sales-skills/sales-buzzabout

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:

1. 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

2. 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.

1. Research setup — start with narrow topics, expand after reviewing initial results; use language filters to reduce noise
2. Credit optimization — batch related queries; use custom date ranges to avoid scanning irrelevant timeframes; credit rollovers help if usage is uneven month-to-month
3. Narrative tracking — set up Slack alerts for emerging narratives so you catch trend shifts early
4. Audience segmentation — refine synthetic audiences by cross-referencing with actual customer profiles; segments are starting points, not final targeting
5. 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_credits when 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.