A Comprehensive Guide To Predictive Analytics.

All you need to know about predictive analytics and how to get it started for your business.

Every single business needs the same thing: getting clients. Reach an audience, the right one, at the right time and sell.

No tricks on that, except there are.

With the current and ever-growing competition, how business deal with the volumes of data they collect seems to be the reason why some of the fastest growing companies have managed to stay ahead.

Data is now more valuable than ever before and not for its sake, but for the insights about a company’s products/services and its customers that it can provide.

Given the prescriptive power of data, marketers are now utilizing it to not only analyze the past trends but also to predict the future behavior of their customers and unlock new opportunities.

Businesses are increasingly looking at predictive analytics to understand how they can engage better with their customers by extracting information from the sea of data at their disposal and predict behavior patterns and unlock new trends.

In the B2B industry, Predictive Analytics Technology Report forecasted, 36.8% of high-growth companies planned to invest in predictive analytics initiatives to drive successful marketing and sales initiatives

A Forbes Insights report that surveyed 306 execs from companies discovered that 86% of companies that have been carrying out predictive marketing initiatives for at least 2 years witnessed “increased return on investment as a result of their predictive marketing”.

In this article, we’ll discuss what predictive analytics is, why businesses need it, what should be measured, and how to implement it for optimized business decisions.


Article content:

  1. Definition
  2. Why now?
  3. Examples of how predictive analytics is being today.
  4. Why is predictive analytics important to your business?
  5. How to create a predictive analytics model, step by step.
  6. What determines the success of a model?
  7. Conclusion

Definition

HBR definition by Tom Davenport

In the context of marketing, predictive analytics involves the application of statistical analysis, algorithms, and analytical queries to structured and unstructured data sets in order to create predictive models.

Simply put, with the availability of big data and artificial intelligence it is now possible to calculate how likely a specific outcome is.

Predictive analytics based on all available customer data and historical actions can help your business accomplish this.

Why Now?

The adoption of digital channels, the rise of social media and e-commerce, upsurge in the usage of mobile, wearables and other devices have contributed to an even greater explosion of data.

While this seems great for marketers, they are, at the same time, faced with the challenge of dealing with this huge volume of both structured and unstructured data and design marketing initiatives based on events and triggers that are generated in real time.

Big data Analysis

It’s because of these challenges that most businesses struggle to understand the benefits of predictive analytics and yet, a survey by Forrester revealed that about 87% of B2B market leaders already use predictive analytics as part of their marketing stack in order to increase their market share and revenue growth.


Here are some cases where predictive analytics has been utilized.

Amazon uses Predictive Marketing to recommends products and services to users based on their past behavior. Some say that recommendations are responsible for as much as 30% of Amazon’s sales.

Macy’s saw the benefits of predictive analytics that resulted from better targeting of registered users of the website. Within 3 months, Macy’s saw an 8 to 12 percent increase in online sales by combining browsing behavior within product categories and sending targeted emails for each customer segment.

Harley Davidson relies on predictive analytics to target potential customers, generate leads and close sales. They identify potential high-value customers ready to make a purchase. A sales representative then contacts the customers directly and walk them through the sales process to find the perfect motorcycle.

StitchFix is another retailer that has a unique sales model that asks users to take a style survey then uses predictive analytics to match customers with the clothes they might like. If the customer does not like the clothes they receive, they can return them for free return shipping.

Churn prediction has had the biggest uptake especially for SaaS and e-Commerce business, here is an actual scenario:

Sprint now uses an AI-powered algorithm to identify the customers at risk of churn and proactively provide personalized retention offers. AI predicts what customers want and gives them the offer when they are most at risk of leaving the company. Since then, Sprint’s churn rate has dropped dramatically, and customers have given the company great scores on its personalized service and targeted offers.


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.


The most beautiful stories are those in which we find a piece of ourselves. Did you?

Overall, predictive analytics allows you to make a marketing campaign and other business decisions in a more informed manner.

Right from nurturing leads to converting them into customers, from marketing the right ideas and products to the right customers to enhance their engagement with your brands, from maximizing mutual value gains to minimizing bounce and churn — predictive analytics models can help you completely transform every aspect of digital marketing.

The good news is, Humanlytics is here to help.

Humanlytics helps marketers increase revenue contribution by solving some of their toughest marketing data analytics problems.

Free consultancy call about how you can use predictive analytics in business.

We understand the challenges of results-driven marketers and have developed a platform and custom data analytics models (Predictive models inclusive) to help you generate the necessary insights about any aspect of your marketing to ensure that you make to the right decision and achieve your growth goals.

Our Dataslinger tool is geared towards helping you bypass all three of those analytics hurdles and make analytics at your business effortless.

We currently support integration with Google Analytics, Facebook Ads, and Google Ads, and if you are integrated, don’t hesitate to reach out to us at mike@humanlytics.co, or visit our website at www.humanlytics.co for more information about what our product can do for your business.

In the next part of this guide, I’ll be showing you how you can also create a predictive model for your business.

See you then.