Leveraging Computer Vision (AI) to Optimize the Retail and Sales Experience

In today’s increasingly competitive retail landscape, our client, a Fortune 500 retailer, recognizes the importance of leveraging technology and innovation to build a strong foundation for future success. They wanted to leverage their existing resources and available technology to optimize the customer experience and increase daily sales conversion rates. When the existing product market didn’t fit their requirements, the retailer sought out UDig to create a custom solution that could accomplish their goals.



After a successful pilot project illuminated an opportunity to increase customer conversion rates by 1-2%, the retailer sought an economically viable long-term solution that would maximize their ROI.


In order to minimize capital expenditures, UDig piloted a custom computer vision (AI) solution that leveraged the retailer’s existing loss prevention infrastructure, including cameras and on-prem servers.


The pilot proved successful in helping optimize team member productivity and creating a personalized customer experience, enabling the retailer to quickly recognize customers who needed help and efficiently deploy resources to improve conversion rates.

Challenge: Improving Business Outcomes Without a Huge Capital Investment

As one of the largest rural lifestyle retailers in the U.S. with more than 2,000 stores, they possess a strong market share in rural communities across the country. However, with increasing competition from online and other retailers, they were seeking innovative ways to better serve customers and increase conversion rates. Our client understood that these goals could be accomplished through the increased and optimized use of technology by mapping and reporting customer and sales associate activity inside the stores. 

When they tested potential solutions, they quickly identified pain points where existing products could not meet all of their requirements:

  • The technology could count people entering the stores, but could not differentiate between customers and employees.
  • The technology couldn’t capture dwell time.
  • As such, existing solutions could not provide the level of data they needed to accurately inform business decisions.

Strategy: Create a Custom Solution Using Artificial Intelligence

To create the custom solution, UDig knew it must first understand the business goals, operations, and technological assets. Given the scope of the challenge and the actions that had been taken, UDig understood the task at hand as well as the need to demonstrate what ultimate success would look like.

First Priority: Deep Dive

To begin, UDig conducted in-depth discussions with the rural retailer to comprehensively understand the company’s needs and goals of analyzing customer traffic patterns and leveraging that intel to improve customer conversion rates, provide greater support to sales associates, and improve the overall customer experience.

Integrated Approach

UDig understood that they wanted to leverage its current technology as much as possible to reduce the need for further expensive IT investments. So, UDig audited the existing IT infrastructure in order to leverage the full capabilities of current assets and proposed a design that required little incremental investment.

Second Priority: Map a Solution

Built upon our understanding of the needs, goals, and existing infrastructure, UDig created a three-phased pilot aimed at proving the success of a custom solution that leverages computer vision, a field of artificial intelligence that enables computers and systems to derive useful information from digital images, videos, and other visual inputs. Ultimately, our approach was to incorporate computer vision to provide them with the intel needed to make informed decisions, without the need to purchase cameras with integrated AI or for a third party to process the data gathered.

Final Priority: Implement, Test, and Optimize Pilot

Once the strategy was built, UDig implemented the three-phased pilot to evaluate its performance and retool as necessary. The three phases included:

  1. Testing and proving the ability to count visitors accurately using analog cameras and existing in-store server configurations, while differentiating between customers and sales associates.
  2. Monitoring dwell time of customers in key areas and creating appropriate alert messages to notify sales associates of customers likely in need of assistance.
  3. Integrating those messages into useful feed outputs aligned to alert business actions. UDig conducted extensive testing and demos, including tracking an executive’s visit to a store, to ensure alignment of data gathering with business goals and outcomes.

Outcome: A Cost-Effective Pilot to Prove Business Value

UDig’s solution is helping drive sales, operational improvements, and enhance the customer experience by aligning intel gathered through computer vision technology with staff activation—all without the need to revamp or replace the current camera and in-store IT server investments. Specifically, the custom solution UDig created is helping analyze:

  • Traffic Data: Monitoring and separating customers from sales associates to accurately gauge store traffic.
  • Register Alert: Triggering alerts to sales associates when customer lines build up at registers.
  • Dwell Detection: Triggering alerts to sales associates when customers are waiting or browsing in high-value areas of the stores (particularly portions that generate greater revenue) for a given period of time.
  • Heat Maps: Each of the previous use cases corresponds with heat maps to monitor and report on customer traffic patterns to inform staffing and other key business decisions.

Ultimately, the custom solution has not only accomplished the initial goals, but it has also provided the client’s team with resources to develop additional use cases. The solution has positioned the rural retailer for future success and enabled the company to gain a competitive edge through the use of technology. 

How We Did It

Artificial Intelligence
AI Video Analytics
Computer Vision

Tech Stack

  • API Integrations
  • Azure Data Lake
  • Kubernetes
  • On-Premise & Virtual Machine Servers
  • Python
  • YOLOv8