For years, marketers have tried to predict future campaign outcomes by looking at historical data from previous campaigns. Drawing inferences from past performance, however, is at best a slippery slope. Even the most intuitive marketing specialists cannot expect to get future predictions right all the time.
Complicating the issue is the proliferation of big data. With the advent of inbound methodologies, the capability of measuring every aspect of campaigns means that organizations are practically swimming in a sea of data. In this environment, making sense of the available data is more complex than ever before.
Fortunately, machine learning has now entered the mix, enabling marketers to use predictive analytics to not only make sense of existing data but also to make some fairly sophisticated predictions about what is to come.
What Is Predictive Analytics?
Predictive analytics leverages machine learning, statistical algorithms, and data sets integrated from multiple channels to not only reveal what has happened in the past but identify probable future outcomes based on current and historical data.
Predictive analytic tools help marketers identify patterns and trends and extrapolate the future implications of those patterns. In other words, predictive analytics works as a sort of crystal ball for B2B marketers.
That is not to say, of course, that predictive analytic tools make marketers omniscient. Rather, predictive analytics provides marketers with another tool for identifying customer behaviors and adjusting campaigns to align more closely with customer interest and intent.
Goals for Predictive Analytics Use
Marketers can use predictive analytics in some ways. In various surveys and studies, marketers identify a variety of goals they hope to achieve via predictive analytics, including:
• Improved customer acquisition
• Improved insight into customer behaviors
• Improved measurement of campaign effectiveness
• Increased customer lifetime value
• Improved customer retention
What Can You Do with Predictive Analytics?
How can all these goals be achieved by using predictive analytics? Consider six common ways that marketing technologists are using predictive analytics now.
Predictive Modeling: Predictive modeling generally can be categorized in one of three ways.
• Cluster models help you to segment your customers, based on any number of variables by which you want to categorize them.
• Propensity models intuit future customer behaviors, including predictions about factors such as customer lifetime value, the likelihood of engagement, or the tendency of leads to convert, unsubscribe, or churn.
• Collaborative filters can be used to make product recommendations based on previous behaviors or other variables. Perhaps the most commonly recognized use of collaborative filters is Amazon’s “customers also viewed” functionality, which is designed to create opportunities to upsell and cross-sell.
Lead Scoring: Predictive analytics also enables better lead scoring. With algorithms and machine learning to help you predict the potential of a lead to convert, predictive analytics helps you prioritize which leads are sales-ready and identify which leads will need continual nurturing.
In this way, you can more accurately segment your audience and provide the appropriate nurturing content for wherever your lead is in the sales funnel. Thus, you save time and money by targeting your marketing message appropriately, with the result that conversions increase while marketing spend decreases.
Identification of High-Value Accounts: Similarly, predictive analytics helps you identify which of your accounts are of high value. Armed with this knowledge, you can pursue an account based marketing strategy, which has been proven to be more cost-effective for businesses of every size.
Product Development: Predictive analytics also helps you align your product development with customer preferences. A higher level of responsiveness to customer needs can differentiate you from your competitors. Thus, predictive analytics gives you a true competitive edge in the realm of product development and product-specific marketing.
Omnichannel Strategy: Predictive analytics also informs your marketing strategy by helping you to identify the right channels to distribute your content. For instance, predictive analytics can help determine customer trends regarding social media usage, the level of acceptance of email marketing, preferences toward online and offline messaging, and so on. With the insights you gain from predictive analytics, you can more effectively direct your messages to the right audience via the right channel.
Customer Experience: Perhaps one of the most important ways you can use predictive analytics is for improving customer experience. Understanding your customers more fully via predictive analytics means that you will be able to find areas in which you can optimize your product and your marketing message to truly engage your customers at every stage of your sales funnel. Improved customer experience leads to improved conversion rates and, ultimately, to improved customer lifetime value.
The Bottom Line
Predictive analytics represents a new and improved tool for marketing. Through machine learning and statistical algorithms, you can achieve deeper insight into customer behavioral patterns, which in turn leads to better campaigns, better conversion rates, and better ROI for your marketing strategies. That, by any standard, is the outcome you would expect from this brave new world of marketing technology.
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