Marketing can often be a creative endeavor. You come up with ideas, campaigns, ads, and designs that you think will resonate with your target audience and put your brand in the spotlight.
In other words, marketing involves a lot of guesswork informed by experience.
It doesn’t always work out. Have you ever invested all your creative efforts into a campaign only to see it flop, with little to no engagement or expected ROI? There’s nothing worse.
The logical desire to ensure that marketing hits all the right notes, laid the foundation for early database marketing — analyzing sets of customers and prospects to find similarities, and tailor future campaigns and offers to their needs. It was manual work at first, requiring lots of time to perform pattern-matching by hand.
Soon enough, however, marketers were flooded with massive amounts of data from purchasing history to demographics. The access to that data also became ubiquitous through various web apps. Finally, the unprecedented computing power of the cloud made it possible to improve data-processing models practically overnight.
The ultimate result of these innovations was the rise of predictive marketing. In fact, as far back as 2015, up to 91% of top marketers were already committed to implementing predictive marketing in their organizations.
Let’s explore what predictive marketing is, how you can leverage it to improve sales and marketing processes at your company, and what the future of predictive marketing looks like.
What Is Predictive Marketing?
Predictive marketing is the process of leveraging both your existing customer data as well as external information on similar audiences to forecast future behavior, trends, and outcomes.
The goal of predictive marketing is to help you make better marketing decisions, improve internal processes, and create more effective marketing strategies.
Predictive marketing is actually a derivative of predictive analytics.
Predictive analytics is an algorithmic technology that uses hypothetical projections and rules engines based on a limited number of data points. The hypothesis is human, typically a marketer's theory of what a success state looks like.
However, the human brain and rules engine technology we use to enforce the hypothesis is limited in terms of the number of permutations we can envision and the number of data points we can assess and apply through those algorithms.
Simply put, it's limited by our human brain’s capacity to compute all the variants and our human bias to optimistically express outcomes. That’s why AI and machine-learning is paving the way to a next generation of predictive analytics/marketing that can go far beyond what a person is capable of.
While predictive marketing is still young, its adoption is skyrocketing, and you can see how it has become a critical part of the marketing stack at larger companies.
How Predictive Marketing Works
Predictive marketing works at the intersection of big data and predictive analytics.
The first step to getting valuable predictive marketing suggestions is to make sure you’re continuously collecting customer data (which you can do in aggregate and with respect to privacy), from the number of interactions with your product to the ROI on your ads, etc.
The second step is having the right tools to automate and analyze your real-world data, and derive action-based recommendations out of it. The selection of such tools is growing daily, and their application is becoming more and more sophisticated as well.
A simple example of predictive marketing is seeing personalized product recommendations on ecommerce websites like Amazon or eBay. Amazon knows everything you’ve searched for and bought in the past. Based on what other people similar to you are buying, its predictive models are then able to suggest products that you’re most likely to buy as well.
Another example, from the SaaS world, would be applying predictive analytics to identify customers who have a high probability of cancelling your service. An effective predictive marketing tactic here could be automatically offering them a deeply discounted annual plan, so you can prevent churn and extend their CLV (customer lifetime value) by another year.
While traditional marketing requires you to be creative and effective from scratch, every time, predictive marketing just keeps learning and getting better, offering more refined and specific suggestions to drive sales and conversions.
The Benefits of Predictive Marketing
Predictive marketing helps improve all your marketing efforts by being able to accurately anticipate customer (or prospect) behavior and trends across all communication channels.
As a result, you can get more out of your advertising spend by creating campaigns that target specific customer segments and have a much higher conversion rate. You can redesign your website to streamline customer acquisition or increase the average order value. You forecast with greater accuracy, optimize prices, develop new products, etc.
The underlying idea of predictive marketing is being able to offer a variety of personalized and dynamic suggestions informed by your customer behavior rather than trying to sell a one-size-fits-all solution.
The Challenges of Predictive Marketing
Even though predictive marketing can solve a lot of problems for sales and marketing departments worldwide, it’s important to understand what predictive marketing is not.
Predictive marketing is not artificial intelligence. That means that most predictive analytics or predictive marketing tools don’t have the ability to examine thousands of data points in real-time, nor do they know if those data points will be informative or causal to each individual's behavior. Only AI can assess such a vast array of data points and then compare every permutation against billions of data points to train the models to predict an outcome with unprecedented accuracy.
A buyer intent model, Lift AI is a great example of this. It is an AI that leaps past what you know as predictive marketing. Lift AI leverages a proprietary machine-learning model to determine how likely it is for any visitor on your website to buy your product.
To do this kind of calculation, Lift AI needs to capture and process countless data points - both historical and real-time, to successfully identify anonymous website visitors (up to 98% of your traffic) and their buyer intent— note that this isn’t a “prediction” but rather a “prescription” - Lift AI works with over 85% accuracy under this model.
Once Lift AI knows the buying intent of your visitors, it can assign them a buyer intent score and connect them with your BDRs through chat in order of priority. High-scoring visitors can be directed to live chat right away for conversion, while lower-scoring ones can either be met with a nurturing bot or a self-help chat interface.
In fact, Lift AI has increased chat conversion rates for companies like PointClickCare, Intelex, and Nitro anywhere from two to 10 times in just 90 days.