What are post-purchase CX metrics?
Post-purchase CX metrics are measurements of the customer experience after the sale: how quickly and completely questions are solved, how customers feel about it and whether they come back. They connect support work to revenue.
They matter because retention is where aftersales service pays off. Reichheld and Sasser in Harvard Business Review (1990) found that cutting customer defections by 5% raised profits by 25% to 85% in the service businesses they studied, and Harvard Business Review notes a new customer can cost five to 25 times more to win than an existing one costs to keep.
What are the formulas for post-purchase CX metrics?
| Metric | Formula | What it tells you |
|---|---|---|
| Resolution rate | Resolved conversations ÷ total conversations × 100 | How much demand you actually solve |
| First contact resolution (FCR) | Resolved in first contact ÷ total resolved × 100 | Whether customers must come back |
| CSAT | Positive ratings (4–5 of 5) ÷ total ratings × 100 | Satisfaction with the interaction |
| Net Promoter Score (NPS) | % promoters (9–10) − % detractors (0–6) | Likelihood to recommend |
| Customer Effort Score (CES) | Sum of effort ratings ÷ number of responses | How easy it was to get help |
| First response time | Sum of time to first reply ÷ conversations | How fast customers hear back |
| Time to resolution | Sum of time from open to resolved ÷ resolved conversations | How long problems last |
| Contact rate per order | Conversations ÷ orders × 100 | Whether problems are prevented |
| WISMO share | Order-status conversations ÷ all conversations × 100 | Delivery communication gaps |
| Return rate | Units returned ÷ units sold × 100 | Product and listing fit |
| Repeat purchase rate | Customers with 2+ orders ÷ total customers × 100 | Loyalty after the first order |
| Cost per conversation | Total support cost ÷ conversations | Efficiency of the support operation |
Use the same time window and the same definition of “resolved” for every channel and for AI and human conversations.
Which metrics measure support quality?
Resolution rate
The share of conversations fully solved. Define "resolved" strictly: the customer did not reopen or contact you again on the same issue within a set window, such as 72 hours. See resolution rate explained.
First contact resolution (FCR)
The share of resolved conversations that needed only one contact. Low FCR usually means missing data (the agent cannot see the order), missing permissions (the agent cannot refund) or unclear policy.
CSAT
A one-question survey after the conversation. Count 4 and 5 on a 5-point scale as positive. Watch response rates: a 5% response rate says more about who answers than about everyone.
Customer Effort Score
Asks how easy it was to get help. It is especially useful for returns and warranty flows, where effort is the main complaint.
NPS
A relationship metric rather than a support one. Use it to track overall loyalty, and use CSAT and CES to judge individual interactions.
Which metrics measure speed?
First response time and time to resolution. Report medians as well as averages, because a few very old conversations distort the average. Speed without resolution is a trap: a quick first reply that solves nothing lowers FCR and raises volume.
Which metrics connect support to business outcomes?
Contact rate per order
The best single test of whether aftersales problems are being prevented. If orders grow 20% and conversations grow 5%, prevention is working.
WISMO share
High WISMO share points to gaps in delivery communication. See the WISMO guide for fixes.
Return rate
The NRF and Happy Returns 2024 returns report estimated 16.9% of US retail sales would be returned in 2024. Compare your own return rate by product, size and reason, rather than against a single industry average. See returns customer service.
Repeat purchase rate
The share of customers who order again. Compare customers who contacted support with those who did not, and customers whose issue was resolved on first contact with those who needed several. That comparison shows the revenue value of good aftersales service.
Cost per conversation
Total support cost (people, tools, AI fees) divided by conversations. Track it next to resolution rate so cost cuts do not come from solving less.
What should a post-purchase CX dashboard include?
Keep it to one screen with three rows, reviewed weekly.
- Quality: resolution rate, FCR, CSAT (split by AI and human).
- Speed and cost: median first response time, median time to resolution, cost per conversation.
- Business: contact rate per order, WISMO share, return rate, repeat purchase rate.
Below it, list the top five contact reasons this week versus last week and one action per reason. A metric without an owner and an action rarely moves.
See quality across AI and human conversations. Insights detects issues in real time and runs quality monitors.
How do you measure AI in post-purchase support?
Measure AI on resolution, not deflection. Deflection counts a conversation as handled when the customer stops replying, even if they gave up or called instead. Resolution counts only conversations where the problem was solved.
- Use the same resolved definition for AI and humans.
- Track AI resolution rate by intent (WISMO, returns, warranty), since some intents automate far better than others.
- Track CSAT on AI-resolved conversations separately.
- Track escalation quality: did the person who picked it up have the context they needed?
How Aftersales reports it
Agent is billed at $0.99 per resolution, counted once per conversation, and conversations it cannot help with are free, so the resolution count is also your AI bill. Aftersales customers see a median 71% resolution rate after 90 days. Insights adds real-time issue detection, quality monitors and forecasting across AI and human conversations in Inbox. See pricing for plan details.
What are common mistakes with CX metrics?
- Counting a conversation as resolved when it was only closed.
- Using different definitions for AI and human conversations.
- Reporting averages only, which hide long-tail waits.
- Chasing CSAT with a low response rate.
- Tracking support metrics without any business metric next to them.
- Comparing against generic industry benchmarks instead of your own trend.