Buzzabout Platform Help
Helps the user with Buzzabout platform questions — from research setup and credit optimization through AI analysis, narrative tracking, audience segmentation, and competitive intelligence.
Step 1 — Gather context
If references/learnings.md exists, read it first for accumulated platform knowledge.
Ask the user:
-
What area of Buzzabout do you need help with?
- A) Research setup — topics, keywords, language filters
- B) Credit management — optimizing credit usage, understanding per-credit overage costs
- C) AI analysis — interpreting sentiment, trends, pain points
- D) Narrative tracking — setting up Slack alerts for topic shifts
- E) Audience segmentation — creating and refining synthetic audiences
- F) Competitive research — reverse-engineering competitor strategies
- G) Integrations — webhook/Slack/email alerts, MCP server (Pro+), REST API (Business+)
- H) Billing — Starter vs Pro vs Business vs Enterprise plan differences
- I) Something else — describe it -
What's your goal? (describe your specific question or problem)
If the user's request already provides most of this context, skip directly to Step 2. Lead with your best-effort answer using reasonable assumptions (stated explicitly), then ask only the most critical 1-2 clarifying questions at the end.
Step 2 — Route or answer directly
If the request maps to a specialized skill, route:
- Social listening strategy or tool comparison → /sales-social-listening [question]
- Audience intelligence (where audiences spend attention) → /sales-sparktoro [question]
- Content intelligence and trending content → /sales-buzzsumo [question]
- Reddit-specific monitoring or lead gen → /sales-social-listening [question]
- Buyer intent signals and prioritization → /sales-intent [question]
Otherwise, answer directly from the platform reference below.
Step 3 — Buzzabout platform reference
Read references/platform-guide.md for the full platform reference — capabilities, pricing, credit system, integration recipes, and comparison with alternatives.
Answer the user's question using only the relevant section. Don't dump the full reference.
Step 4 — Actionable guidance
You no longer need the platform guide — focus on the user's specific situation.
- Research setup — start with narrow topics, expand after reviewing initial results; use language filters to reduce noise
- Credit optimization — batch related queries; use custom date ranges to avoid scanning irrelevant timeframes; credit rollovers help if usage is uneven month-to-month
- Narrative tracking — set up Slack alerts for emerging narratives so you catch trend shifts early
- Audience segmentation — refine synthetic audiences by cross-referencing with actual customer profiles; segments are starting points, not final targeting
- Competitive research — analyze competitor brand mentions alongside your own; focus on sentiment differences and unmet needs
If you discover a gotcha, workaround, or tip not covered in references/learnings.md, append it there.
Gotchas
Best-effort from research — review these, especially items about plan-gated features and integration gotchas that may be outdated.
- Credit-based pricing is unpredictable. Plans bundle monthly credits (2,000 Starter / 10,000 Pro / 50,000 Business) and cap mentions per research (300 / 1,000 / 1,000). Consumption varies by query complexity and data volume; a broad scan burns more than a narrow one. Overage is tiered per credit ($0.025 / $0.02 / $0.015) and the API returns
402 insufficient_creditswhen you run dry. - REST API starts on Business; MCP on Pro. Starter has neither — only webhook/Slack/email alerts. The MCP server (
https://mcp.buzzabout.ai/mcp/) unlocks at Pro for read-only assistant access. The REST API (https://api.buzzabout.ai/v1,x-api-key: bz_live_...) unlocks at Business. Custom data sources remain Enterprise-only. - Webhooks/Slack/email alerts are the only automation on Starter. Tracking agents push daily/weekly alerts to Slack, email, or a webhook URL (e.g. a Zapier Catch Hook). Webhook payload schema and signature verification are not publicly documented — inspect a test payload.
- Synthetic audiences are AI-generated, not tracked cohorts. They're derived from conversation patterns, not actual user profiles. Useful for messaging direction, not for targeting.
- Data sources are broad but depth varies. Reddit and TikTok analysis is strongest. Instagram, LinkedIn, and X coverage depends on public data access, which platforms restrict periodically.
- Free trial is limited. Evaluate whether the credit allocation covers your research scope before committing to a paid plan.
