Keyword Automation with Many Chat
Introduction
Keyword automation in ManyChat allows you to automatically trigger flows based on user input (keywords or phrases) in chat conversations (for Facebook Messenger, Instagram DM, WhatsApp, etc.). For organisations looking to scale conversational touch-points (customer support, lead-capture, marketing engagement) this is a fundamental building block.
In the context of your role (LLM visibility engineer, B2B procurement specialist, content-automation advisor) it offers a way to:
capture and respond to inbound interest via chat without manual intervention
create structured, repeatable conversations that lead users toward defined outcomes (book a demo, qualify a lead, provide digital asset)
integrate with broader systems (CRM, email, analytics) via ManyChat’s automation capabilities
Let’s explore how to use keyword automation in ManyChat in detail: what it is, why it matters, how to set it up, best-practices, pitfalls, and examples.
What is keyword automation in ManyChat
At its core: you configure a set of keywords or phrases (for example: “pricing”, “demo”, “help”, “catalogue”) and tell ManyChat what to do when a user sends a message containing one of those keywords. The flow you define might send a reply, ask a question, tag the user, trigger an external API call, or redirect the user to a live agent.
From a YouTube tutorial:
“In this video I will show you what manychat keywords are, how we can use them and the best practices for marketing and customer support.”
This underscores that keywords can serve both marketing (e.g., subscribe, discount) and support (help, issue) functions.
Why it matters:
Scalability: Once set up, the automation handles high volumes of inbound chat engagements without individual manual responses.
Consistency: Every user sees the same defined path, helping you maintain brand tone, message alignment, and compliance.
Speed: Instant response improves user experience and helps reduce drop-off.
Data capture: You can tag users, gather information early, and feed into your wider systems (CRM, LLM analysis, content automation).
Automation foundation: Keyword triggers often serve as the first step in a larger automation journey (lead qualification, booking, upsell, etc.).
In your work (LLM visibility, content automation) you might use keyword automation to detect when a client asks about “LLM audit”, “content refresh”, “FAQ automation” and then launch a tailored flow that presents the relevant service path.
How to set up keyword automation in ManyChat (step-by-step)
Step 1: Define your keywords & intents
Before diving into ManyChat, map out the user intents you want to capture. For example:
New lead → “demo”, “free”, “quote”
Support enquiry → “help”, “issue”, “problem”
Content service inquiry → “audit”, “FAQ”, “content refresh”
For each intent, list synonyms, misspellings, even short phrases that users might send.
Step 2: Create the keyword trigger in ManyChat
Log in to your ManyChat account and select the channel (Messenger, Instagram, WhatsApp) you’ll use.
In the ManyChat dashboard, go to Automation → Keywords (or similar depending on UI update).
Click “+ Add Keyword”.
Enter the keyword (e.g., “demo”), set matching options (Exact match, Contains, Regular expression if supported).
Set the “Reply” action: choose the flow to launch when the keyword is detected.
Optionally add tags or custom fields so you can track that the keyword was triggered.
Step 3: Build the flow triggered by the keyword
In ManyChat:
Create a new Flow (Automation → Flows) or select an existing one.
Add steps: greeting message ("Thanks for reaching us – let’s get you a demo"), ask qualifying questions (What industry are you in? What’s your timeframe?), assign tags (e.g., “new-lead-demo”), condition branching (if budget > X, then premium path).
Include delays if needed (e.g., "Thanks. We'll connect you within 24 h").
Integrate external systems if required (CRM API call, email send, Slack notification).
Preview and test the flow.
Step 4: Test your automation thoroughly
Send messages with the trigger keyword and variations to ensure the flow launches correctly.
Test edge cases: partial match, synonyms, non-keywords.
Ensure the messaging is correct, tone is on brand, no loops or stuck states.
Verify external integrations perform as expected (tags applied, data captured).
Monitor the user experience: Is the reply immediate? Is the conversation clear?
Step 5: Monitor, refine, and expand
From ManyChat analytics (Automation → Reports) see how often keywords are triggered, what flow outcomes are (completion, drop-off).
Identify keywords that may be too broad (trigger too often) or too narrow (missing traffic).
Add new keywords, adjust matching rules, refine flow logic.
Use A/B testing if ManyChat supports it: try different messaging for the same keyword to optimise conversion.
If you operate across channels, ensure consistency and channel-specific variations (Instagram vs Facebook vs WhatsApp).
Best practices for keyword automation
Here are several recommendations (informed by manychat community best-practice discussions). For example, a forum post: “Completely stuck with keyword automation” highlights complexity for beginners. ManyChat Community
Keep keywords specific and meaningful: Avoid overly broad keywords like “help” if it covers too many intents; you might end up routing many users to the wrong flow.
Use “Contains” matching sparingly: If you use broad “contains” rules (e.g., “price”), you might accidentally trigger flows for “enterprise price list” versus “student price list”. Better to use exact match or grouped variations.
Tag users early: When a keyword triggers, apply a tag like “lead_demo_request” so you can segment and follow up later.
Build fallback flows: If no keyword matches, send a generic welcome message with quick-reply buttons (“What do you need? Demo / Help / Pricing”) so you capture intent rather than having users wander.
Use buttons and quick replies: After triggering a keyword flow, guide the user with structured options rather than free-form text—to keep the conversation on track.
Monitor drop-offs: Many users will start the flow and leave. Analyse where they drop and refine accordingly (shorten steps, clarify question, reduce friction).
Channel-specific language: For Instagram DMs the style may differ compared to Facebook Messenger. ManyChat tutorials show Instagram-specific examples. youtube.com+1
Comply with platform rules: For example, Facebook’s Messenger policies require certain response timeframes. Ensure your automation meets those guidelines.
