Ideas for Using Zigpoll MCP

Practical use cases for connecting Zigpoll's MCP server to automated workflows, reporting, and customer intelligence

Zigpoll's MCP server exposes 29 tools that let AI assistants read your survey data, run analytics, and create surveys programmatically. This guide covers practical use cases for SaaS companies and solo founders who want to build automated workflows on top of their survey data.

New to the Zigpoll MCP? Start with the MCP setup guide to connect your AI client and get your API key. Then come back here for what to build with it.

Getting Started With Claude Code

The fastest way to start building with the Zigpoll MCP is Claude Code, Anthropic's CLI tool for working with AI directly in your terminal.

# Install Claude Code
npm install -g @anthropic-ai/claude-code

# Add the Zigpoll MCP server
claude mcp add --transport http zigpoll https://mcp.zigpoll.com/mcp

Once connected, you can query your survey data, test analytics tools, and prototype automations conversationally before writing any deployment code. Claude Code can also generate the cron functions, webhook handlers, and integration scripts described in the use cases below.

For IDE-based workflows, you can also connect the MCP to Cursor or Windsurf — see the MCP setup guide for instructions.

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Use Case: Automated Reporting

Set up scheduled reports that pull survey analytics and deliver them to your team — without anyone checking a dashboard.

Weekly survey digest

Deploy a cron function (Vercel, Cloudflare Workers, or similar) that runs on a schedule:

1. Call analyze_response_trends with the last 7 days of data

2. Call get_insights to generate an AI summary of key findings

3. Format and post the digest to Slack, email, or Google Sheets

Monthly attribution report

1. Call analyze_order_values to get revenue breakdown by survey answer

2. Call analyze_location_metadata for channel and traffic source analysis

3. Generate a channel-by-channel attribution report with revenue attached

Trend monitoring and anomaly detection

1. Call analyze_response_trends and correlate_metadata_with_answers on a schedule

2. Compare this period to the previous period

3. Fire an alert when a keyword or theme spikes — for example, "shipping" complaints up 4x in exit-intent responses

This turns survey analytics from a pull system (someone has to check) into a push system (insights come to you). See Analytics, AI, and Reporting for what's available in the dashboard, and use the MCP to automate what you'd otherwise do manually.

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Use Case: Auto-Generating Follow-Up Surveys From Negative Feedback

When negative feedback comes in, programmatically create and deploy a targeted follow-up survey to dig deeper — no manual review required.

How it works:

1. A webhook fires when a new response is submitted

2. Your server calls get_response_summary to check if the response is negative (low NPS score, complaint keywords in open-ended answers)

3. If it hits your threshold, use create_poll + create_slide to generate a follow-up survey tailored to the complaint — for example, "You mentioned shipping was slow — can you tell us more?"

4. publish_poll deploys the follow-up survey

Variations:

• Follow up on NPS detractors only (scores 0–6) — see Launching an NPS Survey

• Follow up on specific complaint categories (pricing, shipping, product quality)

• Build escalation chains: first survey catches the signal, second survey qualifies it, third survey gets the detail — similar to the Layered Post Purchase Surveys pattern

Most companies collect negative feedback and then nothing happens until someone manually reviews it. This closes the loop automatically.

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Use Case: Generating Help Docs From Support Requests

Use patterns in your support survey responses to identify what help documentation to write — and draft it from what customers actually ask.

How it works:

1. Run a contact or support survey with resubmissions enabled so customers can submit requests continuously

2. On a schedule, call analyze_response_trends + get_insights on that survey

3. Cluster the recurring themes and questions (e.g., "How do I change my subscription?", "Where's my tracking number?")

4. For each high-frequency theme, generate a help doc draft using the customers' own language

Going deeper: Cross-reference with analyze_response_metadata to see which pages customers visited before submitting a support request. This identifies gaps in your existing documentation — the topics where people looked for answers, didn't find them, and submitted a request instead.

Help docs are usually written by the team that built the product. This inverts it — docs are generated from what customers actually need help with, prioritized by frequency.

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Use Case: Dynamic Newsletter Segments From Survey Data

Use survey responses to build precise audience segments, then generate segment-specific newsletter content.

How it works:

1. Call analyze_participants + correlate_slides to identify customer cohorts based on survey answers

2. Call analyze_order_values or analyze_shopify_line_items to attach revenue and product data to each segment

3. Export segments to Klaviyo, Mailchimp, or Omnisend via existing integrations

4. Use the segment profile to generate tailored newsletter content for each cohort

Example: Your attribution survey reveals 38% of customers are gift buyers. The MCP shows their top products are candles and gift sets, their average order value is $65, and they mostly come from Instagram. You generate a gift-buyer newsletter featuring those products with social proof pulled from their survey responses.

