By Troy Sample, Vice President of Sales, and Dan Bronson, Partner, Digital Strategy and SEO, impaqX

Most distributors and manufacturers already know their product data is imperfect. What’s less understood is how fast the cost of that imperfection is compounding.

Troy Sample, Vice President of Sales at impaqX, and Dan Bronson, who leads digital strategy and the SEO practice at impaqX, have spent the better part of the last year watching AI reshape how buyers find and select suppliers. What they’ve seen consistently is that the companies navigating it best didn’t start with an AI strategy.

They started by getting their data right.

In industrial distribution, the deals you lose silently are the most expensive ones. A safety manager at a government facility pulls up an SDS sheet for safety gloves. The document shows an ANSI certification from 2015, but the certification changed in 2019. There’s no phone call or email. They just quietly move on. You lose the sale, but what you really lost was trust.

This scenario plays out thousands of times a day across industrial distribution, and the underlying cause is almost never the product itself. The product is probably a good fit. But the data that represented it to that buyer was years out of date, and in a world where procurement decisions happen at the speed of a single click, that gap is enough to end the conversation before it begins.

“In my experience working across industrial distribution, roughly 80% of end-customer orders are repeat purchases. The rebuy is largely locked in. But there’s a critical 20% window that covers new product evaluations, new use cases, and government or institutional purchases. That’s where product data determines whether you’re even considered.” — Troy Sample

That 20% window is the growth opportunity for both manufacturers and distributors. It’s where new relationships form, competitors get displaced, and digital channels either prove their value or reveal their limitations.

As BlueMeteor notes, data quality issues cost businesses between 15 and 25% of revenue annually (BlueMeteor). Gartner estimates that poor data quality costs organizations an average of $12.9 million per year (Gartner). For industrial distributors and manufacturers, those losses concentrate in that 20% window, where buyers are actively evaluating new suppliers across every search platform they use.

Your product information is the only sales rep in the room.

This has always been an ecosystem problem

The conversation about product content in distribution almost always centers on one side of the table or the other. Distributors need better data from manufacturers. Manufacturers struggle to keep their content current across hundreds of distribution channels. Both statements are true, but treating them as separate problems misses the point.

Manufacturers and distributors exist in an ecosystem where data flows in both directions, and the friction at every handoff costs everyone revenue. In our experience at impaqX, roughly a third of ecommerce implementation projects end up running a parallel product data remediation effort. Without it, distributors would go live with 40 to 50% of their catalog in a usable state, instead of the 75% to 95% they actually need to drive revenue from day one. We see this play out regularly.

A manufacturer maintains beautiful, accurate product data on their own website. But when that data needs to reach 50 different distributors, inconsistencies creep in at every step. The core issue is how the data moves (or doesn’t move) through the channel.

The answer is syndication: structured data feeds that flow directly from manufacturers’ Product Information Management (PIM) systems to distributors through established programs. AD’s eContent program is a strong example. It standardizes supplier data and syndicates it to member distributors so everyone works from the same clean source. When syndication works, the data arrives complete, current, and ready to use.

When syndication doesn’t exist, distributors are left to fend for themselves. Some resort to scraping, using web crawlers to pull whatever product information they can find off manufacturer websites and other sources on the internet. Scraping is a completely different animal from syndication. It’s unstructured, uncontrolled, and inherently lossy. Specifications get truncated. Certifications get missed. Images don’t come through. The resulting data is a patchwork that degrades further every time it gets copied. And when AI engines start triangulating the truth from all those sources, nobody controls what answer surfaces first.

“Distributors have the opposite problem from manufacturers. They’re not pushing content out to hundreds of channels. They’re pulling it in from hundreds of suppliers. The challenge isn’t syndication; it’s getting anything usable in the first place, then keeping it current.” — Troy Sample

NAW MDM Research’s recent B2B buyer research states that 45% of buyers report dissatisfaction with their distributors’ online experiences, despite 67% of those same buyers now making at least half their purchases online (NAW MDM Research). The gap between adoption and satisfaction reveals something interesting. Buyers have moved to digital channels because they have to, not because the experience is good. The distributors and manufacturers who close that satisfaction gap are the ones who have solved the product content problem at an ecosystem level.

