--- title: How accurate is GeoAI in predicting future sales or revenue? description: GeoAI Targomo accuracy in predicting future sales and factors impacting the GeoAI model accuracy. --- [Skip to content](https://info.targomo.com/targomoloop/help/how-accurate-is-targomo-geoai#main-content) ![Targomo-logo-email-logo-template.png\]](https://info.targomo.com/hs-fs/hubfs/Targomo-logo-email-logo-template.png?height=40&name=Targomo-logo-email-logo-template.png) - [Sample About Page](https://www.targomo.com/en/about/) - [Sample Contact Page](https://www.targomo.com/en/contact/) - [Default HubSpot Blog](https://info.targomo.com/blog) Open main navigation Close main navigation - [Sample About Page](https://www.targomo.com/en/about/) - [Sample Contact Page](https://www.targomo.com/en/contact/) - [Default HubSpot Blog](https://info.targomo.com/blog) How can we help you? - There are no suggestions because the search field is empty. 1. [TargomoLOOP Knowledge Base](https://info.targomo.com/targomoloop/help?hsLang=en) 2. [FAQ](https://info.targomo.com/targomoloop/help/faq?hsLang=en) 3. [GeoAI](https://info.targomo.com/targomoloop/help/faq?hsLang=en#geoai) # How accurate is GeoAI in predicting future sales or revenue? GEO-AI typically predicts location performance (e.g., revenue) with up to **90% accuracy**. ### How is this measured? - When building the model, a portion of your real store data is **kept hidden** - The model makes predictions for these locations - Predictions are then compared with actual results πŸ‘‰ This is called **out-of-sample validation** πŸ‘‰ It ensures the model is tested on data it has **never seen before** --- #### **What does up to 90% accuracy mean?** - Predictions are **very close to real-world performance** - The model captures the **major drivers of success** - It’s reliable for **decision-making and comparisons** ⚠️ Important: - GEO-AI is not meant to predict **exact revenue down to the euro** - It is designed to **rank locations and estimate potential reliably** --- #### **What influences GEO-AI accuracy?** Accuracy depends on the quality and completeness of inputs. ###### 1. Quality of your store data - Clean, consistent revenue data β†’ higher accuracy - Missing or inconsistent data β†’ weaker predictions πŸ‘‰ Good data in = Good prediction --- ###### 2. Number of locations - More stores β†’ better learning - Fewer stores β†’ limited patterns πŸ‘‰ Larger networks = stronger models --- ###### 3. Data coverage in the region - Rich data (footfall, demographics, POIs) β†’ better predictions - Sparse or outdated data β†’ lower confidence --- ###### 4. Consistency of your business model - Standardized stores β†’ easier to predict - Mixed formats (flagship vs small stores) β†’ more complexity --- ###### 5. Market dynamics - Stable markets β†’ higher accuracy - Rapid changes (new competitors, construction, trends) β†’ harder to predict --- ###### 6. Competitor data completeness - Complete competitor dataset β†’ better modeling of demand split - Missing competitors β†’ overestimated potential --- #### **When is GEO-AI most accurate?** - Mature networks (20+ locations) - Consistent store formats - Good historical performance data - Data-rich urban environments --- #### **When to be cautious** - Entering **new markets with no past data** - Very **unique or experimental store concepts** - Locations heavily influenced by **temporary factors** --- #### **Key takeaway** GEO-AI is **highly accurate for decision-making**, especially for: - Comparing locations - Ranking opportunities - Estimating potential πŸ‘‰ Its strength is not perfect prediction, but **reliable direction and confidence in decisions**. 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