Marker.io integration Marker.io integration END

Location Optimization

It’s difficult to accurately represent and plan data-driven consumer journeys in the physical world.

For strategic location-based initiatives, AI analytics connects complex external data sources to pinpoint specific geographical areas with high potential for profitability and campaign effectiveness.

The Question of ‘Where’

Increasingly, location is the key to unlocking hidden, and potentially valuable, insights within data. By overlaying a combination of socio-demographic, road traffic, real estate and open-street-map data, SparkBeyond Discovery helps identify the top location-based drivers, so you can understand why things happen where they do.

Applications for Location Optimization can be found everywhere, from finding optimal locations in retail site selection and solving traffic bottlenecks, to maintaining and repairing vital infrastructure. The Discovery platform augments data on legacy and existing locations with external inputs. For example, proximity to geospatial  features as tagged by OpenStreetMap may include food chains, roadways, city/rural landmarks (mall, universities, farms, etc.), as well as the presence of competition.

Based on the selected criteria, the platform’s automated hypothesis generation identifies what drives the best outcomes, producing a list of location suggestions based on novel predictors and micro-segments within the data. The outputs can also be used as a predictive model for future location selection, adapting to changing conditions and data as needed.

The post-pandemic shift in consumer behavior requires new hyperlocal patterns of opportunity and risk. Consider an Oil & Gas conglomerate that wanted to profitably expand its network of gas stations and independent convenience stores, while improving existing station revenue. Using SparkBeyond Discovery to combine multiple data sources (vehicle traffic, station configurations, external competition), the organization identified actionable performance drivers used to create:

  • Models to predict revenue potential
  • Network expansion plan to inform long-term strategy
  • Optimization of existing station configuration parameters

Geospatial analytics offers data-driven guidance for network strategy: having the right formats in the right locations, with the right offerings, is essential. For every client for whom foot traffic is relevant – be it stores, agencies, clinics, service centers, banks – AI pinpoints the best locations to yield the most optimal customer experience and business results.

“Somehow, in just over a month, you have overtaken a decade of learning about the best store locations.”

CEO of Japan’s largest convenience store chain

Features

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Business Insights

Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis

Predictive Models

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Micro-Segments

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Features For
External Models

Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis

Business Automation
Rules

Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis

Root-Cause
Analysis

Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis

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