Understanding insights

Insights surfaces a lot of metrics — search counts, match rates, no-match patterns, coverage gaps, orphan counts. The numbers alone don’t tell you what to do. A “low match rate” might mean great things or terrible things depending on context. A “high no-match count” might be an opportunity (lots of shoppers actively searching) or a problem (your data isn’t keeping up). The same number can read either way, and a dashboard that just shows you numbers leaves you to guess. This doc is about converting Insights from a dashboard into a feedback loop. It walks through what each tab shows, how to interpret the metrics correctly, and what concrete actions to take based on what you see. Insights is most useful when you check it regularly — at least once a week during launch, then less often as your data stabilizes. Treat it as a feedback loop, not a dashboard.

How to access Insights

Click Insights in the top nav of ModelMatch admin. You’ll see five tabs: Overview, Leaderboards, Activity Log, Orphans, and Data Health. The first four are available on every plan, with the analytics history window varying by tier (30 / 90 / 365 days for Starter / Growth / Professional). The Data Health tab is Professional-only and provides the deepest diagnostic surface.

Overview tab

What it shows

The Overview tab is the entry point — high-level numbers that tell you whether your data is doing its job. The four metrics that matter most:

• Total searches — How many times shoppers entered a brand and model into your storefront blocks (fit check widget, standalone finder, catalog fit check) over the analytics window.
• Match rate — Percentage of those searches that returned a successful compatibility result.
• Where searches happen — A breakdown of which storefront blocks are driving searches: PDP fit check vs standalone finder vs catalog fit check.
• Result breakdown — Of all searches, what proportion returned Verified fit, Likely fit, or No match.

The first two are the headline numbers. The other two contextualize them — where shoppers are searching tells you which surfaces are working, and the result breakdown tells you the shape of your match rate (lots of “Likely” vs lots of “Verified” means very different things).

How to interpret match rate

Match rate is the most important metric on Overview, and it’s the easiest one to misread. Here’s how to read it.

• High match rate (above 70%) — Most shoppers are finding what fits. Your data has good coverage for the searches happening on your store. This could mean shoppers are only searching for things you already have data for (selection bias), but in most stores it’s a healthy sign.
• Medium match rate (40–70%) — The most common range during early operation. You’re getting both successful and unsuccessful searches. Look at the no-match patterns on the Leaderboards tab to find what to add — that’s almost always where the biggest match-rate improvements come from.
• Low match rate (below 40%) — Either your data is missing common models, or shoppers are searching incorrectly (typos, alternate spellings, wrong brand names). Both are fixable. Aliases for common misspellings handle the second; importing more data handles the first.

The match rate to aim for in steady state: above 60%. Below that, you’re leaving sales on the table — every shopper who searches and gets no match is a shopper who came to your store with intent and left without converting.

What to do if Overview looks off

If searches are zero or near-zero:

• The storefront blocks may not be installed or may not be visible. Check Storefront admin or visit your live storefront in an incognito window to verify.
• Your store may not have enough traffic yet. If installs look right, give it time and focus on other priorities.

If match rate is unexpectedly low:

• Go to Leaderboards to see what shoppers are actually trying to find. The top no-match models are usually the easiest wins.
• On Professional, check Data Health for coverage gap analysis — it identifies brands and series where you’re missing common models.

[Screenshot: Insights Overview tab showing total searches, match rate, where searches happen, and result breakdown.]

Leaderboards tab

What it shows

Leaderboards rank what shoppers are actually doing on your store. Four lists:

• Top searched models — The brand+model combinations shoppers enter most often, regardless of whether they matched.
• Most matched models — Models that successfully match against your records most often (high traffic and high match rate).
• Top no-match models — Models shoppers searched for that returned no result. This is your priority list for adding data.
• Repeated no-match patterns — Models that fail repeatedly across multiple sessions, not just once. Persistent gaps, not one-off typos.

Top no-match models is the most useful list

This is where Leaderboards earns its place. Use it like this:

1. Sort by search count — the entries at the top are searched most often.
2. For each top no-match, ask: “Should I have this in my data?”
3. If yes, find the relevant product in your catalog and add a record that maps it to this model. Manual entry on the Records page is fastest for one-offs.
4. The next shopper who searches that model gets a successful match instead of a dead end.

Set a habit: review top no-match weekly during your first 90 days. After that, monthly is usually enough as the obvious gaps get filled. The marginal effort drops over time, but the early weeks are where this list is densest with easy wins — every fix you make compounds, because every shopper who searches the same model from then on benefits.

Most matched models is your validation signal

The most matched models tell you what’s working. If a brand or model family appears here repeatedly, your data is doing its job for that category. If a brand or model family that should be a major part of your business is conspicuously missing from this list, that’s a signal — either your data is incomplete for that category, or your storefront blocks aren’t placed where shoppers for that category look.

