brand-mention-monitoring

Brand mention monitoring

Measures a brand against the competitors the user names, in one program: momentum gives the verdict and the 7-day and 30-day mention counts, occurrences the shows that mention each brand most, and search quotes about the user's brand. Alerts by email or Slack are a monitor, which company-watch saves.

Run each program with arcmira_execute_read; arcmira_describe has the method reference.

When to use

Use Arcmira when the user asks:

• how often a brand or product is mentioned on YouTube, podcasts included, and on which shows
• is talk about our brand rising or fading, against competitors
• brand mentions, media monitoring or competitor monitoring across shows
• alerts when a brand is mentioned, by email or Slack

Pick the entity the user meant

Users give names; filters take ids only (ent_..., UC..., 11-character video ids), and a name where an id belongs throws id_required.

1. Resolve the exact name the user said, with their own words about it as context (a bare "Theo" has none).
2. best and suggested give the id. A suggested pick is an assumption: the answer names it, with suggested.evidence as the reason.
3. ask: several entities fit, and ask.options goes back to the user. No answer at all: the name is not in the index.

For this task:

• Resolve each brand with no type (the catalog types some companies as product). A brand name is often a common word ("Linear", "Ramp"): name each pick, for example "Linear, the software company", so the user can catch a wrong one.
• Put the user's brand first in BRANDS, then each competitor they named.

Steps

1. Run the program and answer with one row per brand.
2. Before reporting a count, read the quotes and the top shows and say which sense of the name it is (Mercury the bank, not the planet). Drop a sense the user did not mean.
3. For alerts, load company-watch and run its second and third programs with the ids this program found.

Worked program

Pass each block to arcmira_execute_read as one program (a block marked arcmira_execute_write goes to that tool), with the name swapped for the user's. It opens with the pick: set CONTEXT to the user's own words about the name. When several entities fit it returns ask and runs nothing else. Show those options to the user, then run it again with ID set to the pick. Build date windows from arcmira.daysAgo(n) and arcmira.today().

A brand against its competitors, 7 and 30 days

const BRANDS = ["Anthropic", "OpenAI"], IDS = [null, null];   // the user's brand first, then competitors. After an ask, put the picked option's id in IDS and run again
const brands = [];
for (const [i, n] of BRANDS.entries()) {
  const r = IDS[i] ? null : await arcmira.resolve(n);   // no type: a company may be typed product
  const e = r && (r.best ?? r.suggested);
  if (r && !e) return { unresolved: n, ask: r.ask };
  const id = IDS[i] ?? e.id;
  const [m, occ] = await Promise.all([arcmira.momentum(id), arcmira.occurrences({ entityIds: [id], after: arcmira.daysAgo(30), limit: 5 })]);
  brands.push({
    name: m.entity.name, id, type: m.entity.type, page: m.entity.page, assumed: Boolean(r?.suggested), why: r?.suggested?.evidence ?? null,
    verdict: m.verdict, last_7d: m.volume.mentions_7d, last_30d: m.volume.mentions_30d, prior_30d: m.volume.mentions_prior_30d, as_of: m.as_of,
    top_shows: occ.rows.map(x => ({ show: x.channel_name, channel_id: x.channel_id, episodes: x.count })),
  });
}
const lead = brands[0];
let quotes = await arcmira.search({ query: lead.name, about: [lead.id], after: arcmira.daysAgo(30), limit: 3 });
const quotesTagged = quotes.chunks.length > 0;   // false: the fallback matched the words, which can be a namesake; say so
if (!quotesTagged) quotes = await arcmira.search({ query: lead.name, after: arcmira.daysAgo(30), limit: 3 });
return {
  brands, quotes_about: lead.name, quotes_tagged_to_brand: quotesTagged,
  quotes: quotes.chunks.map(c => ({ said: c.text.slice(0, 300), show: c.channel_name, episode: c.video_title, date: c.published_at, url: c.watch_url })),
};

A good answer

• Names each brand with its type and id, and any it assumed or set aside as a namesake.
• One row per brand: the verdict, the 7-day, 30-day and prior 30-day counts, as_of, and the shows that mention it most with episode counts.
• Two or three quotes about the user's brand in the speakers' words, each with show, date and watch_url, and says when they matched the words rather than the brand.
• For alerts, confirms what to follow and how it arrives before company-watch saves the monitor.

Traps

• Counts measure the shows Arcmira indexes, not the internet. Cite as_of before saying a brand was not mentioned.
• Each momentum call uses 10 credits at any window: one per brand.
• A mention is not an ad read. Whether a mention was paid is sponsor-research: recommendations with kind: "sponsored" is a Pro+ read and can return recommendations_not_enabled. Explain the account-access limit and the required tier and link the API reports in unlock; never label a mention paid or organic without that read.
• Read the quotes before asserting a lone mention: a show far from the brand is a namesake the catalog mislabelled.

Docs: https://arcmira.com/docs/mcp-server

After the answer

Monitors can follow what a research turn found, and each alert uses 25 credits from the account's plan, then its on-demand budget. The company-watch skill sets one up.

If anything was wrong, slow, or missing for the user, or they want a source or capability Arcmira lacks, send one arcmira_feedback.