Being on the forefront of innovation is important to any business. So is top-notch customer service (at least it should be). To achieve these top two priorities in tandem takes insight; after all, it’s not easy to stay on the cutting edge of trends and make your customers a primary focus at the same time. Companies that are successful doing this are the ones capable of organizing big data in a useful way.
And the world is flooded with a plethora of data to organize and utilize. In fact, 2.5 quintillion bytes of data are produced every day, according to IBM. The growth has occurred exponentially so that 90 percent of the data that exists in the world today was created in the last two years alone. To glean the most from the preponderance of data related to their customers alone, business owners must be able to intelligently decrypt it and interpret it for their own purposes. So, how does one handle the onslaught of data and then turn it around into organized thoughts and ideas?
Enter big data analytics.
Big data analytics is the process of examining data to uncover patterns and trends. Businesses typically utilize analytics for a few reasons: to gain insight into which parts of their strategy or business model are effective, and to predict trends, enhance decision making and provide insight into what their customers are thinking.
The data is usually broken down by one of two methods, reactive and proactive, described below: Reactive vs. proactive analytics
- Reactive: Simply put, reactive analytics means a business analyzes the data after something has happened. This sort of analysis is extremely useful because it provides insight into a specific plan or strategy that a business tried and whether or not it worked.
- Proactive: This type of data analysis helps a business identify trends for future decision making and gain a deeper understanding of its customers. This can be done by tracking what customers like to buy, when they like to buy it and how they make their purchases. Determining these preferences allows businesses to streamline customer purchasing experiences, for example.
To further illustrate the use of big data analytics, take a look below at how businesses have used big data analytics to enhance their bottom lines.
Examples of big data successes
- Improved customer service: The great thing about big data is that there are no limits to how a business can utilize the information. For example, Morton’s Steakhouse once took advantage of big data in a social media stunt that ended up going viral. A Morton’s customer jokingly tweeted that he would like dinner delivered to Newark Airport for him to enjoy after a long day work-related travel. To humor the customer, Morton’s went the extra mile, following the man’s Twitter account to discover that he is a frequent Morton’s customer with a “usual” order including a porterhouse steak. Having tracked the man’s flight to ensure an accurate delivery at the airport, the Morton’s meal was presented to the customer by a tuxedoed waiter. The customer was delighted and will, no doubt, remain a loyal Morton’s fan.
- Improved business function: In addition to improving customer service, big data can be used to streamline workflow within a business. To illustrate, consider the following scenario, whereby an unnamed fast food restaurant is programming its drive-through cameras, according to an article in TechTarget, to trigger its drive-through display panel to offer certain items on its menu based on the length of the vehicle line. Food items that take longer to cook will be highlighted when the line is shorter, while items that can be prepared more quickly will be displayed when the line is longer. The hope is that customers will opt for the highlighted items, thus making it easier for the restaurant to efficiently manage demand.
- Predict certain trends: Utilizing big data in a predictive fashion is useful in many industries. For example, police forces are beginning to implement specific predictive software that—based on the type of crime, the place, the time and criminal behavior patterns—can predict small (500-foot) areas where crime is most likely to occur. This is an example of forward-thinking analytics at its best, helping law enforcement achieve its goals.
Big data can render some big results, but you need the right platform to achieve positive outcomes. Typically, an analytics platform will streamline the process of organizing data, as it can collect data, present it to the user and assist in organizing it so the data can be presented to higher-ups. To learn more about how utilizing an analytics platform can help improve your businesses functions, click here.