GoSpotCheck: Retail Store Inventory Analysis Using Machine Learning
How Machine Learning Can Benefit Retail Store Inventory Analysis
The retail industry is poised for a significant disruption. Retailers that will stay ahead and thrive are those that are able to rethink their strategies and leverage modern technologies like Machine Learning.
GoSpotCheck’s software platform empowers field teams to collect merchandising, sales, and compliance data, in addition to completing tasks in the field. Their web and mobile apps enable businesses gather real-time retail intelligence allowing managers to spot trends and make better business decisions.
GoSpotCheck’s customers typically undertake a labor intensive process to provide the latest information on store inventory, sales figures, and other key performance metrics. The amount of manual tasks involved creates potential for delays and inaccurate information.
Google Cloud Implementation
SpringML worked with GoSpotCheck to build Machine Learning models that accurately identify products down to the UPC code by analyzing images of shelves in retail stores. This model was built based on the image data provided by GoSpotCheck.
- TensorFlow was used to build the models and Google Cloud Platform was used as the underlying platform.
- Results can be integrated seamlessly within their mobile app so that users get real time visibility of items in a store.
- GCP provides an environment where ML models can be deployed in a serverless and fully managed stack that responds in real time with high availability.
Reduced time to insights via automated item detection helps customers extract store inventory and sales figures faster. They can now spend time analyzing these numbers in real-time and make decisions and take action.