When product content costs you the deal

Sample describes the product content situation as a series of “good hair days and bad hair days.” Sometimes the content is sharp, complete, and exactly where it needs to be. And sometimes it’s messy, uncontrolled, and desperately in need of a clean-up, which is not unusual by any means.

Systems for updating technical documents across distribution channels are often manual, laborious, and easy to deprioritize when everyone is busy. A manufacturer updates an ANSI certification on a safety product and creates a new spec sheet, but doesn’t have an automated way to push that update to every distributor carrying that product. Some distributors get the new version, but many don’t. Or if they do, they don’t have the bandwidth to make the changes to their systems at scale.

The consequences tend to cascade. Distributors increasingly rank their manufacturer partners based on data quality, whether formally or informally. Sample describes it as a stack-ranking based on friction.

“When it comes time for SKU rationalization, which might happen once a year at certain distributors, and let’s say they drop 10 to 20% of their SKUs, the manufacturers creating the most friction in the data flow are the ones most likely to get cut.” — Troy Sample

Getting dropped in a SKU rationalization isn’t clean. The eliminated products take associated revenue with them. Buyers who would have purchased those products often bought other items in the same transaction. One line drops off the approved supplier list, and multiple revenue streams disappear with it.

For distributors, the problem shows up differently but with similar financial impact. When an MRO buyer is putting together an approved vendor list and can’t find current certifications on your product pages, they’re not submitting a request for the missing documentation. They’re moving to the next supplier on the list.

Or consider an estimator searching products for a bid who can’t find the right size, shape, length, or heat rating. A good customer will call their sales rep and ask for help putting the bid together. But when it keeps happening across multiple products and projects, the relationship starts to suffer. At some point, the estimator is looking at a competitor’s platform where the data is available.

Research from BlueMeteor estimates that poor data quality results in operational losses averaging $15 million per year for affected wholesalers (BlueMeteor). For distributors, those losses manifest as abandoned carts, customer service calls that shouldn’t need to happen, and orders that go to competitors who happened to have better product information available at the moment of decision.

Agentic commerce changes the rules

We’re now entering what the industry is calling agentic commerce, where AI actively participates in buying decisions. Bronson sees this shift picking up speed faster than most distributors and manufacturers realize.

“AI triangulates the truth from whatever data exists about your products. Your website, your distributors’ websites, the big box sites, technical documents, reviews, all of it. If your data is inconsistent across those sources, AI doesn’t know which version is correct. It normalizes what it finds and publishes that normalized version as the answer.” — Dan Bronson

Think about what that means. In traditional search, you could tune your own website and have some control over what buyers saw when they landed on your product pages. In agentic commerce, the AI is synthesizing answers from every source it can access. If your most current product data exists only on your website but outdated information lives on 10 distributor sites, the AI might surface the outdated version simply because it appeared in more places.

Recent McKinsey research found that more than 60% of organizations struggle with incomplete, inconsistent, or siloed data, and that this directly slows ROI realization for AI initiatives (McKinsey). The same research showed that a B2B materials distributor using AI-enabled tools was able to unify fragmented product data in roughly one-fifth the time previously required. The speed is remarkable, but the dependency on data quality hasn’t gone away. AI amplifies whatever data foundation you give it. And if your foundation is shaky, you simply won’t be searchable in agentic commerce.

“AI makes this more urgent, not less. Without that foundation, you’re just accelerating chaos.” — Troy Sample

The distributors and manufacturers who understand this are approaching agentic commerce differently. They’re treating product content as infrastructure. They’re building systems that ensure consistency across every channel where their products appear. And they’re using tools like impaqX’s AI Tech Assistant, which lets customers ask natural language questions about technical specifications and get accurate answers pulled directly from structured, verified product data.

The search visibility tax on thin content

The product data problem doesn’t stay inside your catalog or your procurement workflows. It shows up directly in whether buyers can find you at all. Traditional search engines and the new wave of AI-powered search tools both evaluate product pages on the same underlying dimension: how complete, structured, and current is the content? Both penalize thin pages in ways that compound over time.

