Kigu.me: Keeping Up with 'Dodecadoopaling' Sales with Geckoboard
Customer Company Size
SME
Region
- Pacific
Country
- Australia
Product
- Geckoboard
Tech Stack
- Data Visualization
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Revenue Growth
- Productivity Improvements
Technology Category
- Application Infrastructure & Middleware - Data Visualization
Applicable Industries
- Retail
Applicable Functions
- Sales & Marketing
Services
- Data Science Services
About The Customer
Kigu.me is Australia’s largest importer of animal onesies and authentic kigurumis. The company was founded in February 2012 by Aidan Lister and Daniel Labib, who met while studying Engineering at Monash University. The idea of bringing this fun and comfortable piece of clothing to Australia percolated until a second ski trip two years later. That year, the costumes had the same impact, and it didn’t take long before Kigu.me entered the Australian market with a bang. The company is based in Melbourne, Australia.
The Challenge
Kigu.me, Australia’s largest importer of animal onesies and authentic kigurumis, experienced a surge in demand when onesie-wearing celebrities made the comfortable piece of clothing the new ‘it’ item. The trend spread globally, and Kigu.me needed a tool to help them keep up with the growing demand. The company was experiencing a parabolic growth, with sales doubling or tripling every month. The founders found themselves constantly refreshing the orders page to keep up with the sales. They needed a better way to monitor the health of the business on a daily basis.
The Solution
Kigu.me turned to Geckoboard, a data visualization tool, to help them monitor their sales. They signed up for a trial and made a few custom widgets on Geckoboard to bring up the daily, weekly, and monthly sales. This allowed them to see the health of the business on a daily basis. By having the data visualized, they were able to understand more about their data. For example, they found out that Mondays and Tuesdays were their biggest days for sales. Knowing this, they started targeting their social media following on those days and sales increased even more.
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
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
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
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
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.
