2 posts tagged with "Advertising"

TL;DR​

July data from 18 audience packages in one store: two crowds with inquiry costs just 10% apart (¥38.9 vs ¥43.0) ran 4× apart in ROI (5.30 vs 1.25). Inquiry cost and ROI answer different questions — cost per inquiry says whether the traffic was bought expensively; ROI says whether the buyers were worth it — and either ruler alone, used in an environment month, will mislead you.

The situation: ranking by one metric produces fiction​

The comfortable way to read a crowd report is to sort it: by inquiry cost, cut the priciest; by ROI, cut the worst. In July's real data, those two sorts disagree completely. This reconciliation came out of a crowd-report audit while building AI Operations.

The data: one ruler prices traffic, the other grades it​

Across crowds (one month, 18 packages): inquiry costs spread ¥32–44, yet near-identical costs carried ROI from 1.25 to 5.30 — "store new-buyers" at ¥43.0 cost only 10% more than "cross-border buyers" at ¥38.9, and returned a quarter of the ROI. Inquiry cost measures what it takes to pull in one interested buyer; what those buyers then purchase, and at what value, is invisible to it.

Across months (the same store, seven months): January–June crowd inquiry costs held at ¥16–21 with ROI 8.4–18.5; in July, spend tripled (×3.1), cost doubled to ¥39, and ROI collapsed to 4.0. All 18 packages breached together — July was an environment month (platform competition, market-wide moves), not one crowd suddenly failing.

View	Subject	Inquiry cost	ROI	Note
Across crowds (July)	Cross-border buyers	¥38.9	5.30	Highest ROI among the 18 packages
Across crowds (July)	Store new-buyers	¥43.0	1.25	10% pricier, a quarter of the ROI
Across crowds (July)	All 18 packages	¥32–44	1.25–5.30	Costs bunch in a narrow band; ROI fans out 4×
Across months (Jan–Jun)	Store-wide crowds	¥16–21	8.4–18.5	The normal-environment watermark
Across months (July)	Store-wide crowds	¥39 (about doubled)	4.0	Spend ×3.1; all packages breached together

(Technical note: the report's ROI uses 15-day-attributed GMV — while measured attribution back-fill runs as late as day 29 after week end, see Is 16 Days Enough for Marketplace Ad Data?. That ROI only counts what landed inside 15 days: systematically low for long-cycle B2B buyers, and still drifting between months as the ledger finishes posting.)

What it's worth: the mis-pruning ledger​

Repricing by a single ruler in an environment month is wrong in both directions: June's good environment (ROI 18.5) inflates every crowd and hides the ones that genuinely need fixing; July's bad environment (ROI 4.0) condemns them all — including crowds that were merely dragged down by the month. Separating "environment" from "crowd" is what makes pruning precise: what deserves cutting deserves it in good months too; nothing gets cut for the weather.

Disciplines for operators​

1. Read both rulers together: inquiry cost prices the traffic; ROI grades it. Similar costs with multiples-apart ROI is an audience-selection problem — repricing cannot fix it.
2. No rankings in environment months: when store-wide crowd spend and costs move together (as in July), cross-audience comparisons are void that month.
3. Discount the ROI: 15-day-attributed ROI runs systematically low for B2B and drifts until settlement closes (when the platform locks the period's numbers and stops back-filling) — it has not earned the "sole benchmark" chair.
4. Reprice on consecutive trends: single months are noise; three months in one direction with real magnitude is a trend. The full monthly procedure lives in The 1688 Crowd Premium Monthly Method.

The judgment order for developers​

The discipline above ships as an automated pipeline in the crowd report. A comparable month = a finalized month (16 days after month-end, the attribution cutoff) with spend in that month; for cost-trend math, months with zero inquiries are filtered out as well. Checks run in dependency order, later checks overriding earlier conclusions:

Order	Check	Trigger	Outcome	Override relation
1	Finalized-month filter	Only finalized months are judged (16 days after month-end)	Months still in progress are excluded whole	Runs before everything
2	Whitelist split	Crowd not on the operable whitelist (crowds that can actually be repriced)	No data, no judgment	Domain gate
3	Low-spend grouping	Finalized final-month spend below 0.10 × the median spend of operable crowds	Grouped separately, no advice	Before trend; untouched by freeze
4	Trend check	Two consecutive comparable months with spend but zero inquiries → cut candidate; the 2 moves across the latest 3 cost-comparable months point the same way with cumulative change ≥ 0.20 → raise/cut; fewer than 3 months → insufficient	Three-way directional call	Zero-inquiry outranks magnitude
5	Market-freeze overlay	Store-wide inquiry cost moves more than 0.40 month-over-month	Every directional call above flips to frozen	Overrides all directional calls, zero-inquiry cuts included
6	Execution-state filter	Marked stopped → stopped; premium already 0 → no cut advice	Re-judged by execution facts	Runs last, overrides directional calls

(Technical note: the environment check sits after the trend check, not before — each crowd gets its own direction first, then the store-wide overlay flips directional calls to frozen; an environment month freezes the advice without swallowing state groups like low-spend or insufficient data. The freeze trigger is the inquiry-cost ratio, not spend: if spend doubles and inquiries double with it, cost hasn't moved and month-over-month self-comparison still works — freezing only when the efficiency baseline itself shifts. The 0.40 threshold comes from the measured split — normal months move 0.5%–27.6%, structure months 98%–116% — and 0.40 sits mid-band. And the 15-day ROI is display-only in this pipeline; it never enters the judgment.)