[Screenshot: Leaderboards tab showing top no-match models with search counts.]

Activity Log tab

What it shows

Activity Log is a chronological feed of recent events on your store, merged into one stream:

• Search events — Each row is a shopper search with brand, model, result status (match / no match), and timestamp.
• Import events — Each CSV import, manual entry, or Shopify import shows up with row counts and the import kind.

A type filter at the top lets you switch between All activity, Searches only, and Imports only, depending on what you’re investigating.

When to use it

Activity Log is the place to look when something unexpected happens:

• “Did my CSV import actually run?” — Filter to Imports, look for the most recent entry.
• “Why did my record count change overnight?” — Filter to Imports; auto-sync events show up here.
• “Is auto-sync working?” — Filter to Imports; entries with kind shopify-import are auto-sync activity.
• “What did shoppers search for in the last hour?” — Filter to Searches.

The log is bounded by your plan’s analytics history window:

• Starter — 30 days
• Growth — 90 days
• Professional — 365 days

For longer history, you’d need to export your data periodically — or upgrade to a tier with a longer window.

Orphans tab

What it shows

The Orphans tab is the dedicated workspace for managing orphaned records — records pointing at Shopify products that no longer exist. (The full orphan workflow is documented in Data management; this is the quick orientation.)

• Each orphaned record is shown with its compatibility data and a “Last flagged” timestamp.
• Per-row Unmap and Delete actions are available on every plan.
• Bulk Unmap and Bulk Delete are available on Growth and Professional plans.
• A “Run check now” button triggers an on-demand orphan sweep, useful right after big product changes.

What to do when orphans appear

For most cases:

1. Click Unmap. This preserves the compatibility data — brand, model, category — and just clears the broken product link. The record becomes Unmapped instead of Orphaned.
2. Decide later whether to remap to a different product, leave the record as Unmapped, or delete it entirely.

For records that are truly stale (the compatibility data itself is no longer relevant — the model line is discontinued, the part is no longer in your catalog):

• Click Delete. Permanent removal, gone from the database.

For orphans that appear after big product changes (catalog cleanup, vendor changeover, a third-party app’s bulk delete):

• Use bulk operations (Growth and Professional only) to handle dozens or hundreds of orphans in one action.

If your orphan count starts climbing unexpectedly, something is deleting Shopify products. Check whether auto-sync is configured the way you want — and whether a third-party app is bulk-deleting products without you realizing it.

[Screenshot: Orphans tab showing several orphaned records with Unmap and Delete buttons.]

Data Health tab (Professional only)

What it shows

Data Health is the deep diagnostic surface, available only on Professional plans. It surfaces five things, all aimed at telling you what to fix next:

• Quality score — A composite metric reflecting record completeness, mapping rate, and orphan rate. Higher is better. Useful as a single number to track over time.
• Coverage gaps — Brands and model series where you have records but appear to be missing common models. The system compares your coverage against expected model lists derived from your existing records and from web research, surfacing the holes.
• Unmapped product alerts — Shopify products in your catalog that look like they should have compatibility records (based on title, vendor, and type) but don’t. Useful for catching products that slipped through your last import.
• No-match pattern alerts — Sustained patterns of no-match searches that haven’t been addressed yet. These are the highest-priority items to fix because they represent ongoing lost sales.
• Priority review signals — A combined list of records or products that probably need attention, ranked by estimated impact (search volume × no-match rate × revenue potential).

Data Health turns “lots of metrics” into “here’s what to do next.” If you’re operating an active catalog and want to keep your data healthy without manually mining the other tabs every week, Data Health does most of that mining for you.

[Screenshot: Data Health tab showing quality score, coverage gaps, and priority review signals.]

A weekly insights routine

Most merchants benefit from a 10–15 minute weekly review. Build the habit and the work compounds — your match rate climbs, your orphan count stays at zero, and you stop being surprised by what shoppers are doing on your store.

A simple weekly routine:

1. Open Overview. Note total searches and match rate. If anything feels off compared to last week, dig in.
2. Open Leaderboards > Top no-match. Pick three to five entries. For each, ask: “Should I have this in my data?” If yes, take 30 seconds to add the record.
3. Open Orphans. Review the orphan count. If non-zero, click Unmap or Delete on each — or use bulk if you’re on Growth+ and there are several.
4. Skim Activity Log if anything unexpected happened recently. Imports you didn’t run, sync activity you don’t recognize, search spikes from a campaign you didn’t know about.
5. (Pro only) Check Data Health. Review priority review signals and act on the top one or two.

That’s it. Most weeks, this takes 10 minutes and meaningfully improves your match rate over time. The goal isn’t to make every metric perfect — it’s to keep noticing where shoppers are slipping through and patching those holes one at a time.

Insights tells you what shoppers are doing. When something looks broken instead of just suboptimal, see Troubleshooting for common issues and how to fix them.