Predictive analytics

8 Use Cases for Predictive Analytics in Marketing

If you are a marketer interested in leveraging the immense power of customer data to enhance your strategy and drive more revenue out of your marketing campaigns, the term “predictive analytics” has probably come up before.

Whether you have yet to dip your toe in the predictive analytics pool or are already experimenting with using data to influence your marketing strategy, one thing is certain. New use cases continue to emerge with the power to revolutionize the way you market your company’s solution and move customers along the buyer’s journey.

This post walks you through eight different use cases for predictive analytics in marketing. Beginning with attracting new leads all the way through to maximizing your customer’s lifetime value, predictive analytics plays a critical role in every step of your customer’s lifecycle.

First, What Exactly Is “Predictive Analytics”?

As a general marketing definition, predictive analytics involves any activities that leverage existing customer data to make intelligent assumptions about the activity of future customers.

A successful predictive analytics strategy hinges on a few key criteria:

  1. Clean, Quality Data to Decipher

A recent post on the ReachForce blog shares specifically why predictive analytics and clean data go hand-in-hand, and is worth a read.

In short, because any predictive analytics strategy is founded on your existing customer data, a database of clean, quality customer information remains the single most critical component of a successful predictive analytics strategy.

Without robust customer data, any attempt at leveraging predictive analytics might actually prove detrimental to your business by alienating customers and missing opportunities through incorrect or invalidated assumptions.

  1. An Experienced Marketer or Data Analyst

Recognizing trends in your data requires extensive training and experience, which is why an experienced marketer or trained data analyst on staff becomes a “must-have” once your marketing team graduates from predictive analytics basics to some of the more complex use cases included in this post.

  1. The Right Tools and Solutions to Collate Data and Implement Your Strategy 

From a data management solution that collects and unifies data across all marketing channels to the right automation tools to carry out the strategy you develop based on predictive insights, having the right tools in place should be a big focus for the marketing technologist on your team.

Once you have these three components in place, how you use predictive analytics continues to expand into new, exciting opportunities for marketers.

Here, then, are eight predictive analytics use cases proven to have a measurable impact on marketing ROI.

The Impact of Predictive Analytics Starts at the Top of the Funnel

Starting with top-of-the-funnel activities makes sense as a natural kick-off point, but as you will see later on in this post, predictive analytics actually starts influencing your strategy long before a prospect even converts to a lead in your funnel.

That is the cyclical nature of predictive analytics; as leads convert to customers, the data gathered from those new customers influences the next generation of marketing activities.

Here are two ways predictive analytics impact your top-of-the-funnel activities:

#1. Improved Lead Scoring

Ask any salesperson and he or she will tell you:

No two leads are created equal. 

While that may be true for prioritization purposes, predictive analytics teaches you how to correlate the actions of your existing customers to influence your future efforts as a marketer.

That becomes most apparent when looking at the insights a good data analyst can glean from basic demographic and behavioral customer data, particularly in regards to lead scoring.

Historically, lead scoring has been a collaborative task between sales and marketing in which salespeople tell marketers, “these are the leads I want passed to me right away,” and then marketing creates a “score card” of sorts that measures the potential value of an inbound lead and determines (hopefully automatically, though some teams stuck in the stone age may still be managing this as a manual process) whether the lead is “sales-ready” or needs to enter a nurture campaign.

With the power of predictive analytics, lead scoring becomes less of an anecdotal list of criteria from sales and more of an actual data-driven view of your target customer.

When combined with a good automation tool, rules governed by predictive analytics can quickly score leads based on demographic, behavioral, and psychological data. Those scores determine whether leads are “hot” and should be immediately contacted by sales, or if they need more time in a nurture campaign before moving further down the funnel.

#2. Refined Segmentation for Nurture Campaigns

For those leads still in the early stages of the buying process, defining an appropriate plan for lead nurturing should be the natural next step.

Lead nurturing does not take a one-size-fits-all approach.

Instead, the best campaigns for moving leads toward becoming “sales-ready” use segmentation to create customized nurture tracks. Demographic and behavioral data tells you the right level and type of content to help push leads further down the sales funnel. Predictive analytics is, of course, the mechanism that makes that possible.

Predictive Analytics as a Means of Customer Segmentation

Predictive analytics

In addition to appropriately aligning leads to the right nurture campaigns, predictive analytics helps marketers segment customers in a number of key ways, including:

#3. Improved Content Distribution

Your team likely invests a good portion of your marketing budget in quality content.

This makes sense because content marketing has the ability to provide significant ROI for your company. There is nothing more frustrating than putting a bunch of money into developing content, only to find no one opens or reads it.

Often, the content itself gets the blame here when, in reality, the true culprit is an ill-defined strategy for content distribution. Predictive analytics tackles that problem head-on by analyzing the types of content that most resonate with customers of certain demographic or behavioral backgrounds, and then automatically distributing similar content to leads that mirror the same demographic or behavioral habits.

#4. Predictive Lifetime Value

You likely know that the true measure of marketing ROI is your customer’s lifetime value.

Did you know, though, that number can actually be predicted based on the same predictive analytics strategies that help you more accurately distribute content or score leads?

When you look at the historical lifetime value of current customers that match the backgrounds of new customers, you can very simply make a reasonable estimate of that new customer’s lifetime value.

#5. Propensity to Churn

Similarly, protecting your baseline becomes much easier as well when you start leveraging the power of predictive analytics. How?

By learning from past mistakes, of course. Past behavior is indicative of future behavior and nowhere is that more true than with your customers. By analyzing the behavioral patterns of previously-churned customers on your platform, a savvy marketer can identify the warning signs from current customers and either notify the sales partner responsible for managing the customer relationship, or automatically plug the candidate into a churn-prevention nurture campaign.

#6. Upsell and Cross-sell Opportunities

Finally, marketers can leverage that same customer data to also identify upsell and cross-sell opportunities. Much like the ability to forecast customer lifetime value, one can use data from customers who have added on after the initial sale to predict future customer growth.

Predictive Analytics and Data Visualization

As mentioned earlier in the post, predictive analytics is cyclical in nature; new insights feed future marketing decisions, which yield new insights that then feed the next set of decisions, and so on.

That loop back to top-of-the-funnel activities occurs in a stage known as data visualization. Here is how it can impact your marketing strategy.

#7. Determining Product Fit

Developing a scope on customer pain points and market needs becomes far easier when armed with the demographic, behavioral, and psychological data of your customers.

Figure out which features your customers leverage most and listen to your customer feedback (particularly from those who churn) to help determine where needs exist.

#8. Analyzing Optimal Campaign Channels and Content

Of course, all of this activity feeds back toward future campaign design. As new customers enter your pipeline, leverage that data to analyze the most appropriate channels, types of content, and even date and time to target specific audiences.

Conclusion

These eight strategies have the potential to transform your marketing strategy. However, do not forget that they all hinge on clean, quality data. Without that piece of the puzzle in place, predictive analytics can actually have a negative impact on your business.

The good news is, ReachForce is here to help. ReachForce helps marketers increase revenue contribution by solving some of their toughest data management problems. We understand the challenges of results-driven marketers and provide solutions to make initiatives like marketing automation, personalization, and predictive marketing better. Whether you have an acute pain to solve today or prefer to grow your capabilities over time, ReachForce can unify, clean, and enrich prospect and customer lifecycle data in your business, and do it at your own pace.

To learn more about how ReachForce can help you optimize demand generation and your impact on revenue, get a free data assessment and get a demo today.