AI-driven Pricing: Do we need perfect data?
- FutureUP
- 2 minutes ago
- 2 min read
Most people think yes — reality says otherwise 👇
Many teams believe they need perfect data before using AI in pricing (or anywhere else). More data helps — but waiting for perfection delays results.
You can start now and still create impact. Here’s where AI fails due to data — and where it shines. 👇
❌ Areas where AI is unlikely to work
➡️ Limited accessibility: Data is scattered across systems (ERP, CRM, etc.) with restricted or no access to key information.
➡️ Corrupted data: Data has been deleted or destroyed (partially or fully).
➡️ Accuracy or consistency issues: For example, your CRM isn’t updated by salespeople, product codes differ by market with no mapping, or sales units change unpredictably (e.g., bundles, solutions, etc.) without traceability.
➡️ Missing critical data: Essential factors such as competitive intelligence are unavailable — and you consider them indispensable for meaningful insights.
➡️ Relying solely on general-purpose AI: These models typically require very large volumes of high-quality data to compensate for their lower domain specificity.
✔️ Areas where AI can be effective
➡️ Scope is narrow and data is usable: Despite challenges, data for specific products or markets is accessible, or can be cleaned or updated relatively easily.
➡️ Directional, not precise, insights are acceptable: Data gaps exist, but the dataset is sufficient for directional guidance — for instance, identifying increase/decrease price zones or broad customer segments.
➡️ Missing data lacks predictive power: If model accuracy remains strong without certain inputs (e.g., competitor intel), that information may already be indirectly reflected in existing data.
➡️ Missing data can be approximated: You can estimate missing elements (e.g., competitor prices from sales feedback, or a market “climate” index) and validate model accuracy statistically.
➡️ AI is combined with econometric or domain intelligence: Blending AI with econometrics or domain know-how can offset lower data quality through stronger modeling sophistication and extrapolation of missing info.
👉 In summary: The more sophisticated your modeling approach (e.g., integrating econometrics or industry expertise) and the narrower your initial focus (e.g., specific products or markets, or directional insights), the greater your chances of achieving strong results!
That’s why it’s best to start small, test, learn, and scale — refining your approach as the data and insights evolve.
Interested in learning more about AI-Powered Price Optimization and Strategic Forecasting?