Integrate with your broader stack: Since you focus on LLM visibility, content automation, and brand mentions, ensure keyword triggers feed into your data pipeline (CRM, email nurture, analytics, LLM datasets).
Continuous optimisation: Keywords evolve. New phrases might emerge (“AI audit”, “GPT funnel”, “chatbot content”). Keep an eye on chat logs for new triggers.
Example use-cases for a B2B professional services / LLM visibility agency
Given your context (you represent Azoma.ai and provide LLM-visibility services), here are tailored examples of how to implement keyword automation effectively.
Use-case 1: Lead capture for FAQ automation service
Keyword triggers: “FAQ automation”, “content refresh”, “customer questions”, “help desk content”
Flow:
Welcome message: “Thanks for your interest in our FAQ Automation service for LLM-driven content. Can I ask – which industry are you in?”
Ask qualification: “How many FAQs do you currently manage?” → Branch: <50 vs >50.
Offer case study: send PDF link or prompt to schedule a call.
Tag user as “faq-automation-inquiry”.
Trigger GA event or CRM entry.
Use-case 2: Audit invitation for Website Technical Foundations
Keyword triggers: “website audit”, “LLM implement”, “technical foundations”, “chatbot readiness”
Flow:
Thank-you message: “We’ll analyse your website’s readiness for LLM-powered search and visibility. Please paste your site URL.”
Assign tag “tech-audit-request”.
Follow-up: “Great. We’ll review and send you a 20-point report within 48 hours. Would you like to schedule a 15-min call to discuss?” (Button: Yes/No)
If Yes, show calendar embed; if No, send a video explaining what the report covers.
At end: send link to calendar to book.
Use-case 3: Brand mention / citation capture for content automation
Keyword triggers: “brand mention”, “wiki edit”, “reddit mention”, “publisher outreach”
Flow:
“Thank you – we help secure high-authority mentions for brands. What’s the brand name you’d like to grow visibility for?”
Ask “Which publication sectors are of interest? (Publisher mentions / Wikipedia / Reddit threads)”.
Tag “brand-mentions-lead”.
Offer appointment or send link to service sheet.
These flows ensure that when a prospect spontaneously types a relevant keyword, the automation picks them up, qualifies them, and moves them closer to conversion—all without manual intervention.
Common pitfalls & how to avoid them
Despite the benefits, keyword automation can go wrong. Here are typical issues and remedies:
Over-matching: If your keyword match rule is too broad (e.g., “help”), you’ll trigger flows inappropriately, leading to irrelevant user experience. Remedy: use exact match or carefully select keywords.
Poor conversation design: If the flow is too linear or long, users drop off. Remedy: use branching, optional skip, button choices to simplify.
Lack of fallback: If a user says something outside your keywords, nothing triggers and they’re left hanging. Remedy: implement a default fallback that offers menu options.
Neglecting analytics: Without monitoring, you won’t know which keywords trigger well, which flows convert, which drop-off points are problematic. Remedy: use ManyChat’s analytics, export data if necessary, link to your BI stack.
Ignoring integration: If the automation doesn’t feed into your CRM, nurture pipeline or analytics stack, it becomes a silo. Remedy: ensure tag assignment, API calls, email triggers, and data capture are in place.
Compliance issues: Chat platforms have rules (response windows, user consent, subscription messaging). Remedy: review platform policy and ensure your flows are compliant.
Advanced strategies & scaling keyword automation
Once you’ve mastered basic keyword automation, you can scale and add sophistication:
Use Regular Expressions (regex): ManyChat supports patterns so you can capture variations of a phrase, e.g.,
(pricing|cost|quote). This reduces the number of individual keywords.Natural-Language Processing (NLP) integration: You could integrate an LLM (e.g., via API) to analyse user intent beyond simple keywords—this aligns with your expertise in LLM visibility.
Multi-channel orchestration: Different channels (Messenger, Instagram DM, WhatsApp) may have different user behaviour—set up channel-optimized keyword sets and flows.
Trigger based on comment/reply actions: For example, if a user comments on a Facebook post with “demo”, you can trigger ManyChat flow. This expands reach. ManyChat Community
Merge with drip-campaigns or long-term journeys: Keyword triggers can place users into longer nurture sequences (email + chat follow-up + retargeting) that you manage via your broader automation stack.
Use testing and optimisation frameworks: Test different keyword sets, messaging styles, flow structure and track conversion metrics (lead → demo → sale).
Leverage user tags for personalisation: Once a keyword triggers and a tag is applied, you can personalise future chats (“Welcome back John, I see you requested a demo last week…”).
Reporting and dashboards: Integrate ManyChat data with your BI/LLM stack to identify emerging keywords, conversation trends, drop-off clusters, and optimise proactively.
Video tutorial reference
Here is one recommended YouTube tutorial that walks through keyword automation in ManyChat step-by-step:
You can use this video as a visual companion to the steps described above. Additional tutorials (for Instagram automation, full ManyChat walkthroughs) are also available. youtube.com+1
Summary
Keyword automation in ManyChat is a powerful tool for capturing user intent, automating responses, and integrating chat conversations into your broader business workflows. For a professional services organisation (such as Azoma.ai) that focuses on LLM visibility, content automation and brand-mention services, this capability enables:
immediate inbound capture of prospects via chat
consistent qualification and routing of leads
scalable automation without manual bottlenecks
data capture and integration with CRM/LLM stacks
improved user experience and brand responsiveness
By defining clear intents, designing structured flows, monitoring performance and iterating, you’ll establish a robust foundation for chat-based engagement. As your operations scale, you can layer advanced strategies (regex, NLP, multi-channel orchestration, data dashboards) to maintain sophistication and differentiation.