DIY pirate costumes: feature accessories from your catalog
Candidate for review2 → 16 supplied original items
Topic share grew faster than the collected sample. Check business fit before using the idea.
Inspect this topic’s evidenceCampaign opportunity demo
Campaign opportunities for your shop.
Choose a relevant idea in time to act. Review the trend, supporting evidence and fit for your products before sending.
Example retailer: a Toronto costume shop selling pirate hats, eye patches, and face paint.
Campaign goal: choose a useful pre-Halloween email idea for local subscribers.
Saved pre-Halloween scenario: October 24–26, 2026. Synthetic observations, not live Toronto trend data.
Compare Earlier day: Oct 24 → Latest day: Oct 25 (UTC). Each is one full day, midnight to midnight. These are saved dates, not yesterday and today.
Your trend dashboard
See all topics at a glance, then inspect an idea before considering a campaign.
4 saved example topics
1 candidate for review
2 scored source groups
Scored sources: X (Twitter), Reddit. Charts compare Oct 24 and Oct 25 (UTC) on the same 0–10% scale. Share means the topic’s percentage of collected originals, averaged equally across source groups—not market-wide demand.
A topic with more posts over time, but the same share of collected posts
Not flagged as emerging8 → 16 supplied original items
More original items do not establish an emerging trend when the topic’s share stays flat.
Inspect this topic’s evidenceRepeated copies of posts about giving away costumes
Hold back: too little support6 → 3 supplied original items
Repeated delivery records do not count as new original posts. After removing repeated copies, too few original items remain to recommend this topic.
Inspect this topic’s evidenceA topic with no posts observed in the earlier period
Newly observed: growth not assessed0 → 6 supplied original items
No earlier originals were collected for this topic. We cannot calculate a growth ratio from zero.
Inspect this topic’s evidenceFinish your pirate costume
DIY pirate costumes are gaining attention in this saved example. Consider an email featuring pirate hats, eye patches, and face paint you already stock.
Origins (original evidence items): 2 → 16
Share of collected originals: 2.0% → 8.0%
Illustrative fit for a Toronto costume shop. Check your stock, local audience, and delivery or pickup timing before using this angle.
Why review this idea?
An origin is an original evidence item; copies count once using the supplied labels. Share is the topic's percentage of original items in each source group, averaged equally across X and Reddit. Here, 2% → 8% means four times the earlier share.
The saved analysis marks this as a review candidate: it passes the example minimum-support and share-growth rules. That is permission to review the idea, not proof it will work.
Halloween is seasonal; the example compares attention to one costume style before the holiday. Two days of growth do not establish an unusual seasonal trend or customer demand.
Toronto, the local audience, and stocked products are supplied scenario assumptions. The demo does not infer location, check inventory, establish factual credibility, or measure campaign performance.
Preview campaign draft
Draft preview — illustrative. This prewritten sample shows an optional starter draft in Mailchimp’s existing campaign workflow. No model generates this text and no Mailchimp account is connected.
Proposed handoff: pass the selected trend, supporting sources and campaign angle into Mailchimp. Optional starter copy uses details supplied by the owner or an authorized store connection. The native editor integration still needs validation.
Example audience: Toronto-area subscribers interested in costumes. The shop owner must confirm the recipient group and permission to contact them.
YOUR TORONTO COSTUME SHOP
Subject: Finish your pirate costume before Halloween
A few finishing touches for your pirate costume
Putting together a DIY pirate costume? Finish the look with a pirate hat, an eye patch, or face paint from our shop.
- Pirate hats
- Eye patches
- Face paint
Choose the accessories that suit your outfit. Check current stock and confirm pickup or delivery timing before Halloween.
Browse pirate accessoriesExample call to action; product links, availability and fulfilment details would be supplied by the shop.
What the shop owner checks before using this draft
- Read the supporting posts and check whether they justify the suggested campaign angle.
- Confirm that this topic is relevant to the selected subscribers.
- Verify that the featured products are in stock and can arrive or be collected before Halloween.
- Continue in Mailchimp to edit the subject, message and product links, select or confirm recipients, complete readiness checks, and explicitly send or schedule. The trends service does not send automatically.
This static preview neither creates a saved Mailchimp draft nor sends email. The topic, audience, products and copy are supplied examples; sales impact has not been measured.
Hold back copied costume-giveaway hype
100 recorded observations come from just 3 supplied origins.
Share of collected originals: 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.
Copies do not add independent originals. Verified redistribution could help us prioritize attention, but this demo does not use it to promote a topic.
Explore trends
Choose a topic to compare its two saved days and understand our decision. All four examples use synthetic data, not live trend discovery.
Share means the percentage of original items about this topic within each collected source group; the chart averages the source-group percentages equally. Counts below combine those groups. Neither measure covers all posts on the internet.
