By Elogic Commerce · featuring insights from Paul Okhrem
Product recommendation engines are sold as universal wins. Drop them in, watch average order value climb.
In B2C ecommerce, the evidence for this is real. In B2B ecommerce, the picture is more complicated — and companies that treat B2B like B2C in this area tend to get disappointing results.
Paul Okhrem, founder of Elogic Commerce and AI decision consultant at paul-okhrem.com, has a direct view on this from running a 200-person ecommerce engineering firm across hundreds of B2B implementations: "The recommendation engine that works for a consumer electronics retailer is the wrong model for an industrial distributor. The data signals are different, the buying behavior is different, and the failure modes are different."
Why B2B recommendations require a different approach
B2B purchasing behavior has structural characteristics that standard recommendation models weren't designed for:
Low purchase frequency, high order value. A manufacturer might reorder the same consumables monthly and make a capital purchase annually. Most recommendation systems are tuned for frequent, lower-value purchase patterns. With sparse transaction histories, collaborative filtering — "customers like you also bought" — doesn't have enough signal to work well.
Account-level buying, not individual buying. In B2B, the "customer" is an organization with multiple buyers, approval workflows, and defined purchasing categories. A recommendation relevant to the maintenance department buyer is irrelevant to the procurement manager. Most recommendation systems are designed for individual buyer behavior, not account-level purchasing dynamics.
Contracted catalogs and pricing. Many B2B accounts operate under framework agreements that define which products they can purchase and at what price. A recommendation engine that surfaces products outside the account's contracted catalog creates confusion at best, compliance issues at worst.
Relationship-driven purchasing. A significant portion of B2B revenue runs through account relationships — salespeople, account managers, known vendor contacts. The recommendation engine is competing with or supporting a human relationship, not replacing it. This changes what "good" looks like.
Where AI recommendations do create value in B2B
Despite the constraints, there are contexts where AI-powered recommendations generate clear, measurable impact in B2B.
Replenishment prediction for repeat consumables. For companies selling consumables, MRO supplies, or anything with predictable reorder cycles, AI that identifies when an account is likely due for reorder — and surfaces a one-click reorder suggestion — drives meaningful revenue. This is the highest-ROI recommendation use case in B2B that we consistently see at Elogic.
Cross-sell within a job or project context. When a buyer is configuring a system or completing a project-based purchase, recommendations that complete the solution — compatible components, required accessories, complementary supplies — are genuinely useful. The key is that they have to be contextually grounded in what the buyer is currently doing, not based on generic co-purchase patterns.
Substitution during stock events. When a requested item is out of stock, AI-powered substitution recommendations — technically compatible alternatives with clear explanation of the match — convert buyers who would otherwise leave. This is a high-intent moment where a good recommendation has immediate impact.
Discovery for new product categories. For established accounts that have been purchasing in a narrow category for years, recommendations that surface relevant products from adjacent categories the account has never ordered from can expand wallet share. These require careful calibration to avoid recommending outside the account's relevant scope.
What the Elogic implementation approach looks like
At Elogic Commerce, our AI recommendation implementations for B2B clients follow a sequenced approach based on Paul Okhrem's framework for AI investment: validate the use case before scaling the infrastructure.
We typically start with replenishment prediction for a subset of accounts where order history is rich enough to support it. This produces a measurable outcome quickly — within 60-90 days — and builds the organizational confidence and data infrastructure for broader rollout.
We instrument everything before we launch. Baseline AOV, baseline replenishment lag, baseline cross-category purchase rate by account segment. The measurement framework is defined before the first recommendation is served. This is the approach Paul details on paul-okhrem.com as The Proof Standard — a discipline we've adopted internally across all our AI implementations.
We also design the recommendation logic with the sales team, not just the product team. In B2B, the recommendation engine and the account manager need to be working from the same playbook. When they're not, you get a recommendation that contradicts what the salesperson told the buyer last week — which erodes both.
The honest benchmark
Based on our implementations: AI recommendations in B2B ecommerce, when scoped correctly, generate 8-15% improvement in replenishment order frequency for eligible SKU categories, and 10-20% improvement in cross-category purchase rate for accounts where the cross-sell context is right.
Those are not the consumer ecommerce numbers. They're also not the numbers that get cited in vendor decks. But they're real, measurable, and they compound.
Paul Okhrem's position on this: "The honest benchmark is the right starting point for any AI investment decision. Overselling the potential and underdelivering the result is how AI programs lose organizational trust — and trust, once lost, takes much longer to rebuild than the failed pilot did to run."
Elogic Commerce builds AI-powered B2B ecommerce platforms. Founded by Paul Okhrem in 2009. If you're evaluating AI recommendations for your B2B platform, talk to our team.
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