Episode 8 - Show Me the money - 5 Areas Where Product Data Investment Actually Pays Off

Product data projects are some of the hardest digital transformation work to get signed off. The returns are real, but they sit across half a dozen teams, with no single owner who can walk into the CFO’s office and present the full case. In this week’s episode of Product Data Weekly, Clare and Ben work through five areas where product data investment actually shows up on the balance sheet, and how to measure each one properly so the case writes itself.

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Why these projects struggle to get funded

Product data investment, whether it’s a data cleanup, a taxonomy rebuild, a new PIM, or a content enrichment programme, has one structural problem. The benefits are distributed. The ecommerce manager sees a conversion rate lift. The marketing team sees better return on ad spend. Operations sees fewer hours fixing supplier files. None of these on their own is big enough to justify the spend. Aggregated, they often outweigh the cost several times over.

The fix Clare and Ben land on early in the episode is to flip the question. Instead of forecasting what could be gained, look at what bad data is costing the business right now. Returns, lost conversions, wasted ad spend, manual hours. Those numbers are already in the P&L, just not labelled as a product data problem. Once you put them on a single slide, the conversation with finance changes.

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1. Conversion rate

The fastest payoff of the five. Better content on product pages, better filters on listings, better internal site search, all of these reduce buyer uncertainty and lift conversion. You can usually see the lift in weeks rather than months.

The discipline is measuring it cleanly. Pick a single category. Split it into a test group and a control group at similar price points and similar sales volume. Enrich the test set, leave the control alone, compare. A common mistake is putting low-volume products in the test, which gives you no signal because they barely convert in the first place. Whole category enrichment also reads more clearly than a scattergun approach across unrelated SKUs.

The maths gets compelling quickly. On 50,000 product page views a month, a £50 average order value, and a move from 3% to 3.5% conversion, that’s an extra 250 orders. £12,500 in additional monthly revenue. £150,000 a year. Typical lift on a well-executed enrichment programme sits in the 10 to 25% range.

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2. Search visibility and organic traffic

Slower than conversion. The sweet spot for seeing meaningful ranking changes is around three months, not weeks. But this is one of the easiest areas to attribute, because search algorithms are explicit about what they reward: complete attributes, structured data, keyword-relevant titles, valid schema, clean category alignment. Give them that and rankings move.

The point Ben hammers in the episode is that organic traffic is free. If you’re paying heavily for paid search and ignoring the underlying data quality, you’re funding a permanent ad budget to compensate for content that should be ranking on its own. Agent-driven traffic from ChatGPT and Claude is already starting to widen this gap, because those systems also pull from structured product content.

What to measure: organic impressions, click-through rates, ranking changes for target terms. Look for significant ranking jumps on enriched categories versus untouched ones. This is also where cross-functional alignment matters. The person managing product data is rarely the same person watching ranking dashboards. Both need to be in the room.

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3. Return rates

The clearest example of the cost of not doing it. The fully loaded cost of a return is much higher than most teams realise. Pick, pack, ship, customer service, return shipping, inspection, restocking, and in some cases disposal. In a lot of categories, the loaded cost approaches or exceeds the original margin. You’re spending money to net zero revenue. And the customer who returned the item is probably gone for good.

Identifying which returns are a product data problem is straightforward when return reasons are captured properly. In apparel: colour doesn’t match, fit not as described, smaller than expected. In technical and industrial categories: not compatible, doesn’t function as described. For the automotive aftermarket, fitment errors, missing cross-references, and missed supersessions. Anything tagged “not as described” is a basic data failure.

The catch, as ever, is that reason codes often get scribbled on paper at the returns desk and never make it into a system anyone can analyse. Fixing the capture is usually step zero before you can attribute the savings.

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4. Advertising efficiency

When product data on landing pages is thin or wrong, paid traffic converts badly. Cost per click goes up, click-through rates fall, return on ad spend drops. The classic failure pattern is bidding on a specific brand or part number, then sending the click to a generic category page with no information about the product the user actually searched for. Ben’s analogy in the episode is the golf-sale sign on the street. You paid the person to stand there with the sign, you got the click, you opened the door, and there’s nothing in the shop. The acquisition cost is real. The conversion is zero.

The other half of this benefit is the pull-through from organic. As organic traffic improves, paid spend can usually come down without losing volume. The two channels are connected, but most teams plan them separately. Treating product data as part of the advertising strategy, not a separate ops problem, is the change.

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5. Operational efficiency

The hardest of the five to measure, and often the largest in absolute pounds. The measurement problem is structural. There’s no report that automatically captures how many hours people spent fixing a delisting, chasing a supplier for missing data, or rekeying attributes from a PDF into a PIM. People doing the work are on autopilot and won’t write it down accurately when asked. You have to sit with them and watch.

The story Ben tells is from a customer about five years ago. Their product onboarding process for a single SKU ran to eighty slides of documented steps across multiple systems, with a team of sixty people doing it at volume. Cleaning it up cut roughly 90% of the process and moved time-to-list from weeks to hours. Typical savings on this kind of work sit in the 20 to 30% cost reduction range when measured properly.

What to track: time to list, hours per month spent fixing errors, cost to list calculated as person-hours times loaded labour cost. Plus the hidden cost that rarely makes it into the spreadsheet: a slow catalogue is one a competitor beats to market.

A common worry from the team doing the work is that automation means redundancy. In practice it’s usually the opposite. The team handles a bigger catalogue, takes on richer enrichment, and stops being a bottleneck for new launches.

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How to actually run the calculation

The episode closes on the discipline that ties all five areas together. Before any work starts, define the baseline. Set up control groups. Document what the current state actually costs. Without that, you’ll do the work, get the lift, and have nothing to point at six months later when finance asks what changed.

Cross-functional buy-in is the other half. Each of these five areas sits with a different team. Conversion is ecommerce. Organic is SEO and content. Returns are operations or customer service. Advertising is marketing. Onboarding hours sit with merchandising or supplier management. Get a representative from each into the case, and the £150,000 conversion lift, the 10% return reduction, the freed-up team capacity, and the recovered ad spend stop being five small numbers and start looking like one large one.

That’s the version of the business case that gets signed.

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Listen to the full episode

Episode 8 of Product Data Weekly is available now. For more episodes and the weekly newsletter on operational issues inside product data and ecommerce teams, visit productdataweekly.com.

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