Bringing Location and Predictive Sales Analytics to Mexican Retailers
公司规模
11-200
地区
- America
国家
- Mexico
产品
- Revela
- Omen
- CARTO Engine
技术栈
- Big Data
- Machine Learning
- CARTO.js
实施规模
- Enterprise-wide Deployment
影响指标
- Customer Satisfaction
- Productivity Improvements
技术
- 分析与建模 - 大数据分析
- 分析与建模 - 机器学习
- 应用基础设施与中间件 - 数据交换与集成
适用行业
- 零售
适用功能
- 商业运营
- 销售与市场营销
服务
- 数据科学服务
- 系统集成
关于客户
Descifra is a global Location Analytics provider based in Mexico City. Since 2012, they have worked with Retail, Real Estate, and Public Sector clients to inject the power of Location Intelligence into their decision-making. The Descifra team brings together cutting-edge Big Data analysis and Machine Learning techniques to provide unique insights around site planning and sales prediction with their Revela and Omen products. Having delivered 20,000 reports to more than 125 companies, Descifra believes in bringing the power of location, demographic, and socioeconomic data to customers all over the world.
挑战
Descifra, a global Location Analytics provider based in Mexico City, aimed to disrupt the site planning and sales prediction process with their product, Omen. The product brings together a large and diverse range of datasets, making it crucial to have an intuitive and slick interface for decision-makers to extract insights. The visualization tool needed to handle millions of data points, often in real-time, without compromising on speed, security, and geographical granularity. The challenge was to forget traditional 150-page PDF reports and put cutting-edge Location Intelligence visualization in the hands of their clients.
解决方案
When building Omen, the Descifra product team decided to use CARTO Engine as their OEM, powering their solution with Engine’s one-stop shop of geospatial tools, services, and APIs. By selecting Engine, Descifra was able to develop their app with CARTO.js - a simple unified JavaScript library which interacts with Engine. This allowed them to connect to stored visualizations, create new visualizations, add custom interactions, and access or query raw data from a web browser - all as part of their Omen solution. In turn, Descifra was able to construct datasets fitted to standardized layers of digital cartography - differentiating themselves from more traditional Business Intelligence solutions, moving towards their creation of an intuitive Location Intelligence tool.
运营影响
数量效益
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
相关案例.
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.
