Download PDF
Delivering high-quality end user experiences with workforce support
Technology Category
- Analytics & Modeling - Machine Learning
- Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries
- Retail
Applicable Functions
- Business Operation
- Quality Assurance
Use Cases
- Process Control & Optimization
- Remote Collaboration
Services
- Data Science Services
- System Integration
The Challenge
Ibotta faced a significant challenge in managing the receipt moderation process, which required matching items on a receipt with those selected in the app. The process needed to be both fast and accurate to ensure a high-quality user experience. While the company's proprietary OCR system handled most receipts, a substantial number still required human verification. During peak times, such as holiday shopping seasons, the workload became overwhelming, even requiring the CEO to assist in moderation. Ibotta needed a solution that could meet or exceed current performance benchmarks, improve accuracy by 10-15%, and increase efficiency by nearly 50%.
About The Customer
Ibotta is a cash-back shopping app that allows users to earn rebates by purchasing products from various retail partners and submitting a photo of their receipt through the app. Launched in 2012, Ibotta has over 35 million downloads and is one of the most frequently used shopping apps in the United States. The app is popular among large consumer brands for showcasing their products and providing value to retail consumers. Ibotta's success hinges on its ability to quickly and accurately process receipts to ensure a seamless user experience and maximize value for its partners.
The Solution
CloudFactory emerged as the leading solution provider, offering a professionally managed workforce combined with technology that provides a unified workspace, dedicated messaging channels, collaboration tools, and worker engagement tracking. Ibotta ran a pilot program to test CloudFactory's capabilities, which performed exceptionally well. Subsequently, Ibotta transitioned its final-stage moderation to CloudFactory. CloudFactory's ability to monitor worker engagement and gather productivity metrics allowed workers to quickly get up to speed. Today, CloudFactory handles over 12,000 hours of work per month for Ibotta, enabling the company to focus on improving its product and growing its business. The partnership has also expanded to include workers in Nepal and Kenya, providing high-quality execution while making a positive impact in developing nations.
Operational Impact
Quantitative Benefit
Related Case Studies.
Case Study
Improving Production Line Efficiency with Ethernet Micro RTU Controller
Moxa was asked to provide a connectivity solution for one of the world's leading cosmetics companies. This multinational corporation, with retail presence in 130 countries, 23 global braches, and over 66,000 employees, sought to improve the efficiency of their production process by migrating from manual monitoring to an automatic productivity monitoring system. The production line was being monitored by ABB Real-TPI, a factory information system that offers data collection and analysis to improve plant efficiency. Due to software limitations, the customer needed an OPC server and a corresponding I/O solution to collect data from additional sensor devices for the Real-TPI system. The goal is to enable the factory information system to more thoroughly collect data from every corner of the production line. This will improve its ability to measure Overall Equipment Effectiveness (OEE) and translate into increased production efficiencies. System Requirements • Instant status updates while still consuming minimal bandwidth to relieve strain on limited factory networks • Interoperable with ABB Real-TPI • Small form factor appropriate for deployment where space is scarce • Remote software management and configuration to simplify operations
Case Study
How Sirqul’s IoT Platform is Crafting Carrefour’s New In-Store Experiences
Carrefour Taiwan’s goal is to be completely digital by end of 2018. Out-dated manual methods for analysis and assumptions limited Carrefour’s ability to change the customer experience and were void of real-time decision-making capabilities. Rather than relying solely on sales data, assumptions, and disparate systems, Carrefour Taiwan’s CEO led an initiative to find a connected IoT solution that could give the team the ability to make real-time changes and more informed decisions. Prior to implementing, Carrefour struggled to address their conversion rates and did not have the proper insights into the customer decision-making process nor how to make an immediate impact without losing customer confidence.
Case Study
Digital Retail Security Solutions
Sennco wanted to help its retail customers increase sales and profits by developing an innovative alarm system as opposed to conventional connected alarms that are permanently tethered to display products. These traditional security systems were cumbersome and intrusive to the customer shopping experience. Additionally, they provided no useful data or analytics.
Case Study
Ensures Cold Milk in Your Supermarket
As of 2014, AK-Centralen has over 1,500 Danish supermarkets equipped, and utilizes 16 operators, and is open 24 hours a day, 365 days a year. AK-Centralen needed the ability to monitor the cooling alarms from around the country, 24 hours a day, 365 days a year. Each and every time the door to a milk cooler or a freezer does not close properly, an alarm goes off on a computer screen in a control building in southwestern Odense. This type of alarm will go off approximately 140,000 times per year, equating to roughly 400 alarms in a 24-hour period. Should an alarm go off, then there is only a limited amount of time to act before dairy products or frozen pizza must be disposed of, and this type of waste can quickly start to cost a supermarket a great deal of money.
Case Study
Supermarket Energy Savings
The client had previously deployed a one-meter-per-store monitoring program. Given the manner in which energy consumption changes with external temperature, hour of the day, day of week and month of year, a single meter solution lacked the ability to detect the difference between a true problem and a changing store environment. Most importantly, a single meter solution could never identify root cause of energy consumption changes. This approach never reduced the number of truck-rolls or man-hours required to find and resolve issues.