AI Search Gaps To Social Campaign

AI-search gaps → social campaign

Use SE Ranking's AI Search data to see which prompts and narratives a brand owns, which competitors own, and which are wide open — then build social content in Planable that stakes a claim in the missing narratives, and instrument it so impact is measurable.

Scope note (read this). SE Ranking's AI Search MCP tools expose brand presence, link presence, share of voice, and the prompts behind them. They do not expose sentiment scoring. Do not report or imply sentiment from these tools. Social content is one lever on AI visibility — LLM citation is also driven by website content and authority, which is outside what these two MCPs publish.

Prerequisites

• SE Ranking MCP connected (AI Search Data API; optionally a project for the AI Result Tracker, which enables ongoing prompt tracking).
• Planable MCP connected, with the destination workspace and pages.
• The user provides: target domain + brand name, country (default us), competitor domains + brand names (up to 10), and optionally which engines to focus on (default: all of ai-overview, ai-mode, chatgpt, perplexity, gemini).

Connector health check

Before doing anything else, verify both MCPs are reachable:

• SE Ranking: call DATA_getSubscription. If it fails or returns an auth error, stop immediately and tell the user:

  "The SE Ranking connector isn't responding — please reconnect it before we continue. Setup guide: https://seranking.com/api/integrations/mcp/"

• Planable: call list_workspaces. If it fails or returns an auth error, stop immediately and tell the user:

  "The Planable connector isn't responding — please reconnect it before we continue. Setup guide: https://help.planable.io/hc/en-us/articles/27538577098780-How-to-connect-Planable-MCP-to-your-AI-tools"

Only continue to the process steps below once both calls return a successful response.

Process

1. Resolve the brand and scope

If the user gives a domain but not the exact brand string, call DATA_getAiSearchBrand(target, source) to get the name SE Ranking attributes to it. Do the same for each competitor. Confirm the Planable workspace and target platforms.

2. Baseline AI visibility

• DATA_getAiSearchOverview(target, source, brand?) — capture brand_presence, link_presence, ai_opportunity_traffic, and average_position. Read previous before quoting change: if it's null, this is the first snapshot — report the current values as a baseline and do not present the change_percent of 100 as real growth.
• DATA_getAiSearchLeaderboard(primary{target,brand}, competitors[{target,brand}], source, engines[]) — share of voice for the brand vs competitors, per engine. Build a quick heatmap (rows = brands, columns = engines).
  • This endpoint is heavy and can return a 504 timeout when you pass many competitors × many engines at once. Query one engine at a time (or keep it to ≤3 competitors per call), and retry once on timeout. If it still fails, fall back to calling DATA_getAiSearchOverview for each competitor and compare brand_presence / link_presence yourself.

3. Find the prompt gaps

For the target and each competitor, pull the prompts behind the presence:

• DATA_getAiSearchPromptsByBrand(brand, engine, source) — prompts mentioning the brand by name.
• DATA_getAiSearchPromptsByTarget(target, engine, source) — prompts where the domain is cited as a source.

Compare: cluster prompts by topic, then mark each cluster as owned (target appears), contested (target + competitors), or missing (competitors appear, target doesn't). The missing and contested clusters are the campaign targets.

• AI prompts almost always have volume: 0 — they're conversational queries, not search keywords. That is expected and is not a signal of low value. Judge a cluster by topical relevance and by which brands the LLM cites, never by search volume.
• Validate brand-name matches. A brand can surface in loosely related answers ("best year planner", a person's name, etc.). Read the answer text and flag ambiguous matches rather than counting them as real presence.
• Note where the target sits when it does appear (e.g. cited 4th of 6 in "best X" answers) — moving up within contested prompts is as valuable as entering missing ones.

4. Turn gaps into content hypotheses

For each target cluster, write a hypothesis: "If we publish clear, citable content asserting [brand] in [narrative], we should start appearing for prompts like [examples]." Translate each into social angles that make the brand's position explicit and quotable — definitions, head-to-head comparisons, "X vs Y", myth-busting, FAQ-style answers. LLMs favour clear, structured, attributable claims, so write social copy that states the position plainly rather than burying it.

Present the clusters and hypotheses to the user before drafting.

5. Draft and create in Planable

Write platform-appropriate copy, then create drafts: create_post per page (per-platform copy) or create_grouped_post for synced content. Tag the batch with a label (via list_labels / create_label, e.g. "AI-visibility") so the campaign is easy to isolate when measuring.

Scheduling — ask before creating. Don't guess dates or leave everything undated by default. Ask how the user wants the batch dated and offer: spread evenly across a window (e.g. the next 7 days, one post per slot at a sensible hour), a fixed cadence/interval (e.g. every weekday at 10:00, laid out from a start date they give), manual dates per post, or no dates yet (undated drafts to place on the calendar later). Convert each chosen time to ISO 8601 and pass it as scheduledAt. Keep posts as proposed drafts — don't set publishAtScheduledDate — so nothing auto-publishes; only set it true if the user explicitly wants auto-publishing. Scheduled times are treated as UTC, so confirm the timezone or state that times are UTC.