When a single bank tries to estimate market share using only its own customer data using either ATF or CTH instead of a fusion of both, the result is low coverage and misleading insights. CQ solves this by safely combining non‑personal, threshold‑validated data across banks — dramatically increasing visibility while staying fully within regulatory constraints. The CQ SIMULATOR uses Barcelona’s restaurant market as an example. It defaults to Sabadell, which has 14.6% market coverage in the city. The fictitious restaurant Pollovacerdo is a Sabadell customer competing against roughly 7,000 restaurants.
If Sabadell attempted to deliver CQ‑class market‑share insights on its own, Pollovacerdo would appear to hold 0.013% market share — because Sabadell can only “see” 1,022 competitors. The insight is not just incomplete; it is commercially misleading. Now select the other banks. As coverage expands to 6,078 visible competitors, Pollovacerdo’s true market share drops to 0.002%. This is the core problem: No individual bank has enough visibility to deliver accurate, governance‑grade market‑share intelligence. CQ is the control layer that enables banks to combine this information safely, consistently, and in full compliance with regulatory requirements. Only with CQ does the market picture become accurate enough for CMOs to act on — and defensible enough for CROs to trust.
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