Big data is changing the competitive landscape of the market. Companies that can make full use of big data analysis can often provide products and services to the market faster and better maintain consistency with customer needs and aspirations. In 20 14, a survey by research institute Gartner found that 73% of the enterprises surveyed have invested in big data or plan to invest in big data projects in the next 24 months; 20 13 years, accounting for 64%. Respondents believe that improving customer experience and process efficiency is the top priority.
The improvement of customer experience is taking place online or offline, and data is collected from smartphones, mobile applications, POS systems and e-commerce websites. With more kinds of data and information that enterprises can collect and analyze than ever before, what relevant work enterprises are doing now and why they should do it need to be quantified. Moreover, this is the most flexible way to adjust your business strategy to increase or maintain market share. In the process of implementation, the improvement of customer experience is helpful to improve customer loyalty and the growth of enterprise income. On the other hand, if companies choose to ignore relevant data, they are likely to lose customers and transactions and hand them over to competitors who are more agile and savvy in data analysis.
Business process improvement continues to focus on improving efficiency, saving costs and improving the quality of products or services. Big data can provide deeper insight than traditional systems because it has more data points and data source analysis support.
Whether the goal of an enterprise is to promote income growth, speed up the listing of products and services, optimize the labor force or achieve other operational improvements, its core is to become more proactive and reduce passive reactions, which means that predictive analysis is needed to shorten the learning curve.
There are many ways to use big data to enhance and improve business operations. The following are six typical cases.
Shorten time to market
Launching a new product or service involves multiple life cycle stages, some of which are easier to accelerate than others. In the past few decades, drug manufacturers have used clinical trials to simulate learning speed, reduce costs and reduce unnecessary burden on patients participating in trials. With cloud computing and big data, the simulation of clinical trials can become more beneficial to manufacturers and patients.
Bristol-Myers Squibb reduced the simulation time of clinical trials by 98% by extending its internal hosting grid environment to AWS cloud. The company further optimized the dosage level, making the drug products safer and requiring fewer patients' blood samples in clinical trials.
Because clinical trials are highly sensitive to data, Bristol-Myers Squibb established a special and encrypted VPN tunnel to link Amazon gateway, and configured a virtual private cloud to isolate its operating environment from public customers.
Before entering the cloud, scientists used the internal environment, so it took 60 hours to run about hundreds of projects. Now every scientist has a special environment, and 2000 projects can be processed in about 1.2 hours without affecting other members of the team.
After migrating to AWS cloud, Bristol-Myers Squibb was able to reduce the number of clinical trial subjects in pediatric research from 60 to 40, and also shortened the study and research time by more than one year.
Optimize labor force
The human resources departments of some enterprises are using talent analysis and big data to reduce costs, and then effectively manage human resources-related issues. Big data helps them to effectively select new employees who are more adaptable to the enterprise, reduce the employee turnover rate, understand the skills and the output of the existing market labor force, and determine the talents needed for the company to move forward.
Xerox used big data to reduce the turnover rate of its call center by 20%. To this end, we must understand the reasons that lead to employee turnover and determine how to improve their engagement.
Improve financial performance
The financial department of the enterprise not only carried out regular reports and BI work, but also began to use big data to reduce risks and costs, and looked for opportunities to improve the accuracy of forecasting. Specifically, they use data to identify high-risk customers and suppliers to stop fraud, identify revenue loss, and explore new or more effective business models.
Recently, the cooperation between weather company and IBM will enable enterprise users to better manage the impact of weather conditions on enterprise performance. According to the data of the meteorological company, the weather in the United States alone will have an economic impact worth $500 billion every year.
These meteorological data come from more than 6,543,800 meteorological sensors and airplanes, as well as millions of smartphones, buildings and vehicles on the road. These data, combined with other data sources of 2.2 billion unique forecast points, make more than 654.38+000 billion real-time weather forecasts every day on average. For example, retailers can use this data to adjust staffing and supply chain strategies. Energy companies will be able to use these weather data to improve supply and forecast demand. Insurance companies will be able to warn their policyholders of bad weather conditions, so that they can reduce the possibility of car damage in hail weather.
Intelligent sales
A slight modification of an enterprise's sales and marketing strategy may have a far-reaching impact on your enterprise's sales performance, especially after the planned modification of big data analysis.
Imagine that the coupon rate of a six-week direct mail marketing campaign is over 70%. According to the Direct Selling Association, the average return rate of direct mail is only 3.7%. How did Kroger, a grocery chain, do it? On the one hand, they use personalized direct mail according to customers' personal shopping history.
Krogh's customer membership card program was rated as the first in the food industry. More than 90% customers use membership cards to buy products. Although there are other factors that make krogh's financial performance so impressive, its continuous growth for 45 consecutive quarters is at least partly attributed to its customer loyalty programs.
Minimize equipment and asset failures.
Enterprises want to avoid unnecessary business interruption and customer anxiety. Now, sensors have been embedded in all equipment, and enterprises can use these data information to determine when electrical equipment such as airplanes, trains and automobiles need maintenance. Ideally, when a problem occurs, it is best for an enterprise to have a professional maintenance team to understand the cause of the problem and how to solve it.
Pratt company. Whitney, a subsidiary of United Technologies Corp, is trying to reduce unplanned aircraft engine maintenance. According to Airinsight.com, today's engines can collect about 65,438+000 parameters from multiple snapshots during flight. In contrast, the new generation engine can collect 5000 parameters about continuous flight. In this process, about 2gb of data will be generated. Using these data information, Pratt &; Whitney and its partner IBM are able to carry out proactive maintenance.
Take advantage of customers' lifetime value.
Today's authorized customers are more demanding and fickle than ever before. In order to maintain or increase market share, enterprises need to know their customers as much as possible, constantly improve their products and services, and be willing to adjust their business models to reflect their actual needs.
AvisBudget, an American car rental company, has been working on this. They have increased their market share and gained hundreds of millions of dollars in additional income by implementing the integration strategy. Actively participate in determining customer value segmentation, provide layered incentives, and improve customer loyalty. CSC, the IT partner of the company, used the model to predict the lifetime value of AvisBudget customer database, and verified its multi-channel marketing activities and corresponding analysis.
The current customer evaluation data is combined with other data, including customer's lease history, service problems, demographic data of service area, company relationship and customer feedback. Avis Budget also collects and analyzes social media data. The company has a team of social media experts specializing in brand marketing. The company also recently updated its website to further improve the customer experience, and they are using big data to predict regional fleet layout and pricing service demand.
The above are the contents of six real business cases about big data utilization shared by Bian Xiao. For more information, you can pay attention to more dry goods sharing of global ivy.