Make.com and Zapier Twitter Monitoring Automation: The Complete Webhook Guide

> Note: This post focuses on Make.com (formerly Integromat) automation scenarios and compares Make with Zapier. For a step-by-step Zapier-specific walkthrough, see our dedicated guide: How to Set Up Zapier Automation with Twitter Monitoring.

Why Automate Twitter Monitoring?

You've set up keyword tracking and AI digests. Great. But what happens next? Most teams manually check dashboards, copy-paste summaries into Slack, or forward email digests to their CRM.

That's wasted time. With webhooks, Twigest pushes events to your tools automatically — the moment they happen.

What Are Webhooks?

A webhook is a real-time notification. Instead of polling an API every few minutes ("anything new?"), the server pushes data to your URL the instant something happens.

Twigest supports three webhook event types:

• digest.created — Fired when a new AI digest is generated. Contains the summary, key themes, sentiment scores, and top tweets.
• spike.detected — Fired when unusual volume is detected on a tracked keyword. Contains the keyword, current volume, baseline, and spike percentage.
• lead.found — Fired when a tweet with purchase intent is detected (buying signals, competitor complaints, recommendation requests).

Setting Up Webhooks in Twigest

1. Go to Settings → Webhooks in your Twigest dashboard
2. Click Add Webhook Endpoint
3. Paste your webhook URL (from Zapier, Make, or your own server)
4. Select which events you want to receive
5. Save — Twigest will auto-generate an HMAC signing secret for security

That's it. Every time a selected event fires, Twigest sends a POST request to your URL with a JSON payload.

Make.com: The More Powerful Choice for Complex Workflows

Make.com (formerly Integromat) is the tool of choice when your automation needs more than a simple two-step trigger-action. Unlike Zapier, which excels at quick point-to-point connections, Make's visual scenario builder lets you design multi-branch flows with filters, transformers, routers, and iterators — all without writing code.

Setting Up Make.com with Twigest

1. Log in to make.com and click Create a new scenario
2. Add a Webhooks module as the trigger
3. Select Custom webhook and generate a new webhook URL
4. Copy the URL and paste it into Twigest under Settings → Webhooks
5. Select your events (digest.created, spike.detected, or lead.found) and save
6. Click Run once in Make to put the scenario in listening mode
7. Trigger a test payload from Twigest — Make will capture the data structure
8. Build your downstream modules to handle the data

Make's free tier provides 1,000 operations per month — enough for most small teams monitoring a handful of keywords with daily digests.

5 Make.com Use Cases with Detailed Setup

Use Case 1: Auto-Post AI Digest to Slack (with Formatting)

This is the most common automation. Every time Twigest generates a digest, Make formats it and posts to your Slack channel automatically.

Make.com scenario structure:

1. Webhooks → Custom Webhook (trigger on digest.created)
2. Tools → Set Variable (format the digest date)
3. Slack → Create a Message (post to #brand-monitoring)

Slack message template in Make:

```

{{summary}}

```

Make advantage over Zapier here: Make lets you use the formatDate() function inline in the message template, apply text transformers, and conditionally skip the Slack post if the sentiment score is above a threshold — all within the same scenario without adding extra steps.

Use Case 2: Trigger Email Alert When Sentiment Drops Below Threshold

This automation monitors your brand sentiment and sends an immediate email alert when a digest shows negative sentiment breaching a defined threshold. This is an early warning system for brand crises.

Make.com scenario structure:

1. Webhooks → Custom Webhook (trigger on digest.created)
2. Router (branch based on sentiment_score value)

- Branch A: sentiment_score < -0.3 → proceed to email

- Branch B: all other → do nothing (or log to Sheets)

1. Email → Send an Email (alert to brand manager with digest summary)

Why Make is better for this than Zapier: Make's Router module lets you build multi-branch logic without paying for additional Zap steps. A single scenario handles the conditional logic, whereas in Zapier you would need a Filter step plus a separate notification Zap.

Sample email subject line formula:

Sentiment alert: {{keyword}} dropped to {{sentiment_score}} on {{formatDate(created_at; "MMM D")}}

Use Case 3: Log All Mentions and Spikes to Google Sheets

Build a running log of every spike event for later analysis — useful for quarterly reviews, reporting to stakeholders, or identifying patterns in competitor activity.

Make.com scenario structure:

1. Webhooks → Custom Webhook (trigger on spike.detected)
2. Google Sheets → Add a Row (append to your monitoring log)

Google Sheets columns to map:

After 90 days, your Google Sheet becomes a rich competitive intelligence archive — you can pivot by keyword to see which competitors spike most often, which days of the week show the most activity, and how your own brand's spike frequency compares.

Use Case 4: Create a Jira Ticket on Keyword Spike

For teams that manage brand crises through Jira or similar project management tools, this automation creates a ticket the moment a spike is detected — routing it directly to the team member responsible for monitoring.

Make.com scenario structure:

1. Webhooks → Custom Webhook (trigger on spike.detected)
2. Jira Software → Create an Issue

Jira issue configuration in Make:

• Project: Brand Monitoring
• Issue type: Task
• Summary: Spike detected: {{keyword}} ({{spike_percentage}}x baseline) on {{formatDate(detected_at; "MMM D")}}
• Description: Keyword "{{keyword}}" is showing {{current_volume}} mentions/hour against a baseline of {{baseline_volume}}. Review digest and assess response needed.
• Priority: High (if spike_percentage > 5) or Medium (if 3–5x)
• Assignee: Set to your brand monitoring team member

Make advantage: You can use Make's Math functions to dynamically set priority based on spike severity, without needing a second Zap. The condition {{spike_percentage}} > 5 maps to "High priority" in a single branching router.

