eCommerce platforms provide more data than most teams can use.
The challenge is not finding another dashboard. It is choosing the measures that explain the current business problem and lead to a decision.
A useful KPI has three qualities:
- •The team agrees on its definition.
- •It connects to a business objective.
- •Someone can act when it changes.
The following 15 metrics cover acquisition, on-site conversion, customer value, and buying experience. You do not need to track all of them with equal attention.
Acquisition efficiency
1. Traffic by source
Definition: Visits or sessions grouped by channels such as organic search, paid social, email, direct, or referral.
Use it to: Compare traffic volume with the quality and commercial outcome of each source.
Traffic alone is not success. A smaller channel may deliver more relevant visitors, stronger conversion, or greater revenue per visitor.
ShopAssist helps merchants convert more of that traffic to orders.
2. Customer acquisition cost
Formula: Sales and marketing cost allocated to acquisition / new customers acquired.
Use it to: Understand whether the cost of winning a customer is sustainable relative to gross margin and customer value.
Document which costs and attribution window are included. Otherwise, teams may compare different versions of CAC.
3. Cost per acquisition action
Formula: Channel or campaign cost / completed target actions.
Use it to: Compare the cost of an add to cart, lead, first purchase, or another defined action.
Name the action clearly. "CPA" is not useful when different teams use different conversion events.
On-site conversion
4. eCommerce conversion rate
Formula: Orders / eligible visits or sessions x 100.
Use it to: Track the store's ability to turn visits into purchases.
Conversion rate is affected by traffic mix, device, market, product, price, season, and measurement method. Segment before drawing conclusions.
ShopAssist can help shoppers get all the information needed to make a buying decision. This positively impacts sales conversion.
5. Product-view-to-add-to-cart rate
Formula: Sessions with an add-to-cart action / sessions with a product view x 100.
Use it to: Identify whether product pages create enough clarity and desire for the next step.
A weak rate may indicate product-market fit, price, imagery, availability, unclear information, or unresolved purchase questions.
6. Cart abandonment rate
Formula: 1 - completed purchases / initiated carts.
Use it to: Investigate checkout friction, unexpected cost, payment issues, delivery uncertainty, or unresolved doubt.
Do not assume every created cart represents a committed buyer.
7. Revenue per visitor
Formula: Revenue / visitors or sessions.
Use it to: Combine traffic quality, conversion, and order value into one commercial measure.
If traffic rises while revenue per visitor falls, acquisition may be reaching less relevant audiences or the buying experience may be underperforming.
8. Average order value
Formula: Revenue / orders.
Use it to: Understand basket value and test bundles, thresholds, product mix, and contextual recommendations.
AOV should be read with margin and return rate. A larger order is not automatically a more profitable order.
ShopAssist helps shoppers discover, compare and buy products more easily and quickly directly inside the conversational interface. It also provides upselling and cross-selling recommendations, which results in a higher average order value than the one on the shop.
Customer value and product health
9. Customer lifetime value
Definition: Expected value or contribution generated by a customer over the relationship.
Use it to: Set acquisition limits and understand the value of retention.
There are several valid models. Record whether the figure is revenue, gross profit, or contribution margin and whether it is historical or predictive.
10. Repeat purchase rate
Formula: Customers with more than one purchase / total customers in the cohort.
Use it to: Understand whether customers return after the first experience.
Compare cohorts by product, channel, and acquisition period.
11. Customer retention rate
Definition: The share of an eligible customer group that remains active over a defined period.
Use it to: Monitor loyalty and recurring demand.
Define what "active" means for the category. A monthly supplement brand and a furniture retailer require different windows.
12. Product return rate
Formula: Returned items / sold items x 100.
Use it to: Identify products or categories where expectations, fit, quality, or guidance may be weak.
Segment by return reason. Pre-purchase information can address some expectation problems, but it cannot solve product quality or fulfilment issues.
When shoppers confidently find what they are looking for, the product return rate goes down. ShopAssist makes product discovery and comparison easier, which can result in a lower product return rate.
Shopper guidance and service
13. Pre-sales question coverage
Definition: The share of relevant shopper questions that can be answered from approved product and policy sources.
Use it to: Find knowledge gaps before automating guidance.
Review unanswered and low-confidence topics rather than relying only on a single percentage.
ShopAssist sends merchants weekly reports where it automatically uncovers which information might be missing on the shop and is preventing their shoppers from buying.
14. Assisted journey actions
Definition: Product clicks, comparisons, favourites, add-to-cart actions, or orders following an assistant interaction.
Use it to: Understand what shoppers do after receiving guidance.
Use documented definitions and an agreed attribution window. An assisted order is not automatically an order caused solely by the assistant.
15. Customer-intent themes
Definition: Recurring needs, questions, objections, and missing-product requests found in conversations and search behaviour.
Use it to: Improve product pages, merchandising, content, catalogue structure, and future product decisions.
This is qualitative and quantitative. Frequency matters, but the commercial importance of a theme matters too.
ShopAssist handles those automatically and groups them under themes to surface exact buying intent. This way, merchants get data based on which they can develop or sell new products and improve shop and PDP (product discovery page) content.
Choose KPIs around the bottleneck
Different stages require different emphasis.
If acquisition is the problem
Start with traffic quality, CAC, CPA, and revenue per visitor.
If shoppers browse but do not act
Start with conversion rate, product-view-to-add-to-cart rate, search behaviour, purchase questions, and comparison usage.
If the margin is under pressure
Start with AOV, gross margin, return rate, CAC, and customer value.
If the team is overloaded
Start with question volume, answer coverage, escalation rate, response time, and repeated intent themes.
Build a useful weekly view
A practical weekly dashboard may contain only five measures:
- •One commercial outcome.
- •One acquisition measure.
- •One on-site behaviour measure.
- •One customer or margin measure.
- •One qualitative insight.
For example:
- •Revenue per visitor
- •CAC
- •Product-view-to-add-to-cart rate
- •Product return rate
- •Top unanswered purchase question
This gives the team a balanced view without turning reporting into the work itself.
Where ShopAssist data fits
ShopAssist provides easy-to-understand analytics for AI-attributed conversions, maps conversations that lead to sales, and gives information on all shopper questions, surfaces buying intent, and flags products and product information that your shoppers struggle to find.
Before using these measures for decisions, define terms such as assisted conversation, assisted order, attributed revenue, and attribution window.
The most valuable output may be the combination of behaviour and language: what the shopper asked, what guidance they received, and what they did next.
The practical takeaway
The best KPI set is not the longest one.
Choose measures around the most important bottleneck, define them consistently, and connect every metric to a possible action.
Data becomes useful when it changes what the team does next.
Choose one growth bottleneck and build a five-metric view around it.
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