Styling Data Pipelines for Analytics Success at Mayvenn
Company Size
11-200
Region
- America
Country
- United States
Product
- Ascend Unified Data Engineering Platform
- Amazon S3
- Amazon Redshift
- Looker
- Alooma
Tech Stack
- Python
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Digital Expertise
- Productivity Improvements
Technology Category
- Analytics & Modeling - Real Time Analytics
- Infrastructure as a Service (IaaS) - Cloud Storage Services
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Consumer Goods
- Retail
Applicable Functions
- Business Operation
- Sales & Marketing
Use Cases
- Demand Planning & Forecasting
- Predictive Quality Analytics
- Supply Chain Visibility
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
About The Customer
Mayvenn is a highly data-driven company that serves both stylists and their clients. The company's mission is to provide high-quality beauty products with an unparalleled shopping experience. To achieve this, Mayvenn uses data to empower hairstylists and salon professionals while also providing the stylists’ customers with stellar experiences. The company built a robust, data-centric platform to connect the right customers with the right stylists and the right experiences. In addition to this, the platform also powers the growing business with comprehensive data analytics pipelines.
The Challenge
Mayvenn, a company that provides high-quality beauty products and aims to connect customers with the right stylists, relies heavily on data for its operations. The company moves a variety of data, including ad and marketing spend, email, text, and customer service data, from Amazon S3 to Amazon Redshift using Python for analysis and into Looker for reporting. However, the company faced challenges with its previous data orchestration tool, Alooma, which hindered fast iteration of ETLT. The data team at Mayvenn often found themselves blocked on projects due to dependency on the engineering team, which often had a full queue.
The Solution
Mayvenn implemented the Ascend Unified Data Engineering Platform to overcome the challenges they faced with data orchestration. This platform empowered the data analysts at Mayvenn to make changes quickly and cost-effectively without relying on other teams. Within a month, the data analysts were working deeply in the Ascend platform, which accelerated their pipeline and gave them more independence. The Ascend platform also provided a visual flow that made it easy to see every step of the transformations. In addition, Ascend's customer support team was always available to respond to any questions or support requests.
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.
.png)
Case Study
Improving Vending Machine Profitability with the Internet of Things (IoT)
The vending industry is undergoing a sea change, taking advantage of new technologies to go beyond just delivering snacks to creating a new retail location. Intelligent vending machines can be found in many public locations as well as company facilities, selling different types of goods and services, including even computer accessories, gold bars, tickets, and office supplies. With increasing sophistication, they may also provide time- and location-based data pertaining to sales, inventory, and customer preferences. But at the end of the day, vending machine operators know greater profitability is driven by higher sales and lower operating costs.
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.