Use Case 5: Send Weekly PDF Report to Dropbox

This automation collects digest data throughout the week and, on Fridays, compiles a formatted report and saves it to Dropbox — ready for stakeholder distribution or archiving.

Make.com scenario structure:

1. Webhooks → Custom Webhook (trigger on digest.created — collects all week)
2. Google Sheets → Add a Row (accumulate weekly digest data)
3. Make Scheduler (trigger on Friday at 5 PM)
4. Google Sheets → Get Rows (retrieve the week's entries)
5. HTML to PDF or Pdfmonkey → Create a Document (generate formatted report)
6. Dropbox → Upload a File (save to /Reports/Weekly/)
7. Email → Send an Email (notify stakeholders with Dropbox link)

This scenario combines two separate flows: a continuous accumulation flow (step 1–2) and a scheduled delivery flow (step 3–7). Make handles both in a single scenario using its scheduling and data store capabilities. Building the equivalent in Zapier would require multiple Zaps and a third-party data store.

10 More Automation Ideas

1. Digest → Notion Database

Trigger: digest.created

Action: Create a new Notion page with the digest summary, date, and key themes.

Build a searchable archive of all your Twitter intelligence without lifting a finger.

2. Spike → Slack Alert

Trigger: spike.detected

Action: Post a formatted message in your #brand-monitoring Slack channel.

Your team sees anomalies in real time — no one needs to check the dashboard.

3. Lead → HubSpot CRM

Trigger: lead.found

Action: Create a new contact or deal in HubSpot with the tweet URL, intent type, and author info.

Turn social signals into pipeline automatically.

4. Digest → Email Newsletter

Trigger: digest.created

Action: Draft an email in Mailchimp or ConvertKit with curated highlights.

Share weekly Twitter intelligence with your audience without manual curation.

5. Spike → PagerDuty

Trigger: spike.detected (filtered for >200% baseline)

Action: Create a PagerDuty incident for the on-call comms team.

Crisis-level spikes get immediate attention.

6. Lead → Google Sheets

Trigger: lead.found

Action: Append a row with tweet text, author, intent type, and timestamp.

Simple lead tracking without a CRM.

7. Digest → Airtable

Trigger: digest.created

Action: Create a record with structured digest data for trend analysis.

Build your own analytics layer on top of Twigest.

8. Spike → Discord

Trigger: spike.detected

Action: Post in your community's monitoring channel.

Keep your Discord community informed about trending conversations.

9. Lead → Salesforce

Trigger: lead.found

Action: Create a lead record with social context attached.

Enterprise sales teams can act on social buying signals within minutes.

10. Digest → Confluence

Trigger: digest.created

Action: Create a Confluence page under your competitive intelligence space.

Institutional knowledge that's always up to date.

Webhook Security

Every Twigest webhook includes an X-Twigest-Signature header — an HMAC-SHA256 hash of the request body using your secret key. Always verify this signature on your receiving end to ensure requests genuinely come from Twigest.

```

X-Twigest-Event: digest.created

X-Twigest-Signature: sha256=abc123...

Content-Type: application/json

```

In Make.com, you can verify the signature using a Tools → Parse JSON module combined with a Crypto function to hash the incoming body and compare it to the header value. While optional for personal automations, signature verification is strongly recommended for business-critical workflows.

Zapier vs Make: Which One?

Both work great with Twigest webhooks. Use Zapier for simple push-to-Slack automations. Use Make for multi-branch workflows with filters, transformations, and complex routing.

Troubleshooting Make.com Scenarios

Scenario runs but no data appears in Sheets/Notion/Slack

Check that the webhook trigger captured the correct data structure. In Make, click the bubble on the webhook module after a run to inspect the incoming payload fields. If field names look wrong, re-run the test payload from Twigest to refresh the data structure.

Make scenario fires but Twigest test payload is not received

Ensure the scenario is in "Active" status, not just "Run once" mode. Make scenarios in "Run once" mode only accept a single payload for testing — switch to Active for live operation.

Sentiment score field is missing from the payload

The sentiment_score field is present in digest.created events but not in spike.detected events. Make sure you are filtering on the correct event type for sentiment-based routing.

Operations limit reached on Make free tier

Make counts each module execution as one operation. A scenario with 4 modules that runs 250 times per month uses 1,000 operations. If you hit the limit, either upgrade to Make's Core plan ($9/month) or reduce the number of modules per scenario by combining logic steps.

Date formatting issues in reports

Make's formatDate() function requires an ISO date string. The created_at and detected_at fields from Twigest are already in ISO 8601 format, so they work directly. If you are pulling dates from a Google Sheet that stored them as formatted strings, you may need a parseDate() conversion step first.

Getting Started

1. Sign up for Twigest — webhooks are available on Pro and Business plans
2. Create a Make scenario with a Custom Webhook trigger (or a Zap in Zapier)
3. Copy the webhook URL into Twigest settings under Settings → Webhooks
4. Select your events and save
5. Test the connection — Twigest sends a test payload immediately
6. Build your downstream automation modules

Stop copying and pasting. Let your tools talk to each other.

Related reading:

• Step-by-step: how to set up Zapier automation with Twitter monitoring
• Twitter keyword spike alerts: monitor trends in real time
• Best Twitter monitoring tools in 2026