Big Data: 6 Real-Life Business Cases
Better data analysis enables companies to optimize everything in the value chain -- from sales to order delivery, to optimal store hours. Here are six examples of how major enterprises are using data to improve their business models.
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Big data is changing the competitive landscape. Those who are in a position to take advantage of it often get to market faster with products and services that are better aligned with customer needs and desires. A 2014 Gartner survey found that 73% of respondents had invested in -- or planned to invest in -- big data in the next 24 months, up from 64% in 2013. Improving customer experience and process efficiencies are the top two priorities cited by respondents.
Customer experience improvements are happening online and offline, with data being collected from smartphones, mobile apps, POS systems, and e-commerce sites. With the ability to collect and analyze more data, and more types of data, than ever before, businesses are in an unprecedented position to quantify what works, what doesn't work, and why. And, the ones that are the most agile are adjusting their business strategies as necessary to increase or maintain marketshare. When executed well, customer experience improvements can help boost customer loyalty and revenue growth. On the other hand, if a company chooses to disregard what the data is indicating, it may well lose customers and deals to a more agile, data-savvy competitor.
Process improvements continue to focus on efficiency gains, cost savings, and product or service quality. Here, big data can provide more and deeper insights than traditional systems because there are more data points and data sources to analyze.
Whether a company is trying to drive revenue, speed time to market, optimize its workforce, or realize other operational improvements, the goal is to become more proactive and less reactive, which means using predictive analytics to shorten the learning curve.
There are many ways to improve operations using big data. Here are six of them.
Introducing new products or services involves many life cycle stages, some of which are easier to accelerate than others. For the past couple of decades, drug manufacturers have been using clinical trial simulations to speed learning, reduce costs, and limit unnecessary burdens on patients participating in the trials. Armed with the power of the cloud and big data, the clinical trial simulations can be done faster in ways that benefit the manufacturer and patients.
Bristol-Myers Squibb reduced the time it takes to run clinical trial simulations by 98% by extending its internally hosted grid environment into the AWS Cloud. The company has also been able to optimize dosing levels, make drugs safer, and require fewer blood samples from clinical trial patients.
Because clinical trial data is highly sensitive, Bristol-Myers Squibb built a dedicated, encrypted VPN tunnel to the Amazon gateway and configured a virtual private cloud so the environments would be isolated from public customers.
Before moving into the cloud, scientists were using a shared internal environment, so it took 60 hours to run hundreds of jobs. Now that each scientist has a dedicated environment, 2,000 jobs can be processed in 1.2 hours without causing an impact to other members of the team.
As a result of the move, Bristol-Myers Squibb was able to reduce the number of clinical trial subjects in a pediatric study from 60 to 40, while shortening the length of the study by more than a year.
Some HR departments are using talent analytics and big data to reduce costs and effectively manage workforce-related issues. The data allows them to select new hires that are a better fit for the company, reduce employee turnover, understand the skills and output of the existing workforce, and determine the talent the company needs moving forward.
Xerox used big data to reduce the attrition rate in its call centers by 20%. To do that, it had to understand what was causing the turnover, and determine ways to improve employee engagement.
Corporate finance departments are moving beyond periodic reporting and BI, using big data to reduce risks and costs, identify opportunities, and improve the accuracy of forecasts. Specifically, they're using data to identify risky customers, monitor suppliers, thwart fraud, pinpoint revenue leaks, and inform new or more efficient business models.
A recent partnership between The Weather Company and IBM will allow companies to better manage the impact of weather on business performance. According to The Weather Company, weather has an economic impact of half a trillion dollars annually in the US alone.
The weather data is being collected from more than 100,000 weather sensors and aircraft, as well as millions of smartphones, buildings, and moving vehicles. That data is combined with data from other sources to yield 2.2 billion unique forecast points, and an average of more than 10 billion forecasts on an active weather day. Retailers will be able use the data to adjust staffing and supply chain strategies. Energy companies will be able to improve supply and demand forecasting. Insurance companies will be able to warn policy holders of severe weather conditions, so they can minimize the possibility of car damage in the event of a hail storm, for example.
Slight modifications to sales and marketing strategies can have a profound effect on the bottom line, especially when informed by big data.
Imagine a direct mail campaign with a coupon return rate of more 70% within six weeks of the mailing. According to the Direct Marketing Association, the average direct mail return rate is 3.7%. How does grocery store chain Kroger do it? For one thing, it personalizes its direct mailer based on the shopping history of the individual customer.
Kroger also has a loyalty card program that is rated No. 1 in the grocery industry. More than 90% of its customers use its loyalty card when they purchase products. Although there are many factors that have collectively enabled Kroger's financial performance, at least part of its continued growth over 45 consecutive quarters has been attributed to its customer loyalty programs.
Businesses want to avoid unnecessary disruption and customer angst. Now that sensors are being embedded into just about everything, companies are using the data to determine when maintenance is required for planes, trains, automobiles, and even household appliances. Ideally, when an issue has arisen, companies want to understand the issue, what caused it, and how it can be resolved, preferably before a maintenance professional or crew is dispatched.
Pratt & Whitney, a unit of United Technologies Corp., is attempting to reduce unplanned aircraft engine maintenance. According to AirInsight.com, today's engines collect about 100 parameters in multiple snapshots while a plane is in flight. By comparison, a new-generation engine is able to collect about 5,000 parameters continuously in flight. The process generates about 2 petabytes of data. Using the data, Pratt & Whitney and its partner IBM are trying to enable proactive maintenance.
Today's empowered customers are more demanding and fickle than ever. Maintaining or increasing marketshare requires businesses to understand as much as possible about their customers, continually improve their products and services, and be willing to adapt their business models to reflect the actual needs of their customers.
Avis Budget has committed to doing all of this. It implemented an integrated strategy to increase marketshare, which has yielded hundreds of millions of dollars in additional revenue. The initiative involved determining the value of customers, segmenting them, and offering tiered incentives to improve customer loyalty. To do this, its IT partner CSC applied a model that predicts lifetime value to Avis Budget's customer database, and then validated it using a multichannel marketing campaign and accompanying analytics.
The customer valuation data is now combined with other data, including rental history, service issues, demographics, corporate affiliation, and customer feedback. Avis Budget is also collecting and analyzing social media data. It has a team of social media specialists who respond to brand mentions. The company recently updated its website to further improve customer experiences, and it is using big data to forecast regional demand for fleet placements and pricing.
Today's empowered customers are more demanding and fickle than ever. Maintaining or increasing marketshare requires businesses to understand as much as possible about their customers, continually improve their products and services, and be willing to adapt their business models to reflect the actual needs of their customers.
Avis Budget has committed to doing all of this. It implemented an integrated strategy to increase marketshare, which has yielded hundreds of millions of dollars in additional revenue. The initiative involved determining the value of customers, segmenting them, and offering tiered incentives to improve customer loyalty. To do this, its IT partner CSC applied a model that predicts lifetime value to Avis Budget's customer database, and then validated it using a multichannel marketing campaign and accompanying analytics.
The customer valuation data is now combined with other data, including rental history, service issues, demographics, corporate affiliation, and customer feedback. Avis Budget is also collecting and analyzing social media data. It has a team of social media specialists who respond to brand mentions. The company recently updated its website to further improve customer experiences, and it is using big data to forecast regional demand for fleet placements and pricing.
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