Product and brand gap analysis

See which product categories and brands each Shopify wholesale account under-buys compared with similar customers — sized in dollars per year, so your sales team knows which gap is worth the call.

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The short version

  • Every customer card lists Categories to pitch and Brands to pitch — the product types and vendors this account under-buys compared with customers who buy like they do.
  • Each gap is sized in dollars a year, so a sales team can tell a $4,200 opportunity from a $200 one before picking up the phone.
  • Benchmarks are weighted by what customers actually spend, so a crowd of small single-category buyers can't set an unreachable bar for your largest accounts.
  • Each row shows a last bought date, which distinguishes a category they have never tried from one they have quietly stopped buying.

The most valuable thing you don’t know about an account

Ask a wholesale sales team what their top customer buys and they will tell you instantly. Ask what that customer buys from someone else and the room goes quiet.

That second number is where the growth is. An account already buying from you, already set up with terms and shipping, already answering the phone, is the cheapest revenue in the business — and the categories they have never ordered from you are the shortest path to it.

The trouble is that absence is invisible. Nothing in your order history says “this customer has never bought filters.” You have to notice what isn’t there, across hundreds of accounts and hundreds of product types, and nobody does that by eye.

What RevLogic surfaces

Every customer card carries two lists:

  • Categories to pitch — product types this account under-buys.
  • Brands to pitch — vendors this account under-buys.

Each row is a comparison against customers who buy like they do, with the gap converted into an annual dollar figure. A typical row reads:

Filtration — ~$4,200/yr Similar customers spend 11% · they’re at 2% · last bought 14 months ago

That single line tells a sales team everything they need to open the topic: what the category is, roughly what it is worth, how far off this account is, and whether this is a category they have never tried or one they used to buy and drifted away from.

Sized in dollars, ranked by size

An unpriced list of gaps is a list of trivia. Twelve categories a customer doesn’t buy is interesting; knowing that one of them is worth $4,200 a year and the other eleven are worth $150 between them is actionable.

RevLogic converts each gap into an estimated annual value based on the account’s own spending level, then sorts by it. The biggest opportunity is the top row. For a sales team with time for three topics on a call, that ordering is the whole feature.

Benchmarks that hold up

The comparison is only as good as the peer group, and the naive version of this fails badly.

If you compare every customer against a plain average of your base, small single-category buyers drag the average down and every large account appears to have gaps in categories it has no business buying. RevLogic weights the benchmark by how much each customer actually spends, so the target share reflects real purchasing at real volume rather than a headcount of small accounts.

The result is a comparison your sales team will believe, which matters more than the statistics: a gap list that produces one absurd suggestion gets ignored entirely.

Gap or lapse?

The last bought date on each row does quiet, important work. Two rows can show the same dollar gap and mean opposite things:

  • Never bought — a genuine cross-sell. This is a new conversation, and it may need a sample, a price, or a reason.
  • Bought, then stopped — something happened. A stockout, a price rise, a competitor, a change of buyer. That is a recovery conversation, and it is usually the easier of the two to win.

Keeping the date on the row means the sales team can tell which call they are making before they dial.

How it fits with the rest

Gap analysis answers what to pitch this account. It pairs with:

Frequently asked questions

How does RevLogic know what a customer should be buying?
It compares each account against customers with a similar buying mix, not against your whole customer base. RevLogic works out the share of spend that comparable customers put into each product type and vendor, then compares that with this account's own share. Where their share is materially lower, that is a gap — and the difference is converted into an annual dollar figure using what the account actually spends.
What does the dollar figure on a gap mean?
It is an estimate of what closing the gap is worth to you per year. A row reading "Similar customers spend 11%, they're at 2%" with "~$4,200/yr" means that if this account shifted to a typical share for that category at their current spend level, it would be worth roughly $4,200 a year in additional revenue. It is a sizing tool for prioritizing calls, not a forecast.
Why are benchmarks weighted by spend?
Because an unweighted average lets small customers distort the target. If fifty tiny accounts each buy one product type and one large account buys across twenty, a plain average makes every large account look like it has enormous gaps everywhere. Weighting the benchmark by what each customer actually spends means the comparison reflects real purchasing behavior at real volumes.
What is the difference between a product gap and a cross-sell recommendation?
A product gap is about the account, and it is answered before the call — what whole categories or brands is this customer not buying from us? A cross-sell recommendation is about the cart, and it is answered during the quote — what specific products go with what they are ordering right now? Gaps set the agenda for the conversation; cross-sell fills out the order.
Does gap analysis work if I don't use Shopify's product types or vendors?
It works from whatever you have. RevLogic reads the product type and vendor fields on your own Shopify products, so the quality of the analysis follows the quality of your product organization. Stores with consistent product types and vendors get the sharpest gaps; stores with sparse fields still get brand-level analysis where vendors are set.

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