Most newsletter segmentation relies on purchase behavior alone. Adding zero-party survey data creates segments based on intent, motivation, and preference — see Klaviyo Segmentation for how to set up the integration.

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Use Case: Personalized Winback and Retention Workflows

Combine survey signals with customer lifecycle data to trigger winback campaigns informed by what the customer actually told you — not generic "we miss you" emails.

How it works:

1. On a schedule, call list_participants to find lapsed customers who previously completed surveys

2. Call correlate_slides to pull their specific survey responses — what they liked, what frustrated them, what they were shopping for

3. Call analyze_order_values to see their purchase history

4. Generate a personalized winback message and route to Klaviyo or your email platform

Variations:

• Post-purchase survey said "gift" → winback around the next gifting occasion (holidays, birthdays). See Gift Tracking and Segmentation for survey setup.

• Exit-intent survey said "price" → winback with a discount when the item goes on sale

• NPS detractor → winback with a specific resolution to their complaint plus an incentive

• Downgrade survey → re-engagement based on their stated reason for leaving

Generic winback campaigns convert at low single-digit rates. Personalized campaigns using zero-party data convert significantly higher because they address the customer's stated needs rather than inferred behavior.

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Use Case: Competitive and Market Intelligence Monitoring

Deploy surveys on pricing pages, comparison pages, or during onboarding that ask "What alternatives did you consider?" Then use the MCP to track how competitor mentions trend over time.

How it works:

1. Call analyze_response_trends on a schedule to watch competitor mention volume week over week

2. Call correlate_metadata_with_answers to see which landing pages and UTM sources produce the most competitor-aware buyers

3. Set up alerts when a new competitor name starts appearing in open-ended responses

Open-ended analysis can catch competitors you didn't know about — emerging players your team hasn't flagged. This is continuous, quantified competitive intelligence that updates itself.

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Use Case: Product Feedback to Roadmap Prioritization

Turn in-app feedback and feature request survey responses into an auto-updating, revenue-weighted feature request leaderboard.

How it works:

1. Run a feedback survey with resubmissions enabled for ongoing collection

2. Call get_insights + analyze_response_trends to auto-categorize feature requests and rank by frequency

3. Layer in analyze_order_values to weight requests by customer segment value — high-spending customers' requests get priority

4. Cross-reference with analyze_participant_metadata to segment by plan tier, company size, or other properties

5. Push the ranked output to Jira or ClickUp

Every SaaS team does roadmap prioritization manually — reading through feedback and tallying themes. This automates the tally and adds revenue weighting so you're building for customers who drive the most value.

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Use Case: Enriching CRM Records With Multi-Survey Intelligence

Zigpoll's HubSpot and Zendesk integrations already create deals and tickets from individual responses. The MCP adds cross-survey correlation — combining responses from multiple surveys into a unified customer profile.

How it works:

1. On a schedule, call correlate_slides to build customer profiles from responses across multiple surveys

2. Push enriched profile data to HubSpot or your CRM

Example: A customer completed your NPS survey (promoter, score 9), your attribution survey (came from a podcast), and your post-purchase survey (buying as a gift). The MCP correlates all three: "high-value gift buyer, podcast-sourced promoter." That profile updates the HubSpot contact properties automatically.

CRM data is usually behavioral — pages visited, emails opened. Survey data adds stated intent and preference, which is a fundamentally different signal for sales and support teams.

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Use Case: A/B Testing Survey Design

Use the survey management tools to programmatically create variants, deploy them, and measure which performs better.

How it works:

1. Use create_poll + create_slide to generate variant A and variant B

2. publish_poll both with different targeting rules — similar to the Layered Post Purchase Surveys pattern

3. On a schedule, call compare_poll_performance to track completion rates, drop-off, and response quality

4. When one variant wins, use update_poll to consolidate

You can test question wording, question order, number of slides, open-ended vs. multiple choice, and whether a reward slide improves completion rates. This turns survey optimization into a continuous improvement loop.

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Use Case: Churn Prediction From Survey Signals

Use survey response trajectories to identify at-risk customers before they leave — not after.

How it works:

1. Call analyze_response_trends on your NPS or satisfaction survey to track score trajectories per cohort over time

2. Call correlate_metadata_with_answers to identify which behaviors or metadata patterns correlate with declining scores

3. When a low NPS response comes in via webhook, use the MCP to analyze the customer's full survey history and generate a churn risk profile

4. Trigger a retention workflow in Klaviyo or your CRM

This is distinct from the winback use case above — it's predictive (catch them before they leave) rather than reactive (win them back after). The signal is score trajectory plus metadata correlation, not a single bad response.

