A Comprehensive Guide To Predictive Analytics.
By: Mike Wagaba
Tags: Digital Marketing, Ecommerce, Predictive Analytics, Marketing, Analytics

Why Predictive Analytics Is Important

By applying predictive analytics in business, risks can be significantly reduced because decisions will be made based on data, not merely unproven assumptions that rely on instincts and some educated guesses.

That said, Predictive analytics if properly implemented starts influencing your marketing strategy long before a prospect even converts to a lead in your funnel.

As leads in your funnel turn into paying customers, the data collected from those new customers influence the next generation of marketing initiatives.

Here are some possible applications:

1. High-Quality Lead Generation

Lead generation

With predictive analytics, marketers can gauge the customer’s propensity to buy with greater accuracy.

The predictive analytics model can analyze customer data to make these projections and thereby helping marketing teams pass on high quality leads to the sales teams.

Taking Harley Davidson’s example here;

A company can improve the quality of leads they generate by identifying and analyzing its high-value customers. Understanding this customer segment will help provide key insights on how the company can attract more of them and predict who will be most likely to convert into paying customers.

2. Targeted Profiling of Customers

Predictive analytics helps in mapping customer journeys and identifies how customers respond to marketing initiatives.

It gives marketers a greater understanding of how customers responded to a marketing activity, the reasons behind why they did or did not make a purchase and helps them identify how to convert a prospect into a paying customer.

The more we know about our buyers, the better we can plan our marketing initiatives.

Since a marketing database does not contain all the information that we need to deeply understand our customers, coupling predictive analytics with customer data helps us uncover the right patterns to design optimized experiences for our customers.

3. Improved Lead Scoring

With the power of predictive analytics, lead scoring becomes less of a scoring list of criteria from sales to a 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.

4. Segmentation for Nurture Campaigns

Lead nurturing is definitely one of the most crucial aspects of marketing because it’s at this stage where you are supposed to give your leads a compelling reason to become your customers.

And the fact that “Not all leads are created the same”, doesn’t make the process any easier.

This means that the content piece or offer that made one lead convert will not necessarily be enough to make another lead convert. Therefore, the best nurture campaigns have to take a more customized approach with specific tasks designed to move a specific segment of lead towards becoming paying customers.

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

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.

5. Improved Content Distribution

There is nothing more frustrating than putting a bunch of money and time into developing content, only to find no one opens or reads it and often time’s, the lack of a proper strategy for content distribution is the reason.

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.

6. Accurate Prediction of Lifetime Value

You probably already 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.

7. More Insight to Reduce 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.

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.

This is so important because time and resource investment needed to acquire a new customer is a great deal higher than to retain existing ones.

By employing predictive analytics models, marketers can recognize customers who display early signs of churning and the reason behind the same.

This allows them to proactively interact with those customers and take measurable actions to prevent customer loss.

8. Enhanced Upsell/cross-sell Opportunities

Marketers can leverage that same customer data to also identify upsell and cross-sell opportunities.

Businesses can use the available data from previous customers who have had an additional purchase on top of their initial purchase to determine the type of customers prone upsells.

9. Improved Determination of Product Fit

Equipped with historical, sales, and leads data, businesses can better understand exactly what customers’ needs and wants are, which is key to developing better future products.

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.

10. Determining the Optimal Campaign Channels and Content

Of course, all of this activity feeds back toward future campaign design.

The type of content and channels that work better for certain leads can be identified with predictive analytics.

Such insights help tailor a content distribution framework to ensure that your leads receive higher-quality communication from the business through their preferred channels and time.

This, in turn, could increase the probability of sales conversion.

11. Identify New Trends and Growth Opportunities

Predictive analytics also help in identifying new trends and growth opportunities. It gives marketers insights into industry pain points and new trends that are brewing and gives them the opportunity to tweak and model their product or service to suit the customer’s requirement.

Marketers today need smarter data analysis to drive clearer insights and build creative strategies that is why they need to build a stronger relationship between data and marketing creativity.