DIY pirate costumes: feature accessories from your catalog
Candidate for reviewSame bar scale for every topic: 0–10%. Dates are UTC.
- Oct 24: topic originals / all collected originals
- 2 of 100 across all scored source groups
- Oct 25: topic originals / all collected originals
- 16 of 200 across all scored source groups
Our System’s Interpretation
The topic’s share rose from 2.0% to 8.0% and passes the example support and growth thresholds. It becomes a candidate for review.
Counts use supplied original-item labels, not verified audience reach. Two days do not establish sustained demand or local subscriber interest.
See how the source percentages were calculated
| Source group | Oct 24 | Oct 25 |
|---|---|---|
| X (Twitter) | 1 of 50 (2.0%) | 8 of 100 (8.0%) |
| 1 of 50 (2.0%) | 8 of 100 (8.0%) |
A topic with more posts over time, but the same share of collected posts
Not flagged as emergingSame bar scale for every topic: 0–10%. Dates are UTC.
- Oct 24: topic originals / all collected originals
- 8 of 100 across all scored source groups
- Oct 25: topic originals / all collected originals
- 16 of 200 across all scored source groups
Our System’s Interpretation
Topic share stayed at 8.0%. It does not pass the example growth threshold, even if the number of posts increased.
Counts use supplied original-item labels, not verified audience reach. Two days do not establish sustained demand or local subscriber interest.
See how the source percentages were calculated
| Source group | Oct 24 | Oct 25 |
|---|---|---|
| X (Twitter) | 4 of 50 (8.0%) | 8 of 100 (8.0%) |
| 4 of 50 (8.0%) | 8 of 100 (8.0%) |
Repeated copies of posts about giving away costumes
Hold back: too little supportSame bar scale for every topic: 0–10%. Dates are UTC.
- Oct 24: topic originals / all collected originals
- 6 of 100 across all scored source groups
- Oct 25: topic originals / all collected originals
- 3 of 200 across all scored source groups
Our System’s Interpretation
100 delivery records map to only 3 supplied original items, below the example minimum of five. Delivery records are not a verified repost or reader count.
Counts use supplied original-item labels, not verified audience reach. Two days do not establish sustained demand or local subscriber interest.
See how the source percentages were calculated
| Source group | Oct 24 | Oct 25 |
|---|---|---|
| X (Twitter) | 3 of 50 (6.0%) | 2 of 100 (2.0%) |
| 3 of 50 (6.0%) | 1 of 100 (1.0%) |
A topic with no posts observed in the earlier period
Newly observed: growth not assessedSame bar scale for every topic: 0–10%. Dates are UTC.
- Oct 24: topic originals / all collected originals
- 0 of 100 across all scored source groups
- Oct 25: topic originals / all collected originals
- 6 of 200 across all scored source groups
Our System’s Interpretation
We collected no originals for this topic on Oct 24. A growth ratio cannot be calculated from zero; more comparable observations would be needed before applying that rule.
Counts use supplied original-item labels, not verified audience reach. Two days do not establish sustained demand or local subscriber interest.
See how the source percentages were calculated
| Source group | Oct 24 | Oct 25 |
|---|---|---|
| X (Twitter) | 0 of 50 (0.0%) | 3 of 100 (3.0%) |
| 0 of 50 (0.0%) | 3 of 100 (3.0%) |
Compare all topics and inspect the evidence
Inspect the signals
What baseline, current, and window mean
- Window = a fixed period of time
- We group original evidence items by their publication time. Here, each window is 24 hours long, and we compare two adjacent windows of equal length.
- Baseline = the earlier comparison window
- The earlier 24 hours are our reference: Oct 24, 2026 at 00:00 UTC up to Oct 25, 2026 at 00:00 UTC.
- Current = the later comparison window
- The later 24 hours are compared with that reference: Oct 25, 2026 at 00:00 UTC up to Oct 26, 2026 at 00:00 UTC. “Current” means the later period in this saved replay, not today or right now.
- Origin = one original evidence item
- In this synthetic demo, supplied origin labels identify originals. Copied versions share a label and count as one origin. “All origins” includes other topics and background items in the same source group and window.
Each window includes its start and excludes its end. An item published exactly at the shared boundary between the two periods belongs to the current window.
More definitions: observations, share, growth, and review rules
- Topic and source group
- A topic is the subject an item discusses. Here, the source groups represent X posts and Reddit community posts. We compare the same declared groups in both periods.
- Recorded observation versus origin
- An observation is a saved delivery record. Several records can point to one original item, so 100 observations are not necessarily 100 distinct posts, authors, or independent views. Re-delivering the same delivery ID does not add another record.
- Independent support
- Here this means the number of distinct supplied original-item IDs after copies are combined. Their real-world independence and factual truth are not established by the fixture.
