Helping a shopper choose a product is one part of running a store. Understanding which products sell, what acquisition costs and whether customers return is another. Algoshop AI Sales Chatbot addresses storefront conversations; a Shopify analytics dashboard helps the team examine the wider business without confusing a busy storefront with profitable growth.
Fullmetrix brings Shopify revenue, orders, products, customers, cohorts, and forecasts into dashboards alongside advertising and analytics connections. Its listing names Google Ads, Meta Ads, TikTok Ads, and Google Analytics, plus scheduled email or Slack reports. The useful advantage is a connected operating review rather than another isolated sales total.
This guide explains how to use those reporting areas in a weekly review: what to compare, which customer questions to ask and how to turn findings into next steps. The scorecard definitions are merchant guidance, not undocumented Fullmetrix formulas or results from a store we tested.
Decide what the meeting must resolve
Start with three decisions: should acquisition spending change, does a product or operating problem need intervention, and should retention work change? Add metrics when they answer one of those questions.
The opening screen can contain net merchandise sales, paid orders, AOV, ad spend, new customers and a mature repeat-purchase cohort. Keep channel attribution and forecasts in supporting views. A forecast informs planning; it does not belong in the actual-sales total.
Choose a calendar and reporting time zone. Freeze the review time and record late sources. A Monday morning snapshot may differ from a Tuesday snapshot because refunds or ad data arrived later. Source freshness belongs in the meeting packet.
Revenue, order volume and acquisition spending answer different questions. If revenue rises while orders fall, check the products and baskets behind the change before celebrating it. Fullmetrix's connected reporting is useful here because a product-mix question can quickly become a customer or advertising question.
Assemble the reporting view with Fullmetrix
The official listing describes store dashboards, advertising connections, cohort analysis, forecasts and scheduled email or Slack digests. Connect the accounts you use and reconcile a representative period before relying on the meeting packet.
Start with sales and orders. Trace several paid orders, including a discount and a refund, to establish the reporting basis. Add advertising accounts and compare their account IDs, date ranges and currencies with the source reports. Add customer and cohort views after the order definitions are clear.
Compare customer groups over time to understand when shoppers return and buy again.
Schedule the email or Slack digest for the people making the decisions: the media buyer needs spend and acquisition context, merchandising needs order mix, and operations needs refund exceptions. Choose a time when connected sources are sufficiently complete, marking delayed inputs in the packet. This is the merchant's meeting design, rather than a claim that every connector refreshes together.
Move from a headline number to a business question
Fullmetrix's value is the ability to follow a question across different parts of the business. Revenue can begin the review, but product mix, customer history and acquisition spending explain what deserves attention. Its official website describes product and variation views, customer cohorts and segmentation, multi-store reporting, and order-level profit tracking alongside the connected advertising accounts.
Suppose sales rise while order volume falls. Begin with the store's product and order views: did a higher-priced product account for the change, or did a few larger baskets shift the average? Then look at customers: is the growth coming from existing buyers or new acquisition? Finally, compare spending and costs. A stronger revenue total and a stronger business outcome are not always the same thing.
One question, several useful views
Merchandising · What sold differently?
Review products, variants and order mix. Identify the products behind the change before deciding to repeat a promotion or increase stock.
Acquisition · What did new demand cost?
Review the connected advertising sources alongside store orders. Keep attribution definitions visible when comparing channels rather than adding overlapping platform claims.
Retention · Who returned?
Use customer and cohort views to compare groups at the same age. A mature cohort answers a different question from this week's new-customer count.
Finance · What did those orders leave?
Where the relevant cost data is configured, inspect the supported profit view. The website describes product costs, shipping, payment fees and advertising spend; check which actual inputs your store supplies.
Scheduled Slack or email reports make this useful outside the dashboard. A team can receive the recurring review packet instead of someone collecting screenshots from each system. The follow-through is still a human decision: a product-mix change belongs with merchandising, an incomplete source with the reporting owner, and a spending decision with the media buyer.
The website also describes customer reactivation tools, including WhatsApp workflows. Those are separate from the analytical example here. Begin by understanding the customer group and the appropriate campaign permissions; do not assume a retention chart itself launches a campaign or guarantees that customers return.
Define the metrics once
These definitions are for the merchant scorecard, not assertions about Fullmetrix's internal formulas.
| Metric | Definition used here |
|---|---|
| Net merchandise sales | Item sales after discounts and merchandise refunds; shipping and tax excluded |
| Paid orders | Qualifying paid orders in the selected period |
| AOV | Net merchandise sales ÷ qualifying orders on the stated basis |
| Ad spend | Recorded spending across distinct accounts, in one currency and calendar |
| New customers | Customers whose first qualifying purchase falls in the period |
| Blended acquisition spend/customer | Ad spend ÷ qualifying new customers |
| 60-day repeat rate | Customers with a later qualifying order within 60 days ÷ customers in a fully observed acquisition cohort |
If period revenue includes refunds from older orders while the count contains this week's orders, AOV is a period ratio rather than a clean basket-size measure. Keep that definition explicit or calculate a separate order-cohort AOV.
