Download PDF
MarketShare Enhances Its Marketing Analytics Services Using H2O
Technology Category
- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Automotive
- Retail
- Telecommunications
Applicable Functions
- Sales & Marketing
Use Cases
- Predictive Quality Analytics
- Predictive Replenishment
Services
- Data Science Services
The Challenge
Cross-channel attribution, revenue optimization, and forecasting are among the biggest pain points in brand marketing today. Marketing teams need to see a complete picture of their effectiveness but are often limited to partial data in spreadsheets that take months to analyze. As a result, CMOs and analysts often throw darts in the dark when forecasting the outcome of their marketing investments - severely restricting an organization's ability to reach more customers and grow. Organizations are often buried under an avalanche of data, which is often collected on an automated basis, as a byproduct of everyday operations. The process of moving from 'insights' to 'prescriptive action,' as a result, becomes a major challenge.
About The Customer
MarketShare has been studying the challenges of marketing attribution and revenue optimization for more than 10 years. The company has released its industry-leading MarketShare DecisionCloud to help the world's biggest consumer and B2B brands connect raw data to information, knowledge, insight, and ultimately - action. Many of the world's leading enterprise brands ranging from Ford to Intel, Best Buy, Hilton, and MasterCard rely on MarketShare DecisionCloud to connect their marketing investments to revenue. One of the unique value propositions of the company is that it works across the entire marketing department, and all levels of the organization.
The Solution
At the heart of MarketShare's DecisionCloud is a process of real-time modeling. The company relies on H2O's scalable machine learning platform to model its customer data, faster. MarketShare's model development process requires multiple layers. The team uses Hive, Spark, and SQL to process the data and then builds and runs models using H2O. "We run these models on different networks to get them running as quickly as possible," said Ramachandran, SVP and Managing Director, MarketShare. "We started the journey of finding the right solution in 2010. We started off with our own implementation - which is something that a lot of smart software engineers try. After realizing that we needed to pursue another path, we evaluated a number of vendors. We found that H2O was the fastest with its in-memory solution. We found that H2O was 10x faster than anything else out there."
Operational Impact
Quantitative Benefit
Related Case Studies.
Case Study
Integral Plant Maintenance
Mercedes-Benz and his partner GAZ chose Siemens to be its maintenance partner at a new engine plant in Yaroslavl, Russia. The new plant offers a capacity to manufacture diesel engines for the Russian market, for locally produced Sprinter Classic. In addition to engines for the local market, the Yaroslavl plant will also produce spare parts. Mercedes-Benz Russia and his partner needed a service partner in order to ensure the operation of these lines in a maintenance partnership arrangement. The challenges included coordinating the entire maintenance management operation, in particular inspections, corrective and predictive maintenance activities, and the optimizing spare parts management. Siemens developed a customized maintenance solution that includes all electronic and mechanical maintenance activities (Integral Plant Maintenance).
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
