There is a ton of hype about “predictive” in the martech universe— it feels unavoidable. It seems like every other week we see another vendor in the space that has the word “predictive” in their name or tagline. The promise of predictive is an almost universal promise of more data-driven revenue growth. Predictive marketing promises revenue growth based on data within your database and beyond.
Marketers are latching on to this value proposition. In fact, 68% of respondents to EverString’s State of Predictive Marketing Report believe that predictive marketing is a key piece of the marketing stack.
However, the predictive marketing category is still yet to be defined. Its still in its infancy stage. Take for example the term itself. If you look at Google Trends for the term “predictive marketing” you’ll see it wasn’t even in existence just a few years ago.
Search Volume for Predictive Marketing According to Google Trends
The term “predictive analytics” however has been around for a few years and can be seen as a precursor to the predictive marketing trend. Ultimately, predictive marketing is a specialization of predictive analytics which has been around since 2008.
Search Volume for Predictive Analytics According to Google Trends
So what is driving all of this hype? Let’s explore the factors have facilitated the rise of predictive marketing.
The Changing Buyer and Seller Landscape
Over the past few years, the nature of the buyer/seller conversation has fundamentally changed. The buyer journey has shifted toward a a self-serve discovery and evaluation of your offerings—often times without interacting with you directly until the buyer is deep in the funnel. While this new distance causes some distress for marketing and sales teams, it also provides an extremely powerful and useful gift—buyer data. The capability to store and process massive amounts of this data has changed the game for both the buyer and the seller.
That being said, there is a ton of noise out there online—think competitor content, user reviews, and more. In other words, there are plenty of resources for your buyer to look and plenty of solutions. As a result, you, as the seller have to become multi-channel, content-driven, and personalized in their communication approach. And marketers have to be precise. You have to be very focused on generating the right kind of traffic and delivering personalized experiences that build relationships and buyer trust, and gain buyer attention.
The Data-Driven Marketer
Who is the modern marketer, and how has she changed over the past few years? Against this backdrop of information access, data gathering, and the changing buyer journey, today’s marketer has to be increasingly data-driven—ready to prove-out program return on investment and revenue attribution to the executive team.
As marketing technology continues to innovate, marketers must become increasingly more technology-savvy. They must be able to evaluate, implement, learn, and demonstrate the explicit value of all this new technology to executives and their team to keep up with the buyer landscape.
This focus on technology and revenue reporting has caused the marketer to become more data-driven than ever. The fact is, there is so much data out there that exists about the buyer, sales processes, what accelerates deals, etc, that a marketer must learn to crunch these numbers and find trends.
Look at any job posting for a marketing role and you will clearly see that being data-driven is a requirement.
Predictive marketing enables a marketer to take hold of available data and use it to make intelligent decisions that help drive the business forward. With predictive marketing, marketers can look at thousands upon thousands of historical data signals to determine the makeup of a company’s best customers.
Realizing The Potential of Applied Data Science
Before predictive marketing software emerged, a few forward thinking companies and many consulting firms had noticed the potential of using applied data science to tap into, and tie together internal data sources in order to discover net-new customer insights. This new approach helped marketers at these select organizations become more effective, and differentiated these select companies from their competitors.
In a simple sense, for a marketer who is interested in doing segmentation and personalization, the application of data science enables segmentation on tens of thousands of variables instead of just a few.
This newfound access to data was amazing. However, this new approach required massive and rare resources. These valuable insights were reserved for Fortune 500 companies who employed the top data scientists and had the technical resources to build these advanced algorithms.
Fast forward a few years and we now find whole companies that self-identify as “predictive marketing” or “predictive analytics for marketing and sales”. Some of these organizations have been around for almost a decade, but started as consulting companies. Regardless of their origin, all of the predictive companies currently in the market are pursuing one goal— to extract value for customers from a repeatable and effective use of data science.
While many claim to be pursing this goal, not all will provide real value for customers. The organizations that will provide real value are those that have the data and the data science to back up their claims, and are able to present valuable insights in a way that is accessible to the modern marketer. In this category, data and data science matters. The ability to apply the right science to the right problem is important. And ultimately, the biggest challenge of all is the delivery