Campaign opportunity demo

A useful idea for your next campaign.

Start with what you sell. Review a campaign angle and see why another popular topic was held back.

Example retailer: repair products and care supplies.

Campaign goal: a useful care-and-repair guide for existing subscribers.

Illustrative merchant fit. Synthetic observations, not a live trends feed.

Worth reviewing

Help customers care for what they own

A care-and-repair guide could feature relevant repair products from your catalogue.

Independent origins: 2 → 16
Observed share: 2.0% → 8.0%

Illustrative fit, not an inferred recommendation. Check your stock, audience, and the usefulness of the advice before using this angle.

Why review this idea?

The repair-first retail topic gained share in the monitored source cohorts. The saved analysis marks it as a review candidate.

This supports further review. It does not establish customer demand, factual credibility, or campaign performance.

Hold back

Leave copied giveaway hype off the shortlist

100 recorded observations come from just 3 supplied origins.

Observed share: 6.0% → 1.5%

Wait for more independent support before considering this campaign angle.

Why was this held back?

Copied observations do not become additional independent votes. This topic falls below the example policy's minimum of five current origins.

Repetition can still tell us about propagation; it does not establish independent support or suitability for this retailer.

Compare all topics and inspect the evidence

Inspect the signals

Historical replay ends 2026-10-26T00:00:00+00:00. Baseline: 2026-10-24T00:00:00+00:00 to 2026-10-25T00:00:00+00:00; current: 2026-10-25T00:00:00+00:00 to 2026-10-26T00:00:00+00:00. Each window excludes its end.

Sorted by evidence-aware candidate priority. Inspect the baseline and denominators.

TopicRecorded observationsIndependent originsSource-normalized shareInterpretation
repair first retail
16
16
2 in baseline		 2.0% → 8.0%
4.00× share		 Review candidate
Inspect counts and evidence
The share is the equally weighted mean of each source's independent-topic fraction. These counts include background observations in the denominator.
Canonical source cohortBaseline topic / all originsCurrent topic / all origins
blog1 / 508 / 100
forum1 / 508 / 100

Up to five distinct current origins are shown. URLs are fictional .example fixtures; supplied origin labels identify copies. They are not independently discovered provenance.
platform wide noise
16
16
8 in baseline		 8.0% → 8.0%
1.00× share		 No relative rise
Inspect counts and evidence
The share is the equally weighted mean of each source's independent-topic fraction. These counts include background observations in the denominator.
Canonical source cohortBaseline topic / all originsCurrent topic / all origins
blog4 / 508 / 100
forum4 / 508 / 100

Up to five distinct current origins are shown. URLs are fictional .example fixtures; supplied origin labels identify copies. They are not independently discovered provenance.
recycled giveaways
100
3
6 in baseline		 6.0% → 1.5%
0.25× share		 Too little independent support
Inspect counts and evidence
The share is the equally weighted mean of each source's independent-topic fraction. These counts include background observations in the denominator.
Canonical source cohortBaseline topic / all originsCurrent topic / all origins
blog3 / 502 / 100
forum3 / 501 / 100

Up to five distinct current origins are shown. URLs are fictional .example fixtures; supplied origin labels identify copies. They are not independently discovered provenance.
new low sample
6
6
0 in baseline		 0.0% → 3.0%
Unmeasured		 Baseline needed
Inspect counts and evidence
The share is the equally weighted mean of each source's independent-topic fraction. These counts include background observations in the denominator.
Canonical source cohortBaseline topic / all originsCurrent topic / all origins
blog0 / 503 / 100
forum0 / 503 / 100

Up to five distinct current origins are shown. URLs are fictional .example fixtures; supplied origin labels identify copies. They are not independently discovered provenance.

Observation totals count stored delivery records for origins in the current publication window. They are not distinct-post, unique-reader, or time-bounded engagement counts.

“Review candidate” uses an illustrative rule: at least five current origins and a twofold share increase. It is not a probability, proof of a real trend, or a promise of campaign performance. A zero topic baseline triggers abstention.

AI/ML design and evaluation (proposed)

Where AI/ML would help

Proposed design; no runtime AI/ML. The displayed counts come from deterministic code over supplied fixture labels. No model responses or measured AI quality are displayed.

  1. Not implemented: labels supplied

    Suggest topics and possible copies

    Start with deterministic provenance rules. AI/ML could help suggest ambiguous topic and copy groups from text and metadata. Similar wording alone does not prove common origin or credibility.

  2. Provenance checks: not implemented

    Establish the label contract

    Today's synthetic fixture supplies canonical topic and origin labels. Real suggestions would need provenance checks, evaluation, and abstention before counting. That process is not implemented.

  3. Implemented: local checks pass

    Count, normalize, and publish

    Code computes counts, shares, gates, and local commits. Proposed AI groupings would affect those counts and rankings, so their validation and evaluation would be essential.

  4. Not implemented: optional

    Explain relevance for a marketer

    Given a marketer brief and eligible evidence, explain why a topic may fit a campaign and cite supporting excerpts. A human reviews the idea; no campaign is sent.

The numerical replay starts at supplied labels. The proposed AI stages are not invoked. Source excerpts and a structured marketer brief are absent from the counting fixture. The retailer and campaign angle displayed above are hand-authored illustrations; they are not inputs to the ranker or model outputs. Relevance and factual support have not been evaluated.

Inputs, evaluation, and failure behavior we would require
Upstream matching: reduce false momentum
Would use permitted text and provenance. Evaluate labeled rewrites, quotations, and syndication: group split/merge errors plus downstream decision changes. Pairwise scores and fixture-specific flip thresholds alone are insufficient.
Downstream explanation: reduce interpretation effort
Would use an eligible snapshot, permitted excerpts, and a marketer brief. Evaluate claim support, audience fit, and abstention; citations alone do not prove support. Untrusted source text gets no privileged tools.
Separate failure paths
Proposed, untested behavior: explanation failure leaves validated evidence available without prose. Upstream grouping failure withholds new labels; keep the previous eligible snapshot with its true age. A newly discovered topic still lacks a historical baseline.
Replay and publication checks

Recorded replay and failure checks

  • New observations on repeated delivery: 0.
  • Previous feed retained after interrupted publication and database reopen: Yes.
  • Published snapshot: 1; local publication time: 2026-10-27T20:36:50.458531+00:00.

Actual results from this Python run. The comparison controls only reorder the displayed results; they do not execute ingestion or database transactions.

Scope, assumptions, and remaining limitations

Topics, canonical origins, retail relevance, and complete coverage are synthetic fixture inputs. This prototype tests counting and publication contracts; it does not discover origin clusters, measure live source completeness, evaluate a model, crawl sources, or prove distributed recovery.

One original belongs to one topic/source in this fixture. The ranking uses fixed equal source weights, which is a policy choice rather than an unbiased estimate of the market. Background observations form the denominator; their selection needs validation on real data.

The retailer and campaign angle are fixed, hand-authored illustrations, not inferred customer intent or generated copy. No campaign is generated or sent. A candidate still requires the marketer's judgment and evidence of usefulness. Freshness is relative to the fixed replay clock, not a live service promise.