Competitive positioning
Auraqu vs. expert networks vs. consumer-side alt-data
| Dimension | Auraqu | Expert networks GLG, Tegus, AlphaSense | Consumer-side alt-data Yipit, Second Measure |
|---|---|---|---|
| What it covers | Supply side, brand operators, factory suppliers, franchise operators | Experts with institutional knowledge (former executives, consultants) | Consumer behavior, purchases, visits, app usage |
| Cost per respondent | ~€1–2 | €50–200+ (GLG ~$1,350/hr) | N/A (passive transaction/location data) |
| Panel size | 5,270 brand partners (BPI) · 1,455 factories (H&M) | 1–5 experts per engagement | Millions of passively tracked consumers |
| Refresh cadence | Quarterly (recurring, same panel) | On-demand / one-off per engagement | Weekly or daily (continuous) |
| Output format | Structured dataset (CSV / Parquet / API) + transcript archive | Unstructured call transcript / notes | Structured indices, time series, dashboards |
| Longitudinal tracking | Yes, same panel, same questions, every quarter | No, ad hoc, different experts each time | Yes, continuous panel |
| Audit trail | Transcript ID + confidence score per field | Call recording (if enabled); compliance dashboard | Methodology documentation; no call-level provenance |
| Independence | Fully independent of measured platforms | Expert may have undisclosed conflicts; MNPI risk | Fully independent (passive behavioral data) |
| Best for | Recurring supply-side signals: channel satisfaction, allocation intent, supplier stress | Deep qualitative understanding; hypothesis generation | Consumer revenue estimation, same-store sales, DAU/MAU |
Auraqu is a complement to expert networks and consumer alt-data, not a replacement. Expert networks generate hypotheses; Auraqu tests them at scale. Consumer alt-data tracks what buyers purchased; Auraqu tracks what sellers experienced.