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Turn search behaviour into a useful operating queue.
ParticleSearch separates committed shopper searches from intermediate request work, then connects behaviour, quality goals, fixes, health, and outcomes to a merchant review path.
The evidence loop
A signal becomes useful when it leads somewhere.
The dashboard is organised around the movement from observed behaviour to an action you can review later.
Signal
Observe
Evidence
Explain
Priority
Choose
Change
Act
Outcome
Learn
The merchant answer
Analytics are valuable when the denominator is clear and the signal leads to a decision. The dashboard distinguishes a shopper search from the requests used to render it, then connects that activity to quality goals, health checks, product readiness, fixes, and revenue context.
Guides and field notes
Go deeper when the decision needs detail.
Use these practical guides for the context, merchant decisions, and acceptance tests behind the capability you are evaluating.
Analytics dashboard guide
Move from a signal to a reviewable merchant decision.
Metrics guide
Understand what each metric means and cannot prove.
Search quality goals guide
Choose a target, baseline, and guardrails without gaming the result.
Golden queries guide
Protect important search promises while aggregate metrics move.
Revenue attribution guide
Connect search journeys to order evidence responsibly.
Catalogue health guide
Check operational trust before comparing search behaviour over time.
The path
Move from signal to a decision your team can defend.
Analytics should shorten the distance between an observed problem and the next useful investigation, not add another report to interpret.
See the shape of demand
Start with committed shopper searches, request coverage, result engagement, zero-result patterns, refinements, exits, filter combinations, segments, and the products shoppers interact with.
Separate symptoms from causes
Use catalogue health, product readiness, sync review, and query evidence to understand whether a problem is language, data, visibility, or behaviour.
Set a direction and measure the change
Publish one quality goal, review repeated issues in Fixes, and return to the same query and outcome signals after a correction. Revenue attribution adds context, with evidence boundaries kept visible.
Decision guide
Questions this capability should answer
Use these questions to decide whether this capability is the right next move for your store.
What counts as a shopper search?
Use the committed query a shopper left stable long enough to view or followed with a product action. Keep request executions separate because typing, refinements, and retries can produce several requests.
Which signal deserves attention first?
Start with the query, product, or storefront health issue that has enough evidence to justify a focused review.
Is this a data problem or a behaviour problem?
Compare query evidence with catalogue health and product readiness before changing ranking or adding a rule.
What does attributed revenue actually prove?
It shows an observed search touchpoint in a purchase path. Read it as context, not as a causal lift claim.
What is included
One view from shopper signal to merchant action
You do not need another dashboard full of unexplained totals. Each surface exists to answer a question your team can act on.
Shopper searches and request coverage
Review committed shopper searches as a distinct demand signal while keeping intermediate request executions visible for instrumentation, rate definitions, and operating context.
A shared quality goal
Choose zero-result rate, products-found rate, or search click-through, refine the target locally, and publish it as a durable direction for the store team.
Storefront and catalogue health
Keep storefront status, catalogue coverage, and delivery health visible when search behaviour needs an operational explanation.
Fixes and proof loop
Surface repeated issues only when enough evidence supports merchant review, then keep published changes under observation before suggesting another decision.
Regression protection
Protect proven product-query winners and important search promises so an aggregate improvement cannot quietly break a known buyer job.
Query and product analytics
Move from an aggregate trend to the exact query, product, collection, market, or locale that needs a closer look.
Funnel, filter, and segment views
See where shoppers refine, exit, open products, or narrow into thin results, then compare those patterns by surface, collection, and query outcome.
Revenue context
Connect search touchpoints to captured checkout outcomes when available. Treat attributed revenue as evidence of a path, not a causal lift claim.
Before and after review
Keep the original query, published change, and later evidence together so a merchant can tell whether the repair improved the intended behaviour.
A useful boundary
Good product decisions include what the feature cannot solve.
ParticleSearch gives your team more evidence and control. It does not remove the need for accurate product data, a clear merchandising goal, or a review of the result on your own store.
- Analytics describe observed store activity. They do not explain intent by themselves. A high-volume query still needs a merchant review.
- A quality goal shows direction, not causation or diagnosis. Improving the target is only healthy when relevance, protected queries, and commerce guardrails still hold.
- Attribution depends on the available storefront and checkout signals. It should be read with the evidence and confidence context shown in the dashboard.
- Prioritisation helps a team choose where to start. It does not replace a controlled test or product knowledge.
Keep exploring
See how this fits with the rest of the product.
See it on your store
Give your team a clearer way to improve search.
Install through Shopify, enable the app embed in your published theme, and run one proof check on your own storefront. Eligible Shopify installs · Shopify confirms trial eligibility before approval. Billing remains handled through Shopify.