Customer feedback trends

Q: How do you analyze customer feedback at scale?

Cluster open-text feedback into themes using an AI model, then track the size and sentiment of each theme week-over-week. The interesting signal is movement: which themes are growing, which are shrinking, and what's brand new.

Weekly AI report on customer feedback — top themes, sentiment trends, and what changed since last week. Across support, reviews, and NPS.

Use this template How automations work

Runs weekly Scheduled Delivered to Slack, Email

Customer feedback trends

Active

Trigger

Runs weekly

Instructions

Synthesize all customer feedback from the past 7 days into a single, action-ready report.

Delivery

Slack · Email

Example output

A realistic sample of what this automation delivers — your version draws on your data.

Customer feedback trends Runs weekly · Sent to Slack, Email

Sample output

This week's themes (312 mentions)

Onboarding friction 49 (16%)

AI accuracy 38 (12%)

Pricing transparency 22 (7%)

Mobile experience 14 (4%)

Other 189 (61%)

Weekly themes — 312 pieces of feedback across NPS, support, and reviews

Onboarding friction (49 mentions, +18 WoW). Mostly first-source connection. New theme cluster: “I couldn’t tell which warehouse to pick.”
AI accuracy (38 mentions, steady). Mixed — accuracy praise outpaces complaints 2:1 this week.
Pricing transparency (22 mentions, +12). New: “Where’s the seat-based pricing for teams of 3?”
Mobile experience (14 mentions, +8). First time mobile has cracked the top 5.

Score movers. NPS holders trending positive (+4 pts). Detractors mostly clustered around the source picker — same theme as the activation funnel report.

Suggested action. Worth a quick fix on the warehouse picker copy this week. Larger mobile bet for Q3 planning.

The prompt

Copy, paste, customize.

Drop this into a Basedash automation. AI fills in the numbers from every source you've connected.

Instructions

Synthesize all customer feedback from the past 7 days into a single, action-ready report.

Sources to consider: support tickets, NPS / CSAT comments, in-app surveys, App Store / G2 / Capterra reviews, social mentions.

Output:

**Top themes.** 5–8 clusters with count, sentiment, and a single representative quote.

**What's new.** Themes that grew > 50% week-over-week.

**What's improving.** Themes that shrank.

**Specific accounts to acknowledge.** Up to three names worth a personal reply.

**Recommended action.** One bullet for product, one for support, one for marketing.

Data sources