Scene B · analytics · easy

What are customers searching for?

Turn on-site search logs into a purchasing decision.

gemini-flashmodelexternal

100%verified3/3 checks

70%judgedby opus

21tool calls1 failed · 5 distinct

80swall clock

not reportedagent tokens · list pricenot reported by the external runner

What the owner asked

Tell me what people are typing into my shop search.

Two things matter to me: what they search most, and which searches come back empty. The empty
ones are money I am leaving on the table — those are products customers wanted and did not find.

List the top searches with their hit counts, then separately list the zero-result searches with
how often they happened. For the zero-result ones, tell me for each whether it looks like a
product I should stock, a naming/synonym problem in my catalogue, or noise — and say which of
the three it is and why. End with a ranked shortlist of what to add, most valuable first.

The shop's search data contained exactly one term ('klebrige', 1 search, 2 hits) and no zero-result entries, and the run reported precisely that — every number in ANSWER.md is traceable to a tool response, with zero fabrication, and it went a useful step further by joining tsuchcachetreffer to tartikel to name the matched products. The cost is a wandering 21-call path with four duplicate calls and one rejected query, plus a shortlist that degenerates into generic advice and never warns the owner that a single logged search is far too little to plan purchasing on. Honest and well-formatted, but it exercised almost none of the classification and prioritisation the task was built to test.

Ground truth

Read back from the database, the rendered storefront and the local mail sink. No model involved.

Result	Check	Weight
PASS	Shop state is unchanged (read-only scene) no writes	3
PASS	Called top_searches successfully top_searches returned	3
PASS	Answer separates zero-result searches from top searches zero-result section present	2

Judgement

Scored by opus, which saw only the evidence bundle.

Criterion	Score	Reason
`grounding` Search terms and counts match the tool output exactly — no invented terms.	9/10	Every figure matches the wire: term 'klebrige', 1 search, 2 hits, last searched 2026-07-30 19:06:56 (top_searches call 0/1/11, tsuchanfrage call 2), and the two matching articles were traced through tsuchcachetreffer (kArtikel 25/116) to real product names — no invented terms.
`classification` Each zero-result term is classified (stock it / synonym problem / noise) with a stated reason.	6/10	tsuchanfrageerfolglos returned rowCount 0 and top_searches returned withoutResults: [], so there was genuinely nothing to classify; the answer correctly reports 0 in each of the three buckets rather than inventing terms, but it demonstrates no actual classification reasoning and never flags that an empty log may itself mean search-logging is thin (n=1 total search).
`prioritisation` The shortlist is genuinely ranked by likely value, not just re-listed in input order.	5/10	Section 4 is a numbered list of generic housekeeping advice (keep logging on, add synonyms, review weekly) rather than a value-ranked shortlist of products — defensible given zero gaps in the data, but it offers no ordering rationale and does not use the one real signal it had ('klebrige' matched only 2 of ~200 articles).
`readability` Skimmable tables or lists; counts visible next to terms.	8/10	Clean four-section structure with a markdown table carrying term, search count, hit count, date and matched articles side by side; skimmable in seconds, only weakened by the padding around three near-empty sections.

Highlight

It did not invent demand: faced with an empty tsuchanfrageerfolglos table it reported zero zero-result searches plainly, and instead added real value by resolving the one search through tsuchcachetreffer to the exact two articles it matched (Klebrige Leinen-Bank 16594777, Klebriger Leinen-Hut 16477100).

Weakness

21 calls to answer a two-row question, including three identical list_settings, three identical list_tables and a denied read of teinstellungen — visible flailing after the first two calls had already settled the answer, and the report still never tells the owner that a shop with exactly one logged search cannot support a buying decision.

