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Noogata Launches AI Location Analytics Library for Bricks-and-Mortar Insights

The library allows organizations to define locations with attributes that relate to a specific business question.

Note: TDWI’s editors carefully choose vendor-issued press releases about new or upgraded products and services. We have edited and/or condensed this release to highlight key features but make no claims as to the accuracy of the vendor's statements.

Noogata, a specialist in no-code artificial intelligence (AI) data analytics for enterprises, has launched its location analytics library. Building on its existing e-commerce library, the location analytics library applies Noogata’s no code AI data analytics platform to physical locations for consumer packaged goods (CPG) brands.

Sales and marketing professionals can now leverage AI to gain actionable insights, including generating and scoring leads for new sales opportunities, through understanding the locations relevant for their business. Until now, sales and marketing teams struggled to enrich location data with all relevant information from hundreds of external data sources, such as demographics, economic and sales trends, and to apply the advanced analytics techniques required to gain real insights. Noogata’s location analytics library automates this entire process and generates a unique “location fingerprint” based on thousands of different features for each location.

The library allows organizations to define locations -- from bricks and mortar stores to agricultural fields -- with attributes that relate to a specific business question. Field sales managers can use these insights to find new, potential locations similar to those currently successful and target their field sales efforts there. CMOs and their teams can take insights from bricks and mortar sales and use them to optimize targeted marketing efforts online.

Noogata’s location analytics library consists of the following AI boards:

  • Lead scoring: Find and prioritize field sales opportunities for consumer goods companies
  • Retail footprint expansion: Identify optimal locations for new stores
  • Product launch analytics: Select the best locations for new product launches
  • Hyper-local demand forecasting: Identify pockets of sales growth and uplift across all channels
  • Competitive analysis: Classify competitive peer groups for each location and track sales and marketing impact.
  • Marketing analytics: Provide localized insights into demographic patterns and preferences
  • Agriculture yield optimization: Optimize operations and minimize environmental impact

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