Over the past few years, more industries have begun to adopt predictive analytics to inform decision-making, optimize efficiency, and get ahead of the competition. Perhaps most recently, the marketing industry has started to explore the many ways in which predictive analytics could revolutionize the field by leveraging data to better anticipate customer needs, personalize campaigns, and optimize marketing strategies.
If you're in the marketing field or aspire to break into this industry, understanding the applications, benefits, challenges, and emerging trends that will shape the future of predictive analytics marketing could help you gain a competitive advantage in this dynamic field.
What Is Predictive Analytics?
In simplest terms, predictive analytics refers to a data analysis technique that uses technology (including artificial intelligence, statistical modeling, and machine learning) to predict or forecast what might happen based on raw data.
Predictive analytics is already used across a number of industries to help businesses make informed decisions based on the likelihood of something happening (or not happening). The banking industry, for example, uses predictive analytics to determine the likelihood of an applicant defaulting on a loan, which can then inform application decisions based on risk and other factors.
Through predictive analytics, businesses across a variety of industries can work to make more informed decisions while improving the efficiency of workflows and mitigating risk in the process.
Key Components of Predictive Analytics
There are three key components of predictive analytics:
- Data Collection and Preparation – This refers to the process of gathering relevant data and information from different sources. From there, data may need to be prepared through methods like data cleaning, scrubbing, formatting, and structuring to prepare the data for statistical modeling.
- Statistical Models – Once data is ready for modeling, experts need to determine which statistical model or framework will be used to process the data based on goals and other factors. Different types of statistical models may include linear regression, logistic regression, inferential, and descriptive.
- Machine Learning – Today, machine learning is being used more often in predictive analytics to help determine the likelihood of future outcomes based on algorithms that can identify patterns that may be difficult to pinpoint with the naked eye.
How Is Predictive Analytics Used in Marketing?
Although predictive analytics has been used in other industries for many years, it's still a relatively new concept in the world of marketing. However, as more marketing teams begin to see the benefits of predictive analytics marketing, it's likely that this trend will continue to take off. With this in mind, marketing professionals who want to remain relevant and competitive in their field should be prepared to apply predictive analytics in their own marketing strategies.
Fortunately, there are plenty of ways to go about this.
Personalized Marketing Campaigns
For starters, some marketing teams are beginning to experiment with using predictive marketing to create more personalized and tailored campaigns. More specifically, predictive models and machine learning can be used to better determine and predict customer preferences and behaviors. This can empower marketers to customize campaigns based on a potential customer's specific preferences and interests.
When marketing is more personalized, it's statistically more likely to be effective. In fact, according to a 2021 report, "companies that grow faster drive 40% more of their revenue from personalization than their slower-growing counterparts."
Using predictive analytics, marketing teams can offer personalized product recommendations to target customers, remind customers about abandoned shopping carts, and even explore dynamic pricing options to optimize sales.
Customer Segmentation
In addition to being used for campaign personalization, predictive analytics can also be useful in marketing as a tool for customer segmentation. Customer segmentation is a common strategy used by marketers to divide potential customers into different groups based on shared interests, demographics, behavior, or other factors that they have in common. From there, businesses enjoy the opportunity to tailor their marketing to those segmented groups accordingly.
Where does predictive analytics come into play? In simple terms, it can be used by marketing teams to pinpoint which customers are most likely to respond to certain campaigns based on historical data and predictive modeling. From there, marketing professionals can optimize their customer segmentation campaigns.
Demand Forecasting
Finally, marketing professionals familiar with predictive analytics can use it to forecast demand and sales, especially when ample historical sales data is available. Using past sales data, economic indicators, and other market research data, marketing teams can apply statistical models and machine learning algorithms to predict how much inventory the company will go through at any given time.
This can help marketers better plan their campaigns. Marketers, for instance, may use demand forecasting to predict the ideal time to launch a campaign for a particular product based on forecasted changes in demand.
Benefits of Predictive Analytics in Marketing
By following best practices for using predictive analytics in marketing, your team benefits in several ways.