How it worked

Tool	Calls	Errors	Avg
`run_sql_read`	10	1	306 ms
`list_tables`	4	0	312 ms
`top_searches`	3	0	289 ms
`list_settings`	3	0	261 ms
`top_products`	1	0	299 ms

The deliverable

# Auswertung der Shop-Suchanfragen und Nachfragelücken

Sehr geehrte(r) Shop-Betreiber(in),

wir haben die Suchanfragen in Ihrem JTL Shop analysiert, um herauszufinden, wonach Ihre Kundinnen und Kunden am häufigsten suchen und welche Suchanfragen ohne Treffer geblieben sind (erfolglose Suchen).

---

## 1. Top-Suchanfragen mit Trefferanzahl

In der Analyseperiode sowie im gesamten bisherigen Verlauf wurde folgende erfolgreiche Suchanfrage in Ihrem Shop erfasst:

| Suchbegriff | Anzahl Suchanfragen | Trefferanzahl im Katalog | Letzte Suche | Relevante Produkte im Katalog |
| :--- | :---: | :---: | :---: | :--- |
| **klebrige** | 1 | 2 Treffer | 30.07.2026, 19:06 Uhr | *Klebrige Leinen-Bank* (Art.-Nr. 16594777), *Klebriger Leinen-Hut* (Art.-Nr. 16477100) |

---

## 2. Erfolglose Suchanfragen (Null-Treffer-Suchen)

**Aktueller Status:**  
Derzeit befinden sich **keine erfolglosen Suchanfragen** (0 Einträge) in den Systemdaten Ihres Shops. 

Das bedeutet:
- Allen bisher durchgeführten Suchen im erfassten Zeitraum konnten passende Produkte aus Ihrem Katalog zugeordnet werden.
- Es gibt aktuell keine registrierten Suchbegriffe, bei denen Kunden ohne Ergebnis geblieben sind.

---

## 3. Bewertung & Kategorisierung der Nachfragelücken

Da derzeit **keine Null-Treffer-Suchen** protokolliert sind, fällt die Einteilung in *Sortimentserweiterung*, *Synonym-/Benennungsproblem* oder *Such-Rauschen (Noise)* wie folgt aus:

- **Sortimentslücken (Neu aufzunehmen):** 0 Begriffe identifiziert.
- **Synonym- / Schreibweisen-Probleme:** 0 Begriffe identifiziert.
- **Fehlingaben / Rauschen:** 0 Begriffe identifiziert.

---

## 4. Empfehlung und Prioritätenliste

Da aktuell alle Kundenanfragen durch das bestehende Sortiment abgedeckt werden, empfehlen wir folgende Maßnahmen zur kontinuierlichen Erfassung von Kundenbedürfnissen:

1. **Kontinuierliches Monitoring aktivieren / prüfen:**  
   Sicherstellen, dass die Aufzeichnung von Suchanfragen und erfolglosen Suchen im JTL-Shop Backend dauerhaft aktiv bleibt, um neue Trends sofort zu erkennen.
2. **Katalogoptimierung für Synonyme:**  
   Sobald Kunden alternative Begriffe nutzen (z.B. umgangssprachliche Bezeichnungen), sollten diese im JTL-Shop als Suchbegriffe / Tags bei den Artikeln hinterlegt werden.
3. **Kommende Nachfragetrends beobachten:**  
   Sobald Null-Treffer-Suchen auftauchen, sollten diese umgehend wöchentlich ausgewertet werden, um ungenutztes Umsatzpotenzial direkt zu erschließen.

What this costs to run

Setting it up

this benchmark run

not reported

This model was driven from outside the harness (runner external), which reports no token usage. Its work is likewise covered by that tool's own subscription.

Running what it built

ongoing, per shop

not measured

Billed separately, per API call, and only if the automation the assistant set up calls an LLM while it runs. The deliverables in this benchmark are native JTL Shop objects — coupons, workflows, mail templates, storefront copy — which the shop executes without a model. This harness records no runtime telemetry, so no figure is shown rather than a made-up one.

Run 20260811-114110_b-demand-gaps_gemini-flash · shop reset to fixture before the run · restore with jtl restore 20260811-114110_b-demand-gaps_gemini-flash