This segmentation divides your customers into 3 to 4 segments based on their geographical location.
You might need to adjust segments based on your unique customer base. For example, for an outdoor brand, the segments can be — mountain area, coast area, city area, and others.
- 🏔️ Mountain explorer
- 🏝️ Coast explorer
- 🏙️ City explorer
- 🌐 All rounder
This segmentation is a good fit for your brand if
- You cover a wide range of geographies.
- You can easily generalize and correlate certain shopper preferences with the geographic area. For example, customers located in coastal areas have very different fashion preferences than the ones located in mountain areas.
|- Easy to set up.|
- Universal across different brands.
- Additional tracking is not required.
|- Broad generalization that customers from the same region will have the same fashion interests.|
- May lead to incorrect geo tracking (VPNs).
The geo-location data comes from session events. We can divide this data into different groups using the session_start events.
- The 'City Explorer' segment aggregates the last session_start data of shoppers located in the largest cities where the customers are located.
- The 'Mountain Explorer' segment aggregates the last session_start data of shoppers located in mountain areas.
- The 'Coast Explorer' segment aggregates the last session_start data of shoppers located in coastal areas.
- The 'All rounder' segment includes the rest of the shoppers who don't fit into the other segments.
Custom tracking is not required for this segmentation.
We recommend that each segment has a size of a minimum of 100,000 daily visitors. This ensures that segments are large enough for Bloomreach Discovery to learn and for the AB tests to get statistically significant results.
Updated about 1 month ago