Related skills
/sales-social-listening— Social listening strategy — monitoring setup, tool comparison, sentiment analysis, competitive intelligence, crisis detection/sales-sparktoro— SparkToro — audience intelligence revealing where audiences spend attention (websites, podcasts, YouTube, subreddits)/sales-buzzsumo— BuzzSumo — content intelligence, trending topics, journalist database, backlinks API/sales-reddinbox— Reddinbox — AI audience intelligence across Reddit, X, HN with intent scoring and market briefs/sales-do— Not sure which skill to use? The router matches any sales objective to the right skill. Install:npx skills add sales-skills/sales --skill sales-do
Examples
Example 1: Optimize credit usage for market research
User says: "I'm burning through my monthly credits in the first week doing competitor research — how do I make credits last?"
Skill does:
1. Reviews how credit consumption works — broader queries and longer date ranges consume more
2. Recommends narrowing date ranges to last 30 days instead of all-time
3. Suggests batching related competitor queries into single research sessions
4. Notes credit rollovers mean unused credits carry over — consider spreading research across weeks
Result: Practical credit optimization strategy to stretch the monthly credit allowance across a full month
Example 2: Set up automated content ideas via Zapier
User says: "How do I get Buzzabout to send me trending topic ideas in Slack every morning?"
Skill does:
1. Notes the simplest path is a native Slack alert from a tracking agent (no Zapier needed); for AI-generated ideas, route a tracking-agent alert to a webhook URL (e.g. a Zapier Catch Hook) instead
2. Walks through the Zapier workflow: Buzzabout alert → Zapier webhook trigger → OpenAI action (generate content ideas) → Slack message
3. Notes Starter has only webhook/Slack/email alerts; Pro adds the MCP server and Business adds the REST API (api.buzzabout.ai/v1) if a programmatic pull is needed instead
4. Suggests daily alert frequency to avoid credit overconsumption
Result: Working Zapier automation pipeline from Buzzabout to Slack with AI content generation
Example 3: Compare Buzzabout vs SparkToro for audience research
User says: "Should I use Buzzabout or SparkToro to understand my target audience?"
Skill does:
1. Explains the fundamental difference: Buzzabout analyzes what audiences say (conversations, sentiment, pain points); SparkToro reveals where audiences spend attention (websites, podcasts, channels)
2. Notes Buzzabout is better for understanding motivations and trending narratives; SparkToro is better for channel discovery and ad placement
3. Suggests using both if budget allows — SparkToro for finding channels, Buzzabout for understanding what resonates
4. Compares pricing: Buzzabout Starter $50/mo ($40/mo billed yearly) vs SparkToro Personal $50/mo — similar entry price
Result: Clear comparison based on user's specific research goals
Troubleshooting
AI analysis results feel generic or shallow
Symptom: Buzzabout returns insights that seem obvious or don't match what you see in actual conversations
Cause: Topic query is too broad, covering millions of conversations without specificity
Solution: Narrow the research topic with specific keywords, competitor names, or product categories. Use language filters if your market is non-English. Try the AI Chat interface to ask follow-up questions that drill into specific themes. Reduce the date range to focus on recent conversations where trends are clearer.
Research credits depleting too fast
Symptom: Running out of monthly credits (2,000 Starter / 10,000 Pro / 50,000 Business) well before month end
Cause: Running broad queries, long date ranges, or too many parallel research sessions
Solution: Check credit usage in the dashboard. Batch related queries into fewer sessions. Use custom date ranges (last 30 days) instead of all-time scans. Lower-tier plans also cap mentions-per-research (300 on Starter), so a narrower scope is cheaper. Watch for 402 insufficient_credits on API calls. Overage is billed per credit ($0.025 / $0.02 / $0.015 by tier), so for sustained heavy use the next tier up is usually cheaper than overage.
Slack alerts aren't arriving for narrative tracking
Symptom: You set up narrative tracking but Slack notifications aren't coming through
Cause: Slack integration may not be properly connected, or alert thresholds are set too high
Solution: Verify the Slack connection in Buzzabout settings. Check that narrative tracking topics have alert thresholds that match realistic conversation volumes. Test with a known trending topic first to confirm the pipeline works. If using Zapier webhook instead of native Slack, verify the webhook URL is correctly pasted as the digest destination.
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