The Social Proof page (open it from Social Proof in the Platform sidebar)
lets you control which social proof badges get attached to your products. Badges are
stored on each product in a social_proof_badges field.
There are five badge types, each in its own card. Every card has an on/off toggle in
its top-right corner — turning it off disables that badge type entirely, regardless of
the values in its fields.
Important: Clicking Update saves your configuration, but it does not apply
the badges to your products on its own. After saving, you must run a product sync
for the new configuration to take effect.
Best Seller
Tags the top selling products over a specified date range.
Tags the products with the most purchases in a recent window.
| Field | Meaning |
|---|---|
| Time Window (days) | Look back this many days when counting purchases. |
| Number of Products | How many of the top-purchased products to tag as "Best Seller". |
Example: Time Window 30, Number of Products 10 → the 10 most-purchased products
over the last 30 days get the best_seller badge.
Trending Now
Products with increased velocity.
Tags products whose recent activity has accelerated compared to their normal baseline.
| Field | Meaning |
|---|---|
| Trending Event | Which event to measure: Adds to Cart or Purchase. |
| Increase Threshold | How much the velocity must rise to qualify: +50%, +100%, or +200%. |
| Minimum Views | Minimum activity a product needs before it can qualify — filters out noise from low-data products. |
| Recent Time Window (hours) | The "recent" period used to measure current velocity. |
| Historical Average Window (days) | The longer period used to establish the baseline average. |
How velocity works: velocity compares the event count in the recent window
against the per-window average over the historical window. A product is tagged
trending_now when its recent rate exceeds the baseline by more than the
selected threshold.
Hot Item
High purchase conversion relative to views.
Tags products that convert views into purchases at a high rate — i.e. products
in the top percentile of purchase rate (purchases ÷ views).
| Field | Meaning |
|---|---|
| Percentage Threshold | Which top slice of products to tag: Top 5% or Top 10%. |
| Time Window | Date range of view/purchase data to analyze: 7 Days or 30 Days. |
| Minimum Data Volume (views) | Minimum views a product needs to be ranked — ensures the conversion rate is reliable. |
Products that clear the minimum-views bar and land in the top percentile of purchase
rate get the hot_item badge.
New Arrival
Tag recently added products.
Tags products added (or updated) within a recent window, based on a date field
on the product.
| Field | Meaning |
|---|---|
| Date Field to Use | The product date field to read (e.g. updated_at). The dropdown lists the fields available on your catalog. |
| Time Window (days) | Products whose chosen date falls within this many days are tagged. |
A product gets the new_arrival badge when the selected date field is within the
time window of today. If the field is missing or the date can't be parsed, the
product is not tagged.
Dynamic Count-Based Badges
Time-window based activity counters.
Tags products with activity-based badges over the time windows you specify. Each of
the three fields is independent — leave one blank to skip that badge.
| Field | Meaning | Badge applied |
|---|---|---|
| Viewed Last X Days | Tag products viewed within the last X days. | viewed_last_X_days |
| Purchased Last X Days | Tag products purchased within the last X days. | purchased_last_X_days |
| Added to Cart Last X Days | Tag products added to cart within the last X days. | atc_last_X_days |
The number you enter is substituted into the badge name (e.g. Viewed Last 7
Days → viewed_last_7_days).
Saving changes
- Adjust the toggles and field values for each badge type.
- Click Update in the top-right corner. A confirmation toast appears on success.
- Run a product sync so the updated configuration is applied to your products.
Until a product sync runs, your products keep whatever badges they had before.