Name the source account for every metric. Advertising conversion revenue and store sales are different measures. Switching between them halfway through the meeting changes the interpretation.
A complete weekly scorecard
Daily revenue charts and breakdowns help you follow sales trends.
Build the packet from your connected store's actual reports. This review checklist pairs each question with the information needed to answer it; it is not a screenshot of Fullmetrix results.
Swipe horizontally to read the full table.
| Review area | Bring to the meeting | What the team should resolve |
|---|---|---|
| Sales and orders | Matching periods, order count and the sales definition | Whether changes reflect purchase volume, basket size or reporting treatment |
| Product mix | Product and variation performance | Which items explain the change and whether availability affected sales |
| Advertising | Account-level spend and attributed results | Whether the compared accounts, windows and currencies match |
| Customer groups | Acquisition dates and customer history | Whether new and returning buyers are contributing differently |
| Retention | Cohorts with equal observation periods | When buyers return, rather than whether a new cohort has already matured |
| Profit and refunds | Supported cost inputs and refund records | What remains after costs and what needs an operational response |
Read the rows together. A larger average basket may come from higher-priced products or returning buyers, not a healthier acquisition campaign. A strong revenue total can also conceal missing cost inputs. Follow the relevant product, customer or order detail before assigning a cause.
Finish with a short explanation in plain English: what changed, what evidence supports that reading and what still needs checking. That is more useful than a packet containing every available chart with no clear decision.
Reconcile revenue without inventing an organic remainder
Advertising-attributed revenue and store sales need not match. Their difference is not automatically organic revenue. Platforms may claim overlapping orders, use different attribution windows or report conversions on another date.
Create a source note listing value, definition and reason for difference. Compare click/view attribution, conversion windows, order time, refund treatment, tax, shipping and exchange rates. Trace material discrepancies to individual records where supported.
Spend from distinct ad accounts can usually be summed when the basis matches. Summing their attributed revenue is more problematic. Consolidation makes sources easier to compare; it does not create a causal or deduplicated attribution model.
If Shopify and the dashboard disagree on store sales, resolve missing or differently treated orders before analyzing campaign efficiency. A connector discrepancy and a business problem require different owners.
Read cohorts and forecasts on their own terms
Fullmetrix's official website describes cohort retention curves and RFM segmentation—recency, frequency and monetary value. These provide two different perspectives: cohorts compare customers grouped over time; RFM considers how recently customers bought, how often they buy and how much they spend. A recent first-time buyer and a formerly frequent buyer may therefore need different follow-up, even when both made no purchase this week.
Choose an observation window suited to the purchase cycle. A refill business and a furniture store need different expectations. Compare cohorts at the same age, and inspect promotion, seasonality and product mix before attributing a difference to retention messaging.
Use cohorts to ask when customers return and whether later acquisition groups behave differently at the same age. Use customer segments to decide which group deserves attention next. For a replenishable product, review the purchase cycle before choosing when to contact buyers; for a durable product, consider relevant complementary purchases rather than assuming the same item should be reordered. These are campaign-planning choices, not guaranteed outcomes from segmentation.
Forecasts provide a separate planning view based on historical trends. Keep the generation date and horizon with the forecast and compare it with actual sales over the matching period. If the two diverge, review promotions, product availability and demand changes before revising stock or cash plans. A forecast is not another measured revenue stream.
Convert the exceptions into assigned work
Turn the weekly review into assigned work
Should acquisition spend change?
Responsible person: Media buyer
Evidence for the next meeting: Campaign spending changes, same-definition new-customer counts and a bounded test proposal
Is the order mix hiding a stock problem?
Responsible person: Merchandising lead
Evidence for the next meeting: Product mix, large-order concentration and entry-product stock history
What caused the refund increase?
Responsible person: Operations lead
Evidence for the next meeting: Reasons, original order dates, affected SKU counts and corrective action
The retention lead separately compares mature cohorts at the same age, using product and promotion mix to interpret differences. The analytics owner resolves incomplete sources before a business conclusion is assigned to them.
“Look into ads” has no completion condition. “Bring the campaign spend change and a consistent new-customer count before proposing a budget adjustment” does. Put these tasks first on the next meeting's agenda.
A thirty-minute meeting can use five minutes for source completeness and old actions, ten for scorecard exceptions, ten for the most consequential investigations and five for assignments. Close tasks when the evidence answers the question, including when the explanation is a reporting mismatch.
The product LTV guide takes acquisition-product analysis deeper; the profit analytics guide supplies cost coverage. Fullmetrix fits the team that needs a connected operating review. Its useful output is a scorecard with a reliable basis and a short action register.
