The way that businesses operate has changed drastically within the past decade. In the past, business decisions were often made by testing ideas, measuring results, and making the proper adjustments afterwards. However, because of all of the data and information readily available, businesses are beginning to realize that the entire decision making process has become much easier. The revolution of big data is allowing businesses to gather and analyze data more quickly and efficiently than ever. The results are better decision making, stronger marketing campaigns, and a better experience for consumers.
The ability to make informed decisions is one of the biggest benefits companies are getting from big data. Executives want as much information as possible before making decisions. By having valuable insight into a customerâs needs, spending habits, and desires, companies can launch more personalized advertising campaigns which translate to higher conversions. Predictive analytics makes this possible.
Itâs impossible for a company to reach into the mind of every one of their customers to learn more about them, but big data and predictive analytics can offer similar results. Funneling valuable information such as purchase history, location, age, social media activity, and other data into a database such as the MongoDB NoSQL database allows companies to easily get the answers to the questions they would ask if they interviewed all of their customers.
Where is all this data coming from?
In order to make these informed decisions, companies need to know how and where to source data. Data used for predictive analytics comes from a variety of sources. Much of the information is public record, but there are also search engines, social media sites, and the information that companies gather on their own through surveys, their own website-´s analytics, social media interaction and other methods.
However, simply gathering this data is not enough. In order for the information to be of any value, it has to be defined, sorted, and analyzed to make predictions. When it comes to big data, the real question is not âhow to store itâ, but âwhat to do with it?â
The ability to not only analyze large datasets, but also to build them more quickly is helping companies immensely according to the analytics consulting firm New Vantage Partners. The founder of the company, Paul Barth, stated that big data analytics decreases the âtime to answerâ. If a retail chain wants to know how long an item will be in production or other questions that require a look into the future, data models can be built in a matter of hours to find the answer. So as you can see, the scope of use for predictive analytics goes beyond just marketing.
Using big data to increase sales
According to a survey conducted by CSO Insights of over 1,200 sales executives worldwide, in 2012, less than two thirds of sales representatives met their quotas and fewer than 57% of companies managed to reach their targets. Predictive analytics could be the key to increasing those numbers in 2013.
Predictive analytics and big data can help marketing departments generate better leads, which according to CSOâs survey is one of the major contributing factors of low sales numbers. Their survey revealed that 68% of respondents were struggling to get an adequate amount of leads from marketing efforts. With deeper analytics, sales reps and marketing departments can get more information about customers before ever contacting them and get better qualified leads.
The combination of big data and predictive analytics is powerful. CIOâs and marketing departments are really grasping this concept and using it to their advantage.
This review is listed under Data & Information Management Community