On the traditional SEO side, the correlation between content depth and ranking performance is well documented. Product pages ranking in the top 3 positions on Google average nearly 3x more unique content than pages ranking on page two (SEMrush 2025 Ecommerce Study). That gap is driven almost entirely by completeness of specifications, descriptions, and technical detail. When one outdoor retailer restructured just 400 product titles to match the language buyers actually use in searches, they saw a 67% increase in impressions within 3 months (SEMrush 2025 Ecommerce Study). The lesson isn’t about clever copywriting. Product pages with real substance (dimensions, certifications, application notes, compatibility data) earn the search visibility that sparse pages never will.

The emerging discipline of Generative Engine Optimization, or GEO, adds another dimension. Researchers at Princeton, Georgia Tech, and the Allen Institute for AI published the first large-scale study of how content characteristics affect visibility in AI-generated search responses. Their findings: adding verifiable statistics, expert quotations, and technical specificity to content can improve AI visibility by 30 to 40% (Aggarwal et al., KDD 2024). The study also found that traditional tactics like keyword stuffing actually decrease AI visibility (Aggarwal et al., KDD 2024). The AI search layer rewards substance and penalizes filler, which means product pages built on real technical content have a clear advantage over pages that are little more than a part number and a one-line description.

What makes this urgent is the compounding effect of data inconsistency. When AI engines encounter conflicting information about a product across multiple sources (different specs on the manufacturer’s site versus a distributor’s site, an outdated certification in one channel but a current one in another) they reduce confidence in all of those sources simultaneously. Research estimates that inconsistent brand data across platforms reduces AI recommendation rates by 30 to 40% (ZipTie.dev). Meanwhile, content updated within the last 30 days receives more than 3x as many AI citations as stale content (ZipTie.dev). For distributors and manufacturers whose product pages haven’t been meaningfully updated in years, that’s a measurable, accelerating visibility penalty.

The same foundational data work that solves your catalog problems, your procurement integration challenges, and your channel consistency issues also solves your search visibility problem. Complete, accurate, freshly maintained product content determines whether buyers find you at all.

What getting it right actually looks like

We worked with a regional distributor facing the exact data structure challenges that are extremely common across the industry. Their master product data wasn’t aligned with what was showing up in their ecommerce system, and they had uncategorized items sitting in their catalog. Private label products existed only in isolated workspaces instead of their master catalog. Manufacturer and brand data had inconsistencies throughout.

The work required to fix this wasn’t glamorous. It involved:

• Reviewing data structure across their entire system
• Identifying where the truth actually lived and recreating private items so they flowed through the proper data architecture
• Resolving duplicate part numbers
• Building new catalog structures that received updates properly
• Creating subsets to handle restricted items by geography

Once that foundation was solid, the team set up twice-weekly delta exports to keep enriched data current across their selling channels. The result: the organization’s 482 restricted items and 1,171 unrestricted items now flow accurately to the right customers in the right markets, and their 20 previously uncategorized items (which included some of their best sellers) now surface correctly in search and browsing.

This kind of foundational work doesn’t generate immediate applause from customers or revenue headlines, but it’s what enables everything else. With clean, structured data in place, this distributor can now layer on the kinds of tools that create competitive differentiation. For HVAC clients, that might include impaqX’s AHRI System Builder, which helps contractors configure matched heating and cooling systems using certified AHRI data. For distributors in other segments, it might be PikClix for interactive parts diagrams, or FusionX for managing complex data integrations across ERP systems and multiple supplier feeds.

It’s the kind of work that rarely makes it into a press release, but it’s what makes everything that follows actually work. Bob Lewis, founder of impaqX, describes the distinction plainly.

“When distributors launch an ecommerce site before their product data is ready, what typically happens is they launch, see minimal results, blame the platform, and start evaluating different technology. But technology was never the constraint. The foundation was missing.” — Bob Lewis, Founder, impaqX

The segments getting this right

The electrical and HVAC industries offer two instructive models for what getting this right looks like at scale.

