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Categories: Predictive,

Predictive analytics plays a critical role in every step of your customer’s lifecycle. This post explains:

  • What predictive analytics means
  • Which criteria lead to a successful predictive analytics strategy
  • Why predictive analytics is important
  • Eight use cases for predictive analytics in your marketing strategy

What Is Predictive Analytics?

Predictive analytics involves any activities that leverage existing customer data to make intelligent assumptions about the activity of future customers.

Elements of a Strong Predictive Analytics Strategy

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

  1. Clean, high-quality data to decipher
  2. An experienced marketer or data analyst
  3. The right tools and solutions to collate data and implement your strategy

Let’s look at each of these more closely.

1. A Foundation of Clean, Quality Data

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.

2. An Experienced Marketer or Data Analyst to Interpret Data

Recognizing trends in your data requires extensive training and experience, which is why an experienced marketer or trained data analyst or scientist 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.

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

From a customer data platform 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.

Why Predictive Analytics Is Important

Predictive analytics starts influencing your strategy long before a prospect even converts to a lead in your funnel.

Marble maze showing entry, conversion and exit.

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 some use cases for predictive analytics in various stages of your marketing funnel.

Predictive Analytics in Top-of-the-Funnel Activities

Predictive analytics has two specific use cases in your top-of-the-funnel activities

Use Case #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 predictive 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.

Use Case #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.

Predictive Analytics for Customer Segmentation

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

Use Case #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.

Use Case #4. Accurate Prediction of 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.

Use Case #5. More Insight into 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.

Use Case #6. Enhanced Upsell and Cross-sell Opportunities

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 for Better Data Visualization

Remember that 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.

Use Case #7. Improved Determination of 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.

Use Case #8. Better Analysis of 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.

Download the Ultimate Guide to Driving Revenue with a B2B Customer Data Platform.

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