The modern-day marketer has an incredible amount of data at their fingertips. But without systems in place to organize and interpret that data, the extreme amount of information isn’t as effective as it could be. Predictive and descriptive analytics can provide clarity for marketers as they design and deploy insight-driven campaigns that target new customers and improve retention rates of current customers. With a Master’s in Digital Marketing and Data Analytics (DMDA), you can build your understanding and application of descriptive and predictive analytics for the good of your business.
In this post, we’ll explain the differences between predictive analytics and descriptive analytics. We’ll cover how each type of analytics works and what it tells us. We’ll look at real-world examples of predictive and descriptive analytics in the world of marketing. And we’ll share opportunities to learn about both predictive and descriptive analytics on a deep level that translates to success in the marketplace.
Descriptive Analytics vs. Predictive Analytics
Both predictive and descriptive analytics have a range of strategic applications. While those applications overlap when it comes to marketing, they have unique roles.
Descriptive analytics reflect on things past consumer behavior such as:
- Customer purchase history
- The effectiveness of email or social media campaigns
- How webpages perform in terms of clicks, time on page, and conversions
Descriptive analytics help companies understand what’s worked, what hasn’t, and what motivates their customers. Descriptive analytics take numbers and data about what has happened in the past, helping marketers identify patterns and trends.
Netsuite points out how companies may use descriptive analytics to compare over time regarding revenue per employee, expenses as a percentage of revenue, and other performance measures. The same goes for marketing. Marketers may compare one social media platform to another to determine which performed the best for their company during the prior quarter. Alternatively, they may look at an ad’s performance broken down by regions where the ad was shown to study where it succeeded.
While descriptive analytics are used by companies to understand what has happened, predictive analytics are used by companies to determine what is likely to happen next. Supermetrics describes predictive analytics as “the process of using current and/or historical data with a combination of statistical techniques — including (but not limited to) data mining, predictive modeling, and machine learning — to assess the likelihood of a certain event happening in the future.”
They go on to share five practical examples that give a thorough picture of predictive analytics in marketing. Those examples are:
- Customer and audience segmentation (using cluster modeling)
- New customer acquisition (using identification modeling)
- Lead scoring (using propensity modeling and predictive scoring)
- Content and ad recommendations (using collaborative filtering)
- Personalizing customer experiences (using automated segmentation)
In the case of both descriptive and predictive analytics, clear questions that need answers and cohesive organization systems make all the difference. By reflecting on the information discovered through descriptive analytics, marketers can determine the right questions to ask and answers to pursue. Then, using predictive analytics, they can set out to find those answers.
Descriptive and Predictive Analytics in the Real World
The popularity of data and increased corporate desire to hire analysts are directly related to ways that analytics have made a positive difference in large and small companies. Let’s look at how both descriptive and predictive analytics have improved company performance—from storytelling to bottom lines.
When it comes to descriptive analytics, online consumer behavior is the name of the game. Google Analytics empowers online marketers to see how websites are performing, how they should segment customers according to commonalities and differences, and what the consumer base is saying on social media. Additionally, Brandwatch analyzes social media posts to understand the sentiment, identify keywords, answer questions with machine learning, and more.
With a DMDA degree from Emerson, you’ll take classes in exactly these elements through courses like “Customer Segmentation and Descriptive Analytics,” “Online Consumer Behavior,” and “Digital Marketing and Campaigns.” Students also earn Google Analytics and Brandwatch certifications that are built into the curriculum, allowing you to add standout resumé skills that can be immediately applied into a new or current role.
Here are just some of the ways that these many aspects of descriptive analytics have benefited companies’ performances.
Jewelry company Brian Gravin Diamonds wanted to improve its online sales. But how? Enter Google Analytics. The company used descriptive analytics to understand the behavior of its website visitors before they made a purchase. They realized that a large number of people were abandoning their carts, leading to lost sales. Based on that data, they set out to build a system that would encourage those who had abandoned their carts to return to the site. That adjustment led to a 60% increase in customer checkout.
Descriptive analytics empowered marketing automation company Marketo to increase their conversion rates exponentially. By analyzing the characteristics of their website visitors, Marketo created specific segments of their audience based on the products they were interested in and their demographics. From there, Digital Vidya reports, they designed new remarketing campaigns that would serve their audience with data that was a fit for them. That process improved their rate of conversion by ten times compared to “traditional display marketing.”
Social Media Strategy
Fairmont Hotels & Resorts used the descriptive analytics made available to them through Google Analytics to figure out whether their social media “buzz” was truly valuable to the company. So, they began to use a URL builder that would show them in their analytics dashboard how often someone visited their site through a Twitter link, even if the link was copied, pasted, and shared through another platform like email. This way, they could better determine how much they should put into Twitter and allocate resources elsewhere.
Predictive analytics requires a great deal of precision when it comes to executing them effectively. Asking the right questions is the key. DataPine recommends starting with questions like “what exactly do you want to find out?” and “how can you ensure data quality?” Once they have concrete answers to questions like those, marketers can leverage predictive analytics to cultivate growth in their companies. In the Emerson DMDA course “Predictive Analytics,” you will learn how to use predictive analytics to model, automate, and optimize.
Here are some of the ways that marketers have used predictive analytics to benefit their companies.
Increased Customer Loyalty
It’s highly likely that someone you know, including yourself, has watched a television show or movie because Netflix suggested it to them. Guess what powers that recommendation? Predictive analytics. Netflix uses descriptive analytics—information about what someone has already watched—to perform predictive analytics—determining what someone may want to watch. Netflix also uses analytics to make new content. Several of their smash-hit shows were created based on what they learned from analyzing millions of daily television and movie watches, ratings, and searches.
By analyzing consumer behavior that had already occurred through descriptive analytics, statisticians at Target used predictive analytics to determine when customers may become pregnant. Amazon makes recommendations for items and products based on what customers are most likely to buy. And Franklin Sports uses machine learning to make personalized product recommendations based on several predictive models. The list goes on, and the thesis is clear: predictive analytics facilitate personalized marketing efforts that increase sales.
Through predictive analytics functions like customer profiling, table turnover optimization, and studying repeat customer trends, restaurants find ways to improve their menus and design marketing campaigns that lead to greater profits. The same goes for fashion companies that are leveraging predictive analytics to forecast trends and consequently design and market apparel that capitalizes on that trend, leading to higher sales numbers. Predictive analytics are also improving sales in the home décor sector, real estate, and the automotive industry.
Become a Data-Savvy Digital Marketer
Do you have a list of questions that descriptive analytics could answer forming in your mind? Are you ready to learn more about predictive analytics in business? Would the career you desire be closer to your grasp if you had a better understanding of how predictive analytics can be used in digital marketing?
If so, the online Master of Digital Marketing and Data Analytics (DMDA) degree program at Emerson College could be your next step. Our 100% online program empowers you to log in from anywhere so that you can keep up with your personal and professional commitments while working toward your future.
Through the DMDA program, you’ll learn how to leverage descriptive and predictive analytics to create customer-centric digital campaigns, tell excellent brand stories, improve sales, and more. Certifications in key platforms are built right into the curriculum and include Google Analytics, Brandwatch, and Hootsuite. You’ll learn to navigate industry tools like SAS Studio, Student Digital Marketing Simulation Platform, and Enterprise Miner.
Lead in the digital marketing landscape with confidence, skills, and forward-thinking. Schedule an appointment with an Emerson Admission Counselor to discover how you can take the next step toward becoming a data-savvy digital marketer.