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Use Case: Multi-Market and Localization Intelligence

For companies with international users — detect when specific markets have different feedback patterns than the global average.

How it works:

1. Zigpoll auto-translates surveys so you can collect responses across markets

2. On a schedule, call analyze_location_metadata to break down response patterns by country and region

3. Cross-reference with analyze_device_metadata for device and platform differences by market

4. Auto-detect when a market diverges from the global average — for example, "EU customers mention GDPR compliance 3x more than US customers in feedback surveys"

This puts lightweight cross-market consumer research within reach as a scheduled job.

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Use Case: Building Customer Personas and Voice-of-Customer Docs

Programmatically generate and maintain living customer persona documents and voice-of-customer libraries from actual survey data — not assumptions.

Persona generation:

1. Call analyze_participants + correlate_slides across multiple surveys to cluster customers by response patterns — motivations, pain points, purchase context

2. Call analyze_order_values + analyze_shopify_line_items to attach spending behavior to each cluster

3. Call analyze_location_metadata + analyze_device_metadata for demographic and channel profiles

4. Generate a structured persona doc per cluster

Voice-of-customer library:

1. Call list_responses to pull open-ended answers

2. Call get_insights to extract recurring themes and language patterns

3. Organize by persona, topic, or customer journey stage

4. Output a searchable VoC doc written in customers' actual words

Keeping it alive: Re-run monthly and diff against previous persona docs. Flag when a persona is shifting — for example, "gift buyer segment is now 45% of responses, up from 30%, and their top channel shifted from Instagram to TikTok."

Cross-survey depth: Correlate responses from your attribution survey ("how did you find us"), post-purchase survey ("why did you buy"), exit-intent survey ("why are you leaving"), and NPS survey into a single persona profile. Each survey adds a different dimension.

The output feeds directly into marketing briefs, ad copy, onboarding flows, product specs, and sales decks. Most persona docs are written once and gather dust — this builds them from real data and updates automatically.

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Use Case: Dynamic Survey Routing

Use MCP analytics to automatically decide which survey to show next — making your survey program adaptive instead of static.

How it works:

1. On a schedule, call analyze_participants to check response volumes and saturation

2. If a survey has collected enough responses for statistical significance, use update_poll to swap in the next survey

3. Or: identify which customer segments are under-represented and adjust targeting to collect from gaps

4. Or: analyze_response_trends shows a new theme emerging — auto-deploy a focused follow-up survey using create_poll

This works alongside Zigpoll's existing layered surveys, custom triggers, and display rules. The MCP adds the intelligence layer that decides when and what to change.

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How It All Fits Together

Every use case above follows the same pattern:

1. Trigger — a cron schedule, a webhook, or a custom event

2. Intelligence — Zigpoll MCP analytics tools compute the insight

3. Action — create a survey, push to CRM, send an alert, update a segment, or generate content

The MCP is the intelligence layer between your triggers and your actions. The 14 analytics tools handle the computation. The 5 survey management tools close the loop. The 10 data access tools provide the raw material.

What you build is the orchestration — the scheduling, routing, and formatting. The thin glue code. What Zigpoll provides through the MCP is the analytics engine, the survey builder, and the data pipeline.

Integrations That Compose With MCP

The MCP works alongside Zigpoll's existing integration ecosystem. Use these to route MCP outputs to the right destination:

Integration	Role
Webhooks	Event triggers — new response fires your workflow
n8n / Make / Zapier	Workflow orchestration and multi-step automation
Klaviyo / Mailchimp / Omnisend	Email and SMS — segments, winback, newsletters
HubSpot	CRM enrichment, deal creation, contact association
Zendesk	Support tickets and customer routing
Jira / ClickUp	Feature tracking and roadmap prioritization
Slack / Google Sheets	Alerts, reporting, and dashboards
Segment / Amplitude / Mixpanel	Product analytics and event tracking
Shopify Flow	Ecommerce-specific automation triggers

Available MCP Tools

For the full list of 29 tools with descriptions, see the MCP integration guide.

Category	What it does
Data Access (10 tools)	Read your survey structure, responses, participants, and email data
Analytics (14 tools)	Server-side computation — trend analysis, cross-question correlation, geographic and device breakdowns, Shopify order intelligence, and AI-generated insights
Survey Management (5 tools)	Programmatic survey creation, modification, and publishing

The analytics tools don't just fetch raw data — they compute trend breakdowns, answer distributions, cross-correlations, and revenue analysis server-side. You get structured insights back, not rows to process.