Data analytics as a business asset is changing dramatically. That’s because companies are applying data analytics to new problems and making data a driver of innovation.
Data analytics is the science of examining raw data to drawing conclusions that help companies make better business decisions. How?
Here are 14 companies seizing data analytics as a business asset.
- AMERICAN EXPRESS: Started looking for indicators that could really predict loyalty. They developed sophisticated predictive models to analyze historical transactions and 115 variables to forecast potential churn. The company believes it can now identify 24% of accounts that will close within the next four months.
- BOEING: Used data and devices to transform the company from an aircraft manufacturer to an aero-health service provider. To reduce its airline customer’s total cost of ownership, Boeing offered customers a data-based Airplane Health Management service. Performance data can be wirelessly transmitted from each Boeing aircraft directly to the fleet operator for real-time fault management, performance monitoring and customized alerts. The data service allowed Boeing customers to make fix-or-fly decisions quickly, which in turn, helped the airline improve maintenance efficiency and reduce servicing costs.
- CITY OF BOSTON: Introduced Street Bump, an app which enables people to use their smartphone’s accelerometer—a motion detector in the device—to record road conditions and send data to public works employees. With the Street Bump app, citizens simply turns on the app and, as the drive, data is automatically collected and sent to the city. Street Bump was expected to identify the location of potholes—a top concern of Boston residents. Thanks to analytics, the early data has provided some unexpected insights: trouble spots are eight times more likely to be “castings,” those manhole covers, grates and other cast metal lids that are supposed to be flush with the roadway surface but instead heave up due to the extreme cold of a New England winter. Hundreds of these castings have been repaired as a result.
- CONAGRA: Faced the challenges of figuring out the optimal pricing for its products in an environment where consumers are hyper-sensitive, while coping with the ever fluctuating costs for 4,000 raw materials used in some 20,000 products. The company turned to in-memory computing, which loads extremely large volumes of data from multiple sources into one database and enables users to answer questions almost instantly. ConAgra decision-makers had real-time insight into the company’s costs and consumers’ demands. ConAgra also shared data-driven insights with retailers.
- EXPRESS SCRIPTS: Processed pharmaceutical claims and realized that those who most need to take their medications are also those most likely to forget to take their medications. So they created a new product: Beeping medicine caps and automated phone calls reminding patients it’s time to take the next dose.
- INFINITY PROPERTY & CASUALTY CORP.: Realized it had years of adjusters’ reports that could be analyzed and correlated to instances of fraud. It built an algorithm out of that project and used the data to reap $12 million in subrogation recoveries.
- JOHNSONVILLE SAUSAGE: On average, consumer products (CP) companies spend 13.7% of their gross sales on trade promotions, according to a recent study by AMG Strategic Advisors. Immediate insights from analytics enabled Johnsonville to track special offers, such as coupons. With real-time data, Johnsonville correlated the offers with sales performance to adjust the programs based on those insights.
- KAISER PERMANENTE Made a multi-billion dollar investment to build its HealthConnect® health information system. The system securely connects 8.6 million people to their healthcare teams, stores their personal information and provides the latest medical knowledge. In an industry known for chronic high costs and quality issues, the system allowed Kaiser to not just identify and rollout best practices but it also gives the healthcare company a data-driven edge in providing lower-cost and higher-quality care.
- MERCEDES: In the automotive industry, manufacturers must manage a greater number of both models and customization options as they adjust to fragmented consumer demand, while also managing shorter product life cycles. They used real time sensor data from the engines being tested to enable engineers to identify possible problems more quickly. That change, in turn, creates more engine-testing capacity each week and a focus for engineers of refining engine quality. The strategy worked. Mercedes AMG sold more than 32,000 automobiles in a year, their most successful year ever.
- NORWEGIAN CRUISE LINES: Knew it needed to better understand who its customers are, what they value and what they most want to do when onboard the ship. Norwegian implemented a business intelligence (BI) system aimed at delivering insights to business users. From the data, they created onboard food and entertainment packages based on common spending patterns. The data also made it possible for Norwegian to introduce all-inclusive cruising, the first of the major cruise lines to do so.
- TESCO: The supermarket chain collected 70 million refrigerator-related data points coming off its units and fed them into a dedicated data warehouse. Those data points were analyzed to keep better tabs on performance, gauge when the machines might need to be serviced and do more proactive maintenance to cut down on energy costs.
- T-MOBILE: 77% of the most successful U.S. companies use Twitter to communicate with customers and 70% use Facebook. Wireless campaign typically get 1% to 3% negative reactions but sometimes response can go to 12%. T-Mobile used social media for better listening to: 1) Identify trending issues and monitor customer sentiment, 2) Allow the tough conversations to take place, 3) Offer quick responses in real time 3) Make positive connections that deliver on the brand promise. The end result is better customer service. Customer complaints went down by 50% in 3 months because they have been able to come up questions and issues relating to how customers might respond.
- WALMART: The mega-retailer’s latest search engine for Walmart.com includes semantic data. Polaris, a platform that was designed in-house, relies on text analysis, machine learning and even synonym mining to produce relevant search results. Wal-Mart says adding semantic search improved online shoppers completing a purchase by 10% to 15%.
- ZILLOW: provided publicly available mapping of individual home values across the US by combining existing data sources such as transactional history from public records and listings from real estate brokerages with new data from Microsoft-based maps. Its media-based business model continued to expand with nearly 12 million visitors per month and an increasing array of services as Zillow mines the data on visitor actions and continuously builds its data assets.
Do these examples show you how companies are applying data analytics in new and innovative way. Does your organization need guidance using data analytics as a business asset?