Simplifying Retail Restocking
In the fast-paced retail environment, accurate and efficient restocking processes are critical. Anthor developed a restocking app to streamline retailer operations, relying on AI-powered image recognition to manage product replenishment.
The Challenge
Anthor's retail restocking app quickly showed great potential. However, several issues emerged:
Complicated and inefficient image capture processes: Users found the existing workflow for capturing and uploading images cumbersome, leading to errors and reduced productivity.
High reliance on costly AI technology: The app relied extensively on advanced AI algorithms for image recognition, significantly driving up operational expenses.
Increasing expenses as user base grew: The expanding number of users amplified costs related to AI processing, storage, and server resources, impacting scalability and profitability.
These challenges highlighted the need for a simpler, more cost-effective approach.
Example of prototypes for user testing.
The Solution
We worked closely with Customer Success and Engineering teams to streamline and optimize our solution:
Optimized Image Capture Workflows: Redesigned user interactions for capturing images, simplifying the process and reducing user errors.
Structured Templates for Data Collection: Introduced consistent, structured data entry templates to replace the need for complex AI recognition, greatly enhancing accuracy and efficiency.
Lightweight Image Processing Techniques: Implemented efficient image compression and processing methods, reducing the bandwidth and server load.
Backend Optimizations: Enhanced mobile app synchronization with server-side databases, optimizing data handling and improving response times.
Continuous Validation: Conducted extensive A/B testing and user interviews, allowing rapid iteration based on real-time user feedback to ensure continuous improvement.
Results
Reduced Data Collection Time: Users experienced substantial time and effort savings during the data capture process.
Improved User Adoption: The intuitive redesign boosted user satisfaction and increased overall adoption rates.
Cost Savings: Decreased dependency on costly AI image recognition technology and image compression solutions, resulting in significant financial savings.
