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In-store traffic prediction with Machine Learning

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“Forecasting our store traffic closer to real count serves as a critical element in extending our store operations productivity and minimizing our costs.”

-Large Furniture Retailer


The client is a fast growing home furnishings retail chain with 20+ stores across the US. In today’s increasingly competitive retail industry, they are committed to making shopping experience easy and convenient, by delivering an unmatched selection, guaranteed low prices, and same day furniture delivery.

Why We Chose Pluto7

The client selected Google Cloud and Pluto7 to help optimize their store operations by dramatically improving their in-store traffic forecast accuracy. With the right blend of Google Cloud solutions and Pluto7’s expertise, this enterprise successfully leveraged ML, resulting in significant operational efficiencies and increasing profitability. With over 20 stores spread across multiple states in the US, this furniture retailer relies on a unique customer service model to achieve key growth targets and exceptional customer loyalty. This approach demands flexible staffing across stores, which relies on accurate traffic forecasts.


The key to the client’s growth is their differentiated customer service model at scale. To do this, they need accurate in-store foot traffic predictions so they can allocate their resources effectively, stock their individual stores appropriately, and ensure high levels of operational efficiency. To do this efficiently at scale there is a wide range of data sources that are needed. Historical data provides insights into customer behaviors such as past orders and traffic patterns across the year. Marketing campaigns and how they correlate to store traffic is another helpful comparison. In a multi-store environment, regional differences bring complexities but also result in a wider data set.

This is a perfect problem for the Google Cloud Platform to resolve – to handle complexities with data storage, scale of analytics and associated machine learning and artificial intelligence. Identify, collect, analyze and validate relevant internal and external data sets. Leverage ML and AI on the selected datasets and produce a highly accurate 4 week (28 day) forecast of foot traffic for each individual store location. Utilize the results to optimize staff and stock planning to maximize each store’s operating budget and launch effective marketing promotions.


Our customer now accesses highly accurate 28-day forecasts, enabling them to plan more effectively. They have since experienced significant operational efficiency and productivity gains, while continuing to delight customers with their unique and differentiated model for customer service. Given the results achieved, they’re now accelerating business transformation by embedding Google Cloud’s exponential technologies across multiple use cases and business functions.

Enable Decision Intelligence Into Every Corner Of Your Product And Operations.

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Industry Retail

Platform Demand-ml


  • In-store traffic forecasts are currently developed manually by one person, managed on spreadsheets, and require a month of preparation.
  • The process is time-consuming, challenging to scale, cumbersome to update, and unresponsive to real-time business changes
  • As a result, stores can be over or under staffed, resulting in a serious impact on customer service productivity and unnecessary overhead costs.


  • Forecast accuracy increased to +84% from previous max of ~65%
  • Improved operational productivity and customer service by scheduling staff more aligned with realistic demand (store foot traffic)
  • Reduced costs, optimized staff utilization and superior customer satisfaction

Products Used

  • Google Cloud Platform
  • Google Cloud Dataprep
  • Google Cloud Dataflow
  • Google BigQuery
  • Google Cloud Storage
  • Google Machine Learning
  • Google Tensorflow