- Numerator and denominator
- In 1 / 50, the numerator (1) is the count for this topic. The denominator (50) is the total count for that source group and period. Dividing them gives the topic's share: 2%.
- Share, normalization, and equal weights
- For each source group, divide this topic's origins by all origins in that group. This expresses the count as a percentage, so overall source growth is accounted for. The report then averages the X and Reddit percentages equally. This normalization does not make the sample representative of the whole market.
- Lift or growth ratio
- The later normalized share divided by the earlier share. 2% → 8% is 4× the earlier share, or an increase of 6 percentage points. It is not 4× sales, customer interest, or confidence.
- Review candidate, rule, or gate
- A gate is a condition required to proceed. This example requires at least five later-window origins and at least twice the earlier share. Passing makes a topic worth reviewing; these thresholds are not calibrated confidence or proof of campaign usefulness.
- No earlier topic data
- The topic has zero origins in the earlier window, even though other items exist in that window. A growth ratio would require division by zero, so the demo withholds that calculation rather than claiming infinite growth.
- Coverage and propagation
- Coverage describes which sources and periods were observed. Complete coverage is supplied as an assumption for this synthetic dataset, not measured across the internet. Propagation is how widely an item is repeated or distributed; it is different from independent support.
Evidence-aware ordering puts topics passing the example review rules first, then uses relative share growth to order results. Raw observation ordering uses delivery counts, including copies. Neither order is a confidence score.
Sorted by evidence-aware candidate priority. Inspect the baseline and denominators.
| Topic and what happened | Recorded observations | Original items (origins) | Topic share of collected originals Oct 24 → Oct 25 | Our System’s Interpretation | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| DIY pirate costumes: feature accessories from your catalogThis topic gained a larger share of our collected originals and passes the example support rule. | 16 |
16 2 in baseline |
2.0% → 8.0% 4.00× the earlier share |
Worth reviewing | |||||||||
Inspect counts and evidenceEach cell shows origins for this topic out of all origins in that source group, followed by the percentage. For example, 1 of 50 (2.0%) means one original evidence item about this topic among 50 original items in that source group published during that period. The total includes other topics and background items.
For each period, the report averages the X and Reddit percentages with equal weight. It does not average the raw counts. Up to five original-item IDs from the later window are shown. The .example URLs are fictional test addresses, not live source pages. Supplied labels identify which items are copies; the demo did not discover or verify that source history. | |||||||||||||
| A topic with more posts over time, but the same share of collected postsThe count rose, but this topic did not gain a larger share of our collection. We do not shortlist it as emerging. | 16 |
16 8 in baseline |
8.0% → 8.0% 1.00× the earlier share |
Flat share: not emerging | |||||||||
Inspect counts and evidenceEach cell shows origins for this topic out of all origins in that source group, followed by the percentage. For example, 1 of 50 (2.0%) means one original evidence item about this topic among 50 original items in that source group published during that period. The total includes other topics and background items.
For each period, the report averages the X and Reddit percentages with equal weight. It does not average the raw counts. Up to five original-item IDs from the later window are shown. The .example URLs are fictional test addresses, not live source pages. Supplied labels identify which items are copies; the demo did not discover or verify that source history. | |||||||||||||
| Repeated copies of posts about giving away costumesRepeated copies do not supply enough independent original items to pass the example rule. | 100 |
3 6 in baseline |
6.0% → 1.5% 0.25× the earlier share |
Too few originals: hold back | |||||||||
Inspect counts and evidenceEach cell shows origins for this topic out of all origins in that source group, followed by the percentage. For example, 1 of 50 (2.0%) means one original evidence item about this topic among 50 original items in that source group published during that period. The total includes other topics and background items.
For each period, the report averages the X and Reddit percentages with equal weight. It does not average the raw counts. Up to five original-item IDs from the later window are shown. The .example URLs are fictional test addresses, not live source pages. Supplied labels identify which items are copies; the demo did not discover or verify that source history. | |||||||||||||
| A topic with no posts observed in the earlier periodWe found no originals for this topic on Oct 24, so we cannot calculate a growth ratio. | 6 |
6 0 in baseline |
0.0% → 3.0% Cannot calculate growth |
No prior posts: no growth ratio | |||||||||
Inspect counts and evidenceEach cell shows origins for this topic out of all origins in that source group, followed by the percentage. For example, 1 of 50 (2.0%) means one original evidence item about this topic among 50 original items in that source group published during that period. The total includes other topics and background items.
For each period, the report averages the X and Reddit percentages with equal weight. It does not average the raw counts. Up to five original-item IDs from the later window are shown. The .example URLs are fictional test addresses, not live source pages. Supplied labels identify which items are copies; the demo did not discover or verify that source history. | |||||||||||||
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 artificial intelligence / machine learning (AI/ML); no model runs here. The displayed counts come from fixed code rules over supplied example labels. A fixture is a prepared test dataset; synthetic means it is made up for the demonstration. No model responses or measured AI quality are displayed.