FAQ​

Should audience performance be judged on ROI or inquiry cost?​

Both, always: inquiry cost is the price of the traffic, ROI is its quality. Either one alone gets bent out of shape by environment months or single big orders.

Why can similar-cost audiences differ 4× in ROI?​

Inquiry cost only says whether the traffic was expensive, not whether the buyers convert. Audiences with different order sizes and paths turn the same inquiry price into very different GMV.

What to do in a month when all crowd costs jumped together?​

Call it an environment month — when store-wide crowd spend and costs double together, no cross-audience repricing; return to each crowd's own trend after the environment recovers.

That "two rulers + environment detection" method for crowd reports is built into AI Operations — LLM-powered analysis that automatically surfaces market trends, user behavior, and sales data to drive strategy. A crowd report deserves more than one sort button.

TL;DR​

The same product, listed in different marketplace ad solutions, can cost 4× more per inquiry. Weekly ad records for three key products in one store show the same product ranging from ¥17 to ¥78 — and the ordering is dictated by the solution: the merchant growth program is the most expensive for all three products, and switching solutions moves one product's cost by up to 4×. Three rules fall out: grade products on the blended number, grade solutions on same-product same-period comparisons, and never compare solution numbers across non-overlapping time windows.

The situation: the product you're about to pause was wronged by its channel​

Monday review: a product's inquiry cost in your flagship solution looks terrible, and you're considering pausing it. Hold on — the real ledgers of three key products in one industrial-goods store (anonymized) show the same product ranging from ¥20 to ¥78 depending on the channel it enters through. Same product, same page, same price. This reconciliation came out of a weekly-ad-ledger audit while building AI Operations.

Why: for the same product, the solution sets the price​

Line up the weekly records of all ad solutions for three key products. Full-history view first (inquiry cost = cumulative spend ÷ cumulative inquiries):

Ad solution	Delivery window	Product A	Product B	Product C
Whole-store promotion	2024-04 ~ 2026-06 (68–116 wks)	¥30	¥37	¥26
Site-wide, shop-boosting	2025-11 ~ 2026-06 (31–33 wks)	¥25	¥25	¥17
Merchant growth program	since 2026-06-29 (8 wks)	¥77	¥78	¥49
New-customer crowd	same period (8 wks)	¥50	¥20	¥29
Cross-border express	same period (7–8 wks)	¥42	¥22	¥28

Three layers of structure, each more useful than the last:

1. Full history: priciest vs cheapest is 4×+ — ¥78 against ¥17.

2. The ordering is dictated by the solution. The "Merchant growth program" is the most expensive for all three products (¥49–78) — three completely different products, uniformly expensive in this one solution. The dominant factor is the solution (what traffic it buys), not the product (what it sells).

3. Within the same period: 1.8–3.9×. The last three solutions share one time window (8 weeks from 2026-06-29), so their comparison is clean: 1.8× for Product A, 3.9× for Product B, 1.8× for Product C.

(Technical note: the first two solutions' data ends on 2026-06-29 and the last three start that very day — the windows don't overlap. So "old ¥25 vs new ¥77" mixes two factors: solution differences and market seasonality; concluding directly misleads. Statistically this is kin to Simpson's paradox — conclusions consistent per layer can flip once merged. Every "n×" claim in this article comes from the same-period window only.)

One counter-intuitive detail: the "Merchant growth program" isn't cold-start expensive — it keeps getting more expensive. Across its 8 weeks, inquiry cost climbed from ¥26 to ¥107. That retires the "give the new solution time" excuse; money dictated by traffic structure does not arrive with waiting.

What it's worth: two ledgers​

The mis-kill ledger. Product B runs at ¥20 per inquiry in "New-customer crowd," about a dozen-plus inquiries a month. Pause the product because it shows ¥78 in the growth program, and what you discard is not a bad product — it's a cheap channel still delivering steadily.

The true-cost ledger. Which of Product B's five numbers (¥37 / ¥25 / ¥78 / ¥20 / ¥22) is real? All of them, and none. Its actual acquisition cost is the blended one: ¥32,265 total spend ÷ 911 inquiries = ¥35. A single-solution number can overstate or understate a product; only the blend is the product's real price tag — and the stable anchor for budget allocation.

Disciplines for operators​

1. Grade products on the blend: total spend ÷ total inquiries. Per-solution numbers answer "is this channel expensive," never "is this product good."
2. Grade solutions on same-product, same-period comparisons: fix a basket of products and a time window; only then does the ordering mean anything.
3. Never compare across non-overlapping windows: solution handover periods are the danger zone — an old solution's historical cost is not the new one's ruler.
4. A persistently worsening solution isn't worth waiting for: cut budget after 4+ weeks of climbing costs; make keep-or-stop calls with the settlement discipline from Is 16 Days Enough for Marketplace Ad Data? and the full pre-pause checklist in Five checks before you pause.

FAQ​

The same product shows very different inquiry costs across ad solutions — is that normal?​

Yes. Across three key products we measured ¥17 to ¥78 for the same product, and the ranking followed the solution, not the product — each solution buys different traffic.

Which inquiry cost should I use to judge a product?​

The blended one: total spend across all solutions ÷ total inquiries. A single-solution number only says what that channel pays for this traffic — it cannot grade the product.

Can I compare costs across different ad solutions directly?​

Only within overlapping time windows. When old and new solutions don't share dates, the gap mixes solution differences with market seasonality — comparing directly misleads.

That "line up every channel for the same product" reconciliation is built into AI Operations — LLM-powered analysis that automatically surfaces market trends, user behavior, and sales data to drive strategy. Every product's real acquisition cost deserves to be computed once, fully.