AI chatbot for Burst Statistics: ask your privacy analytics
SleekAI's chatbot reads the same burst_statistics, burst_sessions, and burst_goals tables that Burst writes to, so an editor can ask plain English questions about top posts, referrers, and conversion goals using their own OpenAI, Anthropic, Google, or OpenRouter key.
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Your privacy analytics, now askable in plain English
Burst Statistics keeps every pageview, session, and goal hit in your own WordPress database. The plugin writes to wp_burst_statistics, wp_burst_sessions, and wp_burst_goals, with referrer, device, browser, and country fields normalized into wp_burst_lookup_tables. Nothing leaves your server, no cookies are set by default, and there is no third party syncing analytics events to a hosted backend.
That privacy-first posture is also what makes Burst a great fit for an AI chat layer. SleekAI maps the same tables Burst queries for its dashboard into named variables in the system message, so the chatbot can answer questions like What were the top five posts last week and where did the traffic come from against live rows, not a vendor API. The data source wizard handles the join between sessions and goals, and display conditions lock the chatbot to administrator and editor roles so logged-out visitors never see it.
Generic AI assistants either need a separate Google Analytics scrape or refuse to answer at all without an integration. With Burst, the data already lives in WordPress. SleekAI just reads it. The bot can call out a referrer spike, point at the post earning the spike, and suggest a follow-up post idea based on the URL slug, without sending any visitor data to a third party.
Workflow
How SleekAI plugs into your Burst-tracked site
Map Burst tables
burst_statistics, burst_sessions, burst_goals, and the lookup tables for referrer and device. Pick only the columns you actually ask about.
Scope to editors
/wp-admin. Public visitors and subscribers never see the chat trigger or the widget script.
Bring your own key
Tune presets
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A typical Burst Statistics editor conversation
Comparison
Generic chatbot vs SleekAI for Burst Statistics
Generic chatbot
- Cannot read self-hosted analytics tables inside WordPress
- Has no idea which posts, referrers, or goals are tracked
- Often requires sending site data to a third-party analytics API
- Will not respect editor or admin role scoping out of the box
- Cannot answer date-range comparisons without an external dashboard
SleekAI chatbot
-
Reads the same
burst_statisticsandburst_sessionstables Burst uses -
Joins sessions to
burst_goalsfor conversion-aware answers -
Display conditions limit access to
administratorandeditor - Multibot keeps the analytics bot separate from any visitor-facing bot
- Keeps data in WordPress, no third-party analytics API in the loop
Features
What SleekAI gives you for Burst Statistics
Privacy preserved
Burst keeps traffic data inside your database, and SleekAI reads it from there. No analytics events get shipped to a third-party API to power the chatbot, so the privacy posture you chose Burst for survives.
Plain English traffic queries
Top posts, top referrers, goal conversion rate, device breakdown, country mix. Map the columns you care about into variables once, then ask the bot questions instead of clicking into the dashboard.
Editor and admin only
Display conditions on role and logged-in state lock the chatbot to your editorial team. Visitors and subscribers never see it, and the trigger does not load on the public site.
Use cases
Where editors use SleekAI for Burst Statistics
Weekly content review
Ask for the top five posts of the week and the bottom five by engagement. The bot pulls from burst_statistics and surfaces patterns the editor can act on.
Referrer spike alerts
Catch sudden traffic from Hacker News, Reddit, or a newsletter mention before it falls out of the trailing 24 hours. The bot points at the post and the referrer in one answer.
Goal performance
Track newsletter signups, ebook downloads, or product CTA clicks. The bot reads burst_goals and answers whether the goal beat last week.
The bigger picture
Why a chat layer over Burst matters
Most editorial teams picked Burst Statistics for a reason. They wanted to know how their content was performing without shipping every visitor event to a third-party analytics vendor. That privacy posture is good, but it also means the analytics live somewhere only the people who already log in to WP Admin look at them.
Editors do not log in. Writers do not log in. The owner logs in twice a week.
A chatbot that reads the same Burst tables changes the surface from a dashboard into a question. The editor in chief can ask in the morning standup what last week's top three posts were and what the standout referrer was. The freelance writer can check whether the piece they shipped on Tuesday is pulling in newsletter signups.
The owner can ask whether anything is on fire. SleekAI maps the relevant Burst columns into the system message, scopes the chatbot to admin and editor roles, and runs on the same key the site already uses for any other AI work. Burst keeps the data in WordPress.
SleekAI keeps the question loop in WordPress too.
Questions
Common questions about SleekAI for Burst Statistics
No. Burst still owns the charts, real-time view, and per-post breakdown. SleekAI sits next to it as the fast-answer layer. Use the dashboard when you want to explore, and the bot when you already know the question you want to ask, like top posts last week or goal conversion rate by referrer.
The data source wizard exposes burst_statistics, burst_sessions, burst_goals, and the lookup tables for referrer, browser, and device. You decide which columns to surface in the prompt, so token usage stays in control even on high-traffic sites.
Burst already stores everything locally. SleekAI reads from those tables, then sends only the resolved variable values to your chosen AI provider. Excluding any personally identifying fields keeps the chat exchanges as anonymous as the underlying analytics, which is usually the whole reason a site picked Burst.
Yes. Map week-over-week, month-over-month, or rolling 30 days into named variables and the bot can phrase the comparison naturally. Common patterns include this week versus last week and this month versus the prior month, with goal counts pulled in alongside.
Yes. Burst stores goals in burst_goals with status, target URL, and fire count. SleekAI maps those into the prompt so the bot can answer how a specific goal performed in a date range, or whether a recent post is converting better than the site average.
Yes. Display conditions on user role, logged-in state, post type, and URL path are part of every chatbot. Most editorial teams set the analytics bot to administrator and editor only, with a URL rule scoping it to /wp-admin and the front-end widget disabled.
A mid-tier model is plenty. GPT-4o mini, Claude Haiku, or Gemini Flash all handle date-range arithmetic and basic ranking comfortably. Reserve a stronger model only if you ask the bot to suggest editorial follow-ups based on the traffic pattern.
No. The widget loads only when a logged-in editor opens it, and the data source queries hit the same tables Burst already indexes. Display conditions keep the script off the public site entirely, so anonymous visitors see no extra requests on any page.
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