Success metrics that matter
Most confusion about ShopGuide's dashboard isn't about picking the wrong number — it's about treating two different numbers as if they measured the same thing. Here's what each one actually counts, using ShopGuide's real definitions rather than an industry benchmark we'd have to make up.
What each metric actually counts
- Unique users — distinct shoppers, by persistent identity. One person, five visits, still one user.
- Sessions — distinct browsing visits. One shopper can rack up plenty of these over time.
- Interactions — sessions where the shopper actually sent a message, as opposed to a session where the widget loaded and nobody touched it.
- User messages — total message volume. A depth measure, not a reach measure.
- Chat orders / chat-attributed revenue — completed Shopify orders linked back to a chat thread, when a purchase follows a chat interaction.
They're kept separate on purpose. Collapse sessions into interactions, or interactions into orders, and you get comparisons that don't mean what they look like they mean — a month with more sessions but flat interactions is more people seeing the widget without engaging, not more engagement. See Common mistakes for the specific version of this to watch for.
Two ways to count a session
The dashboard lets you switch the session denominator between Shopify's own analytics count and ShopGuide's own thread-derived count. They don't always agree, and that's fine — Shopify's number depends on your store having granted the reporting access ShopGuide needs, and where it's missing, the dashboard shows those ratios as unavailable rather than quietly swapping in a different number. Where both exist, small gaps between them are expected, since they're counted two different ways. Pick one as your reference point and stick with it across reporting periods rather than switching sources mid-comparison.
Revenue is linked, not modeled
Chat orders and chat-attributed revenue aren't a statistical estimate — they come from directly connecting a completed Shopify order to the shopper's chat thread. That makes the number defensible: every order counted is a real, traceable one. It also makes it conservative. A shopper who chats, leaves, and comes back later without a traceable link to that thread doesn't get counted. Treat this number as a floor on chat's actual influence, not a ceiling.
Information Gaps as a quality signal
Separate from volume and revenue, the count and mix of categories showing up in Information Gaps Detection is a real leading indicator of chat quality — one that doesn't depend on shoppers bothering to leave a rating. A system_error count climbing for one shop is worth investigating right away; it's an app-health problem, not a content one. A knowledge_gap cluster around one topic tells you exactly what to add next, since recurring phrasings of the same question already get stacked into one row rather than scattered across duplicates.
Where a real comparison happens
A/B testing is the one place you get closer to a controlled experiment: it attributes both chat engagement and completed orders to each variant of a launch message or prompt, so you're comparing two configurations against real revenue instead of inferring cause from a trend line. See A/B testing and A/B test results — and give a test a full business cycle before treating an early lead as real.