AI chatbot for NPS Follow-up: get context behind scores
SleekAI asks targeted follow-up questions tailored to each NPS score and customer segment, then tags the response for product, support, or sales using your own OpenAI, Anthropic, Google, or OpenRouter API key.
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An NPS score without context is just a number
NPS surveys collect a number from 0 to 10 and a short open-text field. Most users skip the open text, so the team ends up with a distribution of scores and no clue what is driving them. A detractor at 3 might be furious about a billing bug, or might just be a power user who reserves 10 for the second coming. A promoter at 9 might love the product, or might just be polite. Without the context, the score is noise.
SleekAI runs the follow-up as a conversation, not a form. The bot reads the score, the user's plan from wp_usermeta, their recent activity (last login, last feature used, ticket history), and asks the question that fits. A detractor gets "what is the main thing we should fix" plus a path to immediate support. A passive gets "what would make this a 9 or 10" to surface improvement opportunities. A promoter gets "what would you tell a friend about us" to capture quotable language for case studies.
It also adapts to the segment. A detractor on the Enterprise plan triggers an immediate escalation path to customer success. A passive whose last login was 30 days ago gets reactivation framing. A promoter who just upgraded gets asked about referrals. Generic NPS tools cannot do this because they do not see the user behind the score. SleekAI does, because it runs inside WordPress and has access to the same user data your product does.
Workflow
How the NPS bot turns scores into insight
Pass the score in
Map user segment data
Define the theme taxonomy
Wire detractor escalation
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A typical NPS follow-up conversation
Comparison
Generic chatbot vs SleekAI for NPS Follow-up
Generic chatbot
- Asks the same generic follow-up question regardless of score or segment
- Has no idea whether the respondent is a 30-day churn risk or new promoter
- Cannot route detractors to immediate support or promoters to referral flows
- Treats every "feedback" as flat text instead of tagging by theme and plan
- Cannot detect when a complaint embedded in an NPS response needs urgent action
SleekAI chatbot
- Tailors follow-up questions to detractor, passive, or promoter score bands
- Reads plan, last login, and recent feature usage to frame questions in context
- Tags responses by theme so product and ops can analyze drivers across segments
- Routes high-stakes detractors (Enterprise, churn risk) straight to customer success
- Captures quotable language from promoters for case studies and testimonials
Features
What SleekAI gives you for NPS Follow-up
Score-aware follow-ups
Detractors get "what should we fix" plus a path to immediate help. Passives get "what would make this a 9". Promoters get "what would you tell a friend". Three flows from one bot, all tuned to the score band.
Segment-aware routing
Enterprise detractors trigger an immediate customer success ping. Recently churned users get a different ask than active power users. The bot reads plan and activity meta so the segment context is baked into the follow-up.
Quotable testimonial capture
When a promoter explains why they love the product, the bot asks a follow-up that produces a usable quote and asks permission to attribute. Your marketing team gets fresh testimonials without a separate outreach campaign.
Use cases
Where this chatbot earns its keep
Detractor recovery
Catches frustration inside the NPS response and routes the case to support or customer success before the detractor decides to churn. Often defuses the underlying issue inside the follow-up itself.
Passive insight
Surfaces the specific gap between 7 and 9 from passives, who collectively are the largest source of actionable improvement signal but the lowest on engagement willingness.
Promoter testimonials
Captures fresh quotable language from promoters in their own words, with explicit permission, ready for marketing pages, case studies, and review responses.
The bigger picture
Why follow-up depth beats score volume
NPS programs collect millions of scores across the SaaS world and waste most of them because the follow-up text field is usually empty. The few users who do type something tend to be the most extreme, which skews the qualitative picture. The result is a metric that goes up and down with no clear story attached.
Teams treat NPS as a vanity number because the diagnostic signal is missing. A chatbot follow-up changes the economics of qualitative collection. The cost to the user is one more short conversation instead of typing a paragraph cold.
Response depth goes up because the bot asks one question at a time and adapts based on the answer. Tagging happens in real time, so the aggregate themes are visible the next morning instead of after a quarterly analyst sprint. Segment context is baked in.
A passive on the Enterprise plan saying "mobile is rough" carries different weight than a passive on the free trial saying the same thing. Generic NPS tools cannot tell the difference because they do not see the user behind the score. SleekAI does, because it runs inside WordPress and reads the same user object your product uses.
The follow-up is no longer a survey afterthought, it is a structured qualitative pipeline that feeds product, support, and marketing in parallel.
Questions
Common questions about SleekAI for NPS Follow-up
Pass the score as a variable when launching the chat after the survey submission. The bot reads it on every reply and adapts its prompting accordingly. You can also pull it from the survey CPT or postmeta if you store NPS responses as custom posts.
The system prompt defines three flows: detractor (what should we fix, would you talk to support), passive (what would make this a 9 or 10), promoter (what would you tell a friend, can we quote you). Each flow has 2-3 follow-ups so the conversation feels natural rather than scripted.
Yes. The bot detects when a detractor mentions a specific issue (billing problem, broken feature, bad support experience) and offers an immediate path to a human agent. For Enterprise detractors, you can route automatically to customer success with full context attached.
The system prompt includes a taxonomy of themes (product, pricing, support, mobile, performance, etc.). The bot picks the closest tag based on the conversation content and includes it in the response record. Aggregated weekly, the tags show what is driving scores in each segment.
Yes. The bot asks explicitly: "can we share this quote with attribution" or anonymously. Promoters who say yes get logged with the quote, name, role, and date for marketing use. The bot does not assume consent, which keeps your testimonial use compliant.
Anonymous responses still go through the same follow-up flow, but without the segment context. The tags still work, the responses still get logged. They are slightly less actionable than identified responses but still useful for aggregate analysis.
It can flag the obvious mismatches: a 10 with "I almost cancelled" or a 3 with "actually love it, just hate billing". These flags route to manual review so the score-to-action gap does not corrupt the aggregate. Most cases are not mismatches, but the flag exists for the edge.
Each follow-up writes a record to a CPT or webhook endpoint with score, segment, theme tags, conversation text, and any flags. Your product and ops teams query that record in their normal NPS dashboard. SleekAI does not replace your NPS tool, it enriches it with conversational context.
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