What is Data Analytics?
Each day, an estimated 2.5 quintillion bytes of data are created. The past two years alone accounted for 90% of this data creation. But what should be done with it all? This is where data analytics comes in.
Data analytics is the set of techniques used to analyze raw data (unprocessed data) in order to extract relevant information, trends, and insights. This process includes collecting data, organizing it, and storing it, then performing statistical analysis on the data. Once the information is collected, conclusions can be drawn from it, which can be used for problem-solving, business processing, decision-making, and predictions that can inform what a company’s next steps should be. This process relies on disciplines like mathematics, statistics, and computer programming.
This article will explore one of the most popular current fields within data analytics, behavioral analytics, as well as its professional applications across various industries.
What is Behavioral Analytics?
Behavioral analytics is a type of data analytics that generally pertains to studying customer behavior. It is concerned with the “how” and “what” that drive the overall “why” customers behave the way they do. These efforts extend beyond simply monitoring sales rates, and help to paint an overall picture of what customers are looking for, as well as how and when it can be given to them.
Behavioral analytics relies on data that comes from a customer’s engagement with a business, such as from mobile apps, websites, billing systems, and marketing automation systems. By monitoring such metrics as email sign-ups, page views, and registrations, important insights can be extracted that allow Data Analysts to devise measures specifically geared toward engagement, conversion, and retention. Once this behavioral data is collected, it is stored in the form of an “event,” such as a site visit. This information is then used to provide information about customers’ actions, in particular their history and pattern of online purchasing. When optimized fully, behavioral analytics provides an effective way of helping a business target its marketing and sales efforts to reach more customers, sell additional products and services, and ultimately increase revenue.
Industries & Fields that use Behavioral Analytics
The field of behavioral analytics has a wide range of applications, from social media to ecommerce to gaming. It has the potential to provide valuable insights that can drive business decisions. The following are just a few industry-specific uses of this field:
- Product management: By collecting and analyzing behavioral data, it’s possible for product managers to increase engagement and reduce churn.
- Customer service: Despite best efforts, not all marketing campaigns are successful. Even after forecasting customer needs, it’s possible for a business to miss the mark. Many users voice concerns about unsuccessful marketing strategies on outlets like online chat, email, or social media. Customer service teams play a crucial role in not just receiving and analyzing this information, but in applying behavioral analytic measures that are designed to offer helpful and specific responses to customers, as well as to impart their sentiments to the marketing and sales teams so that improvements can be made.
- Marketing: Behavioral analytics helps marketers with many of their daily tasks. It is a powerful tool that can be used to increase the scope and effect of campaigns or improve customer acquisition rates. When marketing teams implement behavioral analytics, they can collect valuable information pertaining to the effectiveness of campaigns, as well as monitor the success of efforts like emails for increasing revenue and customer engagement.
- FinTech: Behavioral analytics draws from machine learning tools and techniques to help automate decision-making at a business. Doing so speeds up the process of analyzing large amounts of data. Applying machine learning to behavioral analytics can be used in security programs to help spot suspicious behavior, as well as to flag fraudulent activity early on.
- Sales: Once a marketing team applies behavioral data to execute or improve on a campaign, the sales team then is able to offer return on investment (ROI) information pertaining to the campaign to further strengthen it.
Benefits of Using Behavioral Analytics
Working with behavioral analytics provides many benefits. Here are just a few of the ways behavioral analytics can help a business or organization:
- It helps a business understand its customers. The main reason businesses collect customer data is to have a better understanding of their customers. By having a more robust knowledge of who customers are and what they want, it’s easier to offer them more targeted services and goods.
- It provides a way to anticipate the needs of customers. Not only are businesses concerned with the needs of current customers, but they also must devote resources to anticipating the needs of future customers as well. Doing so allows businesses to offer customers services or products they may not have even known they wanted.
- It speeds up innovation. By processing and analyzing behavioral data, it’s possible to unearth customer patterns and trends that would not have been apparent to the naked eye. These insights allow businesses to suggest additional offers to customers.
- It increases efficiency. Customizing email and website content to better meet the needs of customers is a powerful way of reaching a targeted audience that is likely to buy a given product. This allows business owners to better manage time, resources, and employee efforts.
Drawbacks of Using Behavioral Analytics
Along with the benefits of using behavioral analytics, there are a few drawbacks to be aware of as well:
- Privacy: Products such as in-home voice devices that are designed to collect behavioral analytics information in an offline format are seen by some as intrusive, since they supply personal information to other parties, such as data companies. However, when purchasing these items, users de facto agree to the terms of use, whether or not they fully understand them.
- Technical concerns: When processing huge stores of data, it’s important not only to have processing capabilities but also technological skills. IT infrastructures containing processors and large databases are often necessary to do so.
- Cost: It can be expensive to store data. Data storage costs can be a deterrent factor for some businesses, especially those with relatively small budgets.
Get Started Learning Data Analytics with Hands-On Classes
Are you interested in learning more about data analytics? If so, Noble Desktop’s data analytics classes are a great starting point. Courses are currently available in topics such as Excel, Python, and data analytics, among others skills necessary for analyzing data. For students who are particularly interested in the intersection between data analytics and social media, Noble’s Social Media Marketing Certificate provides 30 hours of hands-on instruction about cutting-edge marketing strategies.
Those who are committed to learning in an intensive educational environment may also consider enrolling in a data analytics or data science bootcamp. These rigorous courses are taught by industry experts and provide timely instruction on how to handle large sets of data. Over 90 bootcamp options are available for beginners, intermediate, and advanced students looking to master skills and topics like data analytics, data visualization, data science, and Python, among others.
For those searching for a data analytics class nearby, Noble’s data analytics Classes Near Me tool provides an easy way to locate and browse the 400 or so data analytics classes currently offered in the in-person and live online formats. Course lengths vary from three hours to 36 weeks and cost $119-$27,500.