Download PDF
Princess Polly Leverages Modern Data Stack for Enhanced Retail Analytics
Technology Category
- Functional Applications - Inventory Management Systems
Applicable Industries
- E-Commerce
- Retail
Applicable Functions
- Procurement
- Warehouse & Inventory Management
Use Cases
- Picking, Sorting & Positioning
- Time Sensitive Networking
Services
- System Integration
The Challenge
Princess Polly, an Australian fashion boutique, was facing challenges in utilizing data effectively during a time of uncertainty. The company was preparing for a critical launch into the U.S. market and needed to support internal departments in making informed decisions. Anand Bhatt, the Head of Business Analytics, was tasked with building an analytics infrastructure that could demonstrate value quickly and efficiently. As the sole member of his team, Anand needed to maximize his time generating value for the business and minimize manual, time-consuming tasks. A key area of focus was cash flow analysis, with the aim of understanding which decisions were impacting the business’ bottom line to make more effective decisions.
About The Customer
Princess Polly is an Australian fashion boutique that was founded in 2010. The company started in a beachside apartment on the Gold Coast of Australia and has since grown to a team of over 200, based both in the Gold Coast and Los Angeles. The company is a 100% ecommerce site and recently launched in the U.S. market. Anand Bhatt joined the fashion startup in May 2020 as Head of Business Analytics, shortly after the company’s U.S. launch. His role was to up-level how data was being used in the company, especially during a time of uncertainty.
The Solution
Anand decided to use Fivetran, a tool he had experience with, to build the analytics infrastructure. He required a well-established Shopify connector that could support both the company’s Australian and U.S. Shopify accounts and join the data from both accounts for analysis. He also needed a connector to pipe data from their PR system into their data warehouse, with Klaviyo support to import all NPS scores into their system. An AWS Lambda function was created for their Inventory Planner, Returns data pool, using the Fivetran connector for ingestion. After a two-week trial, Anand was able to connect the Shopify APIs into Fivetran, run successful tests, and begin a historical data sync. He also onboarded Mode Analytics to give the team access to the datasets that were beginning to populate.
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.