- Not implemented: labels supplied
Suggest topics and possible copies
Start with fixed rules using source history. AI/ML could suggest ambiguous topic and copy groups from text and its descriptive fields (metadata). Similar wording alone does not prove a common origin or that a claim is true.
- Origin checks: not implemented
Check what each label means
The prepared dataset supplies topic and original-item labels. Real suggestions would need source-history checks, quality testing, and withholding uncertain answers before counting. That checking process is not implemented.
- Implemented in the Python demo
Count, compare, and save results
Python computes counts, comparable shares, and review rules, then saves a consistent result before this page is exported. This webpage does not run ingestion or database writes. Proposed AI groups would change the counts and ordering, so they would need quality testing.
- 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 saved example starts with supplied labels. The proposed AI stages are not invoked. Source excerpts and a structured marketer brief are absent from the prepared counting data. The retailer and campaign angle displayed above are supplied examples; they are not inputs to the ordering code or model outputs. Relevance and factual support have not been evaluated.
Inputs, evaluation, and failure behavior we would require
- Before counting: avoid mistaking copies for growing support
- Would use allowed text and its source history (provenance). Test known rewrites, quotations, and republished copies (syndication). Check both grouping mistakes and whether those mistakes change the recommendation. A score on pairs of examples alone is insufficient.
- After counting: explain the candidate clearly
- Would use a checked result set (snapshot), allowed source passages, and the marketer's business brief. Test whether claims are supported and relevant. Withhold an answer when support is insufficient (abstain). Citations alone do not establish support. Source text gets no tools that can access private data or send messages.
- Separate failure paths
- Proposed, untested behavior: an explanation failure leaves checked evidence available without explanation text. If grouping fails before counting, withhold new labels and keep the previous allowed saved result with its true age. A newly discovered topic still needs an earlier comparison period.
AI terms in plain language
- Semantic matching and topic grouping
- Group items by meaning, not just identical words. Items discussing the same topic are not necessarily copies of one original.
- Provenance and canonical labels
- Provenance is the evidence of where content came from. A canonical label is the stable ID we choose to refer to one item or group. Assigning that ID does not prove the grouping is correct.
- False merge and missed copy
- A false merge combines different originals and can hide support. A missed copy treats copied material as separate originals and can inflate support. Both can change which topic is recommended.
- Schema checks versus grounding
- A schema checks required fields and types. Grounding checks whether a claim is actually supported by the allowed source text. A well-formed response with a citation can still be wrong.
- Marketer brief and eligible evidence
- The brief is the supplied business goal, products, and intended audience. Eligible evidence is content we are allowed to use and that passes the required checks. Eligibility does not establish that every source claim is true.
- Evaluation and held-out examples
- Compare outputs with examples whose expected answers are known. Keep separate test examples, including later dates and related copy families, out of tuning. Passing these checks would not itself prove that marketers benefit.
- Abstention
- Withhold an answer when the evidence is missing, uncertain, or unsuitable instead of guessing.
- Model, prompt, tokens, and latency
- The model produces predictions or text; the prompt is its instructions and input. Tokens are text chunks used to measure model input/output and cost. Latency is the time spent waiting for a response. Track model and prompt versions to identify which configuration produced an output.
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 (saved result version): 1; local publication time: 2026-11-02T01:31:44.792762+00:00.
- Replay and idempotent ingestion
- Deliver the same input records again with the same IDs and contents. The ingestion step adds no extra observations. This does not mean that every rerun of the whole program creates no new snapshot.
- Snapshot and publication
- A snapshot is a saved set of results treated as fixed. Publication makes a checked version the current feed by changing its stored version reference (pointer). Publication time is when Python saved the result, not when the source item was published or this page loaded.
- Transaction and rollback
- A transaction saves related database changes together or discards them together. The recorded test injects an error before promotion, so the partial new result is discarded (rolled back) and the previous feed remains after reopening the database. It does not demonstrate recovery from a killed process or concurrent publishers.
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
The prepared data supplies topic and original-item labels and assumes complete source coverage. The retailer context is supplied for this example. This prototype checks counting and saving results; it does not automatically discover copy relationships, measure live source completeness, test a model, fetch sources, or prove recovery across multiple machines.
One original is assigned to one topic and source group in this example. X and Reddit percentages contribute equally to the average; that is a chosen rule, not proof the sample represents the market. Original items about other topics and background content are included in the total (denominator); their selection needs validation on real data.
The Toronto retailer, local audience, stocked accessories, and campaign angle are fixed examples supplied for this demonstration, not inferred customer intent, verified inventory, geolocation, 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.