Analytics and Reports
What these dashboards help you do
The analytics dashboards show how your cart drawer offers are performing. You can use them to answer questions like:
- Are we making more money after enabling upsells and add-ons?
- Which offers get the most clicks and sales?
- Did a change we made last week help or hurt?
Choose the right date range
Most charts and tables change based on your date filter. Picking the right range is the first step to getting a true result.
In the Analytics area, find the date picker (it may say something like Today, Last 7 days, or show a start and end date).
- Last 7 days: Good for quick checks and recent changes.
- Last 30 days: Better for stable results and normal ups and downs.
- Custom range: Best when you want to measure a campaign (like a holiday sale).
If possible, compare similar time windows (like Mon–Sun vs Mon–Sun). This helps because weekends often behave differently than weekdays.
Understand the main numbers
These are the most common stats you’ll see. The exact labels can vary, but the meaning is usually the same.
Revenue
- What it means: The money made from orders in the selected date range.
- How to use it: Watch for steady growth after you change an offer (like a new discount or new upsell item).
Orders
- What it means: How many orders were placed in the selected date range.
- How to use it: If revenue goes up but orders stay flat, you may be increasing order size (often a good sign).
Average order value (AOV)
- What it means: Average money per order.
- Simple formula:
Revenue ÷ Orders - How to use it: AOV is a key signal for upsells, add-ons, and bundles. If AOV rises while orders stay healthy, your offers are likely helping.
Upsell performance (in-cart recommendations)
Upsells are extra products shown to customers to add to their cart. The upsell dashboard helps you see which suggestions are working.
Common upsell metrics
- Views: How often the upsell was shown.
- Clicks: How often shoppers clicked the upsell.
- Adds (or Added to cart): How often the upsell product was added from the offer.
- Conversion rate: The % of views that turned into an add or purchase (depends on what the dashboard tracks).
- Revenue from upsells: Money connected to upsell items added through the offer.
How to interpret upsell results
- High views + low clicks: The offer is being seen, but it is not interesting. Try a different product, better price, or clearer message.
- Good clicks + low adds: Shoppers are curious, but something stops them. Check price, variants, shipping cost, and product mismatch.
- Low views: The offer may not be showing often. Check your targeting rules and triggers.
Add-on performance (checkbox items like shipping protection)
Add-ons are usually simple yes/no options in the cart (for example, shipping protection or a small extra item).
What to look for
- Attach rate: How often shoppers accept the add-on.
- Add-on revenue: Extra money earned from the add-on.
- Impact on checkout: If orders drop after you push an add-on too hard, you may need to soften the message.
Frequently Bought Together (FBT) performance
FBT offers bundle items that are commonly purchased together. This can increase cart size quickly when the bundle makes sense.
Common FBT metrics
- Bundle views: How often the bundle offer appeared.
- Bundle adds: How often customers added the bundle (or items from it).
- Bundle revenue: Revenue connected to bundle sales.
- Discount cost (if shown): How much discount you gave to drive those bundle sales.
How to interpret FBT results
- High views + low adds: The bundle may not feel like a “natural pair.” Try swapping one item for a better match.
- Good adds but low profit: The discount may be too big. Test a smaller discount.
- Strong results on one product: Duplicate the bundle idea for other best sellers (same pattern, different items).
Use trends to optimize offers
Once you understand the numbers, use them to make small, safe improvements.
Optimization checklist
- Pick one offer to improve (not all of them at once).
- Choose one change (product, discount, message, or placement).
- Measure before vs after using the same date length (example: 7 days before, 7 days after).
- Keep winners, remove losers after you have enough data.
Quick “what does this mean?” guide
This often means fewer people are buying, but each order is bigger (higher AOV). Check if a new offer is helping AOV but making checkout feel harder. Consider simplifying offers or reducing popups/messages.
Your store is getting more purchases, but upsells/add-ons may not be increasing cart size. Try improving your top 1–3 offers (better match, clearer benefit, small discount, or different placement).
Shoppers are interested but not convinced. Try: a better product match, a smaller price item, clearer value ("Pairs with..."), or showing the savings.
This can happen when repeat visitors already saw it, or traffic sources changed. Check a longer range (14–30 days) and compare similar days of the week.
Common mistakes to avoid
- Judging too fast: Avoid making decisions from a single day unless traffic is very high.
- Changing too many things at once: If you change products, pricing, and design together, you won’t know what helped.
- Only looking at revenue: Also check orders and AOV, so you understand why revenue changed.