Improved ROI
It's no secret that marketing campaigns can be costly, with some of them requiring significant up-front investments. Businesses can use predictive analytics to optimize their return on investment (ROI) for each campaign. For example, when campaigns are more personalized to the consumer's needs (which can be done with help from predictive analytics), this can lead to higher conversion rates and a greater ROI overall. With so many different predictive analytics strategies and techniques to draw upon, the potential to make individual marketing campaigns more successful is high.
Enhanced Customer Loyalty
Through predictive analytics models, brands can build a greater sense of customer loyalty over time. This can be done by using predictive analytics to deliver more personalized campaigns that resonate with customers. When customers feel as though a brand's marketing is being tailored specifically to their interests, they're more likely to develop loyalty to that brand. Of course, with greater brand loyalty also comes the potential for increased sales and revenue.
Faster Decision-Making
Making informed decisions in marketing can be a challenge because it requires an in-depth understanding of your target audience, their behavior, their pain points, and so many other dynamic factors. Using predictive analytics and modeling can take a lot of the "guesswork" out of creating a marketing campaign, leading to faster and more informed decision-making. This is especially true in situations where predictive analytics and modeling can be used by marketing teams to forecast and predict potential outcomes based on different decisions (or even entirely different campaigns).
Future Trends in Predictive Analytics for Marketing
With so much changing day-to-day in the world of predictive analytics, only time will tell what the future holds for this exciting and relatively new data analysis method. Still, there are some potential trends that we're already beginning to see emerge in the world of predictive analytics and that could be especially useful for marketing teams.
AI-Powered Predictive Tools
As artificial intelligence (AI) technology continues to advance, some exciting new applications for it have begun to come out. Specifically, AI-powered predictive marketing tools have started to be released by major platforms like Google, Jasper, and HubSpot.
These tools all have different features and may work in different ways. In general, they all use AI technology to analyze different types of customer data to predict the likelihood of future behavior. When marketers learn how to use these tools effectively, they can use them to build more successful campaigns. For instance, a marketing professional may use an AI-powered predictive marketing tool to predict potential customer behavior and even create more personalized campaigns.
Meanwhile, many of these tools provide real-time monitoring, allowing marketing teams to track campaign performance and adjust as needed to maximize ROI.
Personalized Customer Experience
Advanced predictive modeling and AI tools are now also being used to personalize the customer experience by marketing teams, which can lead to increased sales and a better ROI on individual marketing campaigns. Precisely, using AI and predictive marketing tools, teams can adjust their email marketing, social media marketing, and even website content based on a potential customer's unique demographics, behavior, and other factors.
In some cases, this same data is being used to make personalized product recommendations or to offer product pairings. Meanwhile, AI chatbots on websites can provide round-the-clock and personalized support to customers when they need it most. All of these technologies culminate to provide customers (and potential customers) with a level of personalization and dedication not possible before the age of predictive analytics.
Integration With IoT
In the coming months and years, we may also begin to see the increased use of predictive analytics with data from Internet of Things (IoT) devices. Marketing teams may soon be able to use data collected from these devices (such as wearable devices, smart speakers, and other internet-connected products) to identify new marketing opportunities and future trends. This unique application of IoT technology in marketing is still relatively new, but it could be extremely effective in further tailoring campaigns and helping brands make the most of their marketing dollars.
Get Started With Your Marketing Analytics Degree
With so many potential applications in the marketing industry, predictive analytics is likely to be one of the most in-demand skills for marketing professionals who want to set themselves apart and shape the future of marketing strategies. With so many potential applications, predictive analytics marketing is set to become a must-have skill for modern marketers.
Suppose you already have an undergraduate degree (in marketing or a related field) under your belt and are ready to advance your education in marketing analytics. In that case, it may be time to pursue your master's degree. A Master of Science in Marketing Analytics from Champlain College Online can be a great way to build upon foundational principles of strategic marketing with a mastery of cutting-edge data science tools and methodologies.
Plus, because this program is offered in an exclusively online format, you can enjoy the flexibility and versatility you need as a busy marketing professional. Learn more about this program by getting in touch today, or get the ball rolling with your online application for admission right now.
