Affiliate Analytics

#Understand Affiliate through three time axes

Affiliate orders can arrive long after the first invitation. In Affiliate → Analytics, do not reduce the business to one overall conversion rate. The page separates Reachout, Approval, and Post-approval performance, each with its own starting event and observation window.

Choose the Shop scope and time window, then refresh. Portfolio counts at the top describe the current operation and are not necessarily limited by the selected historical window.

APP CAPTUREThe English light-theme Affiliate Analytics overview with Reachout cohorts, responses, and Sample application trends.

#1. Reachout begins on the real invitation date

Reachout cohorts begin when invitations were actually sent. A “response” here means a Creator submitted a Sample application, not any chat reply.

Read:

• invitations sent;
• the day applications begin to appear;
• the cumulative response curve for the same cohort;
• whether differences across dates or Campaigns persist.

Recent cohorts have not completed the full observation period and should not be compared directly with a mature cohort’s final curve. When the page says a window has no mature cohort, more time is required; it does not mean the business produced no result.

#2. Approval begins on the application date

Approval analytics group Sample applications by application date, including Approved, Merchant Rejected, Ignored, Overdue on our side, and In-flight outcomes.

APP CAPTUREThe English light-theme Affiliate Approval analytics with application outcomes and AI versus non-AI decision origins.

Also inspect decision origin:

• AI decision: completed within an authorized automation boundary;
• Non-AI decision: completed by the team or another human process.

Origin alone is not a quality verdict. Read it together with approval, overdue, downstream orders, and current policy to decide which cases can be automated and which still need human review.

#3. Post-approval uses a fixed 90-day order window

Orders can lag far behind Sample approval, so Post-approval performance uses a fixed 90-day window. It reports:

• approved applications that produced orders;
• actual units sold;
• Sample performance on a shipped-Sample basis;
• sales performance on the full Affiliate-channel basis.

These bases answer different questions and must not be treated as one denominator. Use the shipped-Sample basis to evaluate Sample strategy; use the Affiliate basis to understand the Product’s wider channel performance.

#4. Trace every Sample Application in Details

Open Details to move from aggregate charts to one row per Sample Application. Start by choosing a record type:

• Sample review results: current decision, decision origin, and Creator performance at application.
• Fulfillment results: the same application context plus observed shipment, content, and post-application orders.

The date range always means the application date, not the review, shipment, or order date. Further filters include Shop, AI / Non-AI origin, Creator Open ID, Product ID, and current decision. Fulfillment records can also be filtered separately for observed shipment, attributed content, and post-application order.

Creator performance comes from a profile captured between 72 hours before and 24 hours after the application. Columns cover followers, units sold, GMV and GPM, promoted Products and brand collaborations, video and LIVE counts and averages, engagement, post rate, and commission rate. “—” means the value was not captured inside that window. TikTok does not state the measurement period behind each performance metric, so do not add them as if they shared one time basis.

Open a Fulfillment row to inspect the order lines and video or content IDs recorded after that application. One order line may link to more than one Sample Application, so summing rows across applications does not produce an unduplicated order total.

Results support paging and Excel download. Download exports every row matching the frozen search, up to 10,000 rows. Narrow the dates, Shops, or filters and export in parts when the result is larger.

A BD login receives the Details view only and sees Sample records attributed to that BD. Account owners and Affiliate supervisors can use Overview, Explore, and the full authorized Shop scope.

#5. Use Explore for deeper breakdowns

Switch to Explore, select a dataset and contract, then add metrics, dimensions, filters, time granularity, and a chart.

APP CAPTUREThe English light-theme Affiliate Explore builder with dataset, metrics, dimensions, filters, and chart configuration.

Follow three rules:

1. recompute rates from numerator and denominator instead of adding daily percentages;
2. keep time windows and filters consistent when comparing Campaigns, Products, or Shops;
3. do not add GMV from different datasets, because they may describe different event scopes.

Useful questions include which Campaign produces more mature applications, which Product has steadier post-approval orders, and whether one Shop’s overdue reviews cluster in a particular stage.

#6. Establish a review rhythm

#Daily

• Confirm invitations are progressing.
• Check application and approval backlog.
• Resolve reviews or Escalations that block AI work in Agent Workbench.

#Weekly

• Compare candidate, invitation, and application funnels by Campaign.
• Inspect approval, rejection, ignored, and overdue outcomes.
• Feed recurring questions back into Product Knowledge or Business Instructions.

#Monthly

• Evaluate Campaigns and Products with mature cohorts.
• Compare downstream outcomes from AI and human decisions.
• Adjust thresholds and Approval Policies using Sample cost and order performance.

Analytics narrows the next investigation; it should not replace Campaign and relationship context. When a result looks unusual, return to Campaigns for filters and decision records, then inspect the Creator relationship for the actual conversation history.