In electrical, IDEA was founded in 1998 as a joint venture between NEMA and the National Association of Electrical Distributors specifically to solve the data exchange problem. Their IDEA Connector platform is now used by over 1,000 manufacturer brands and 8,000 distributor locations, with a Harmonized Data Model that allows manufacturers to submit data once and have it syndicated to distributors in a normalized, ready-to-use form. The industry decided the friction was expensive enough to be worth solving collectively.

In HVAC, AHRI arrived at a similar outcome through regulatory pressure. Certification is required for commercial bids, government tenders, and federal rebates under the Inflation Reduction Act, which means manufacturers have no practical choice but to keep their certified specs current. Contractors and engineers go straight to the AHRI directory because they know what they find there is verified.

Both demonstrate the same principle: when an industry creates real accountability around data quality, the data gets better and everyone in the channel benefits. The segments still operating without that structure carry the most risk as AI reshapes how buyers evaluate suppliers.

Even without industry-wide coordination, individual companies can create their own internal standards for how product data flows through their systems, how updates get managed, and how quality gets verified before content reaches buyers.

The stakes beyond your website

Product data quality also determines whether you can compete for the largest accounts in your market.

Enterprise and institutional buyers (large contractors, government agencies, hospitals, universities) increasingly run their purchasing through e-procurement platforms like Coupa and SAP Ariba. These systems require suppliers to meet minimum data quality thresholds before a catalog is even activated. Every item needs a complete description, a valid UNSPSC classification code, correct unit of measure, lead time, and pricing. Many buyer organizations layer on additional requirements, including product images and current certification documents. A distributor whose catalog fails these thresholds is invisible. The buyer’s procurement policy may simply prevent non-integrated suppliers from being used at all. The e-procurement tools market is projected to reach $26.4 billion by 2034, with large enterprises accounting for more than two-thirds of that spend (Dataintelo). The distributors who can’t meet these data requirements are locked out of their most valuable growth opportunities.

The payoff extends to your own website as well. Research consistently shows that complete, well-structured product content (detailed descriptions, full specifications, quality images, current documentation) drives measurably higher conversion rates. Visitors who arrive through AI-driven search are approximately 4.4x more likely to convert compared to traditional search visitors, and they stay longer, view more pages, and bounce less (Akeneo). But they only convert if the product page they land on delivers the depth and accuracy they’ve already been primed to expect. Incomplete product pages waste the highest-intent traffic you have.

What this means for how you compete

The distributors and manufacturers taking product content seriously end up in a very different competitive position. Their content is differentiated. Their technical documents stay current. And their search presence reflects actual product expertise instead of generic descriptions that haven’t been touched in years.

“Distributors will increasingly rank their manufacturer partners by the quality and usability of the content they receive. The manufacturers whose data is clean, complete, and easy to work with become preferred suppliers. The ones creating friction get rationalized out.” — Troy Sample

For distributors, the same dynamic applies in reverse. The ones making it easy for buyers to find accurate, complete product information in that critical 20% window are the ones winning new business. The ones where buyers hit dead ends (missing specs, outdated certifications, thin product pages) are quietly losing deals they never knew they were in contention for.

impaqX was once approached by a frustrated industrial distributor who couldn’t figure out why their site was generating zero revenue. Our founder, Bob Lewis, asked a series of questions, and they’re the ones we ask at the beginning of every engagement that touches product content: Tell us about your data. How complete is it? How does it flow through your systems? Where are the gaps? Before any technology decisions get made, we need to understand what foundation exists, because the best ecommerce platform in the world can’t fix a data structure problem.

The companies who get there did the foundational work first. They defined what complete product content looks like for their business, built processes to keep it current, and created systems that ensure consistency across every channel where their products appear. In a world moving toward agentic commerce, product data is the foundation everything else is built on.

Article cowritten with Dan Bronson.

Dan Bronson is co-founder of Impaqx, a consultancy helping wholesale distributors and manufacturers grow through B2B ecommerce, SEO, and analytics. He’s spent two decades building data and systems, starting with business intelligence at USAA and his first work with language models back in 2006. At Abatix, a $100M industrial distributor, his digital rebuild won competitive bids against Fastenal and MSC Direct. Today he builds the AI agent systems that turn distributor catalogs and customer behavior into revenue. Dan is also a Major in the Texas Army National Guard with more than 22 years of service.