Map buyer language once. Use it to write clearer pages and test whether AI recommendations change.
One input. Two outputs.
Buyers explain their decisions in public. They ask questions on Reddit. They complain in reviews. They compare products on forums.
A crawlable store can still speak seller language while product recommendations reflect buyer questions and concerns.
DecodeIQ maps that buyer language into nine entity types and freezes the questions used for measurement.
The Voice Map feeds buyer-matched content. The frozen questions create a baseline and same-query re-check.
DecodeIQ measures recommendation evidence. Your store analytics measure conversion.
The seller typed two things. Buyers said 1,499.
Seller input: AeroBuds Pro — ANC, IPX7. 2 spec tokens. That is all.
1,499 buyer entities from real conversations.
One buyer-intent query starts the scan.
Query: “best wireless earbuds for running”. Networks: reddit, youtube, amazon, editorial, forum.
73 sources analyzed, 1,499 entities extracted.
The scan becomes a ranked map of how buyers decide.
Buyer Concerns 90 · Objection Density 92 · Use Case Diversity 91
- Earbuds must stay in place through sweat and pace. (37 sources, 6 networks, strong signal)
- Runners must hear traffic and hazards. (37 sources, 6 networks, strong signal)
- Sweat resistance claims are not trusted. (37 sources, 6 networks, strong signal)
Each listing bullet traces to a stored buyer concern.
- Secure fit that holds through sprints and sweat. Traces to: Earbuds must stay in place through sweat and pace.
- IPX7 is a certified rating, not a marketing label. Traces to: Sweat resistance claims are not trusted.
- Noise cancellation disengages for road running. Traces to: Runners must hear traffic and hazards.
The lines cross because the mapping is stored data, not sequence.
54 recorded answers. 6 questions, 3 engines, 3 waves.
ChatGPT 18/18 · Gemini 18/18 · Perplexity 15/18
| Entity | Appears | Share | Pos. median |
|---|---|---|---|
| Shokz | 51/54 | 94.4% | 4 |
| Shokz OpenRun Pro 2 | 33/54 | 61.1% | 5 |
| Beats | 32/54 | 59.3% | 2 |
| Bose | 30/54 | 55.6% | 5.5 |
| Soundcore | 21/54 | 38.9% | 5 |
| Jabra | 20/54 | 37.0% | 3 |
“Which wireless earbuds for running include bone conduction design (keeps ears open)?”
“Which wireless earbuds for running deliver better safety while hearing surroundings?”
Coverage only. No composite score before calibration.
Two checks, 1h 55m apart. Nothing was published between them.
| Entity | First check | Second check | Change |
|---|---|---|---|
| Shokz | 50 | 51 | +1 in spread |
| Beats | 35 | 32 | -3 in spread |
| Bose | 34 | 30 | -4 in spread |
| Jabra | 23 | 20 | -3 in spread |
| Bose Ultra Open Earbuds | 12 | 16 | +4 in spread |
| Shokz OpenRun Pro 2 | 33 | 33 | 0 in spread |
Measured spread: about ±4 of 54 with nothing changed
Every move above sits inside that spread. It is noise, not a trend. The two checks used different question sets, so the spread blends engine variation with question variation. This is why DecodeIQ reports a variance band before it reports movement.
Every number traces to a stored production record.
Four steps. Dated results.
The loop has four steps.
Run a Category Scan or Product Scan to map buyer language and freeze the questions.
Run the frozen questions before publication and record the recommendation evidence.
Publish buyer-matched content to an accessible surface and record the change.
Run the exact same questions again and compare.
Generate and publish content written in buyer language, then record what changed: Product Listing · Blog Post · FAQ Section · Buying Guide · Social Proof · Listing Attack Plan.
Sourced, dated, and honest about failure.
Every scan shows its sources. Every generation shows which buyer entities it used.
Every visibility check stores its exact questions and shows per-engine status, including when an engine's live-search behavior cannot be confirmed.
Re-checks compare only identical questions on identical scope.
If a check comes back incomplete, we report no score rather than a guess.