test the review share on your own catalog before you count on it, and weigh the cost of wrong data alongside the hours.
How do you build the ROI case for product data enrichment?
A business case holds up best when its numbers come from your own data. List the savings and benefits you expect, with how you'll measure each one, and lead with the hours saved.
| Saving or benefit | How to measure it |
|---|---|
| Hours saved on research and data entry | Time a sample of products before and during the pilot |
| Lower agency or outsourcing spend | Invoices before and after |
| Less time spent fixing listings | Number of rejected or corrected listings, multiplied by the time each fix takes |
| Products go live faster | Days from supplier file to live product, before and after |
| Smaller backlog | Number of products waiting for data before they can go live |
| Fewer rejected or suppressed listings | Marketplace reports |
| Fewer returns for "not as described" | Returns data by reason code |
| Fewer support tickets about product details | Ticket tags or categories |
| More conversion and search traffic | A controlled test, such as one enriched category compared with a similar one left as it is |
ROI = (measured benefit − total cost) ÷ total cost, over the same period. Total cost includes the software, setup, review time and ongoing rule updates.
Keep conversion and search traffic gains on a separate line until a test supports them. Conversion and returns also move with price changes and promotions, so a simple before-and-after comparison can overstate the effect. If the case pays back on hours and spend saved alone, any proven gain in conversion adds to it.
Most of the cost of new software comes up front, in setup and onboarding, while the savings build as more products go through it. So the first few months may cost more than they save. Show the payback period alongside the annual return, calculated as first-year cost divided by monthly savings once the tool is running. Rolling out one category at a time also means savings start before the whole catalog is set up.
How do you test the numbers before committing?
Run a pilot on one category before you sign. Pick one with high traffic or many incomplete products, and measure four things:
- Minutes per product before and after
- The share of products flagged for review, and how long each review takes
- Attribute completeness before and after
- Days from supplier file to live product
Those four numbers replace the assumptions in your business case with your own results. Our guide to manual vs automated product data enrichment covers setting up the rules and review for a first category.
How does Hypotenuse AI lower enrichment costs?
Hypotenuse AI works on both sides of the cost: the time your team spends reviewing, and the cost of poor data reaching your listings, which its AI data quality checks catch before products go live.
| Cost line | How Hypotenuse AI helps |
|---|---|
| Review time | Every value shows the source it came from. Values the AI is less sure of are flagged, and the rest can be approved in bulk. |
| Poor data reaching your listings | AI data quality checks flag products whose fields disagree with each other or with the product image, before they go live. |
| Compliance risk | Checks titles, descriptions, attributes and images against your banned and required terms and each channel's rules, set per channel and category. Every flag names the rule and the field. |
| Repeat corrections | Uses your team's corrections to handle similar products better over time. |
| Copywriting | Generates product descriptions in bulk from the enriched attributes, in your brand voice. |
The numbers in your business case are only as good as the baseline behind them. Teams usually start Hypotenuse AI on a sample of their own products, which gives you your real review share and minutes per product for the pilot described above. See how product data enrichment works.
FAQs
Is AI enrichment cheaper than manual data entry?
Typically, yes, though it depends on the quality of the AI enrichment. If most of the AI's values pass review, the savings add up quickly for catalogs that add products regularly. If a tool flags most values or gets them wrong, it can end up costing more. Measure both methods on the same sample of products, including review time and the errors each one lets through.
How long does it take to enrich a catalog?
Manually, multiply your measured minutes per product by the number of products. At 25 minutes a product, 10,000 products take about 4,200 hours, or roughly two years of one person's full-time work. With AI enrichment, processing runs in bulk, and the time that matters is your team's review.
Should you count conversion gains in the ROI?
Yes, once a test supports them. Conversion also moves with price changes, promotions and the season, so a before-and-after comparison can credit enrichment with gains it didn't cause. Test one enriched category against a similar one left as it is, and show the result as its own line next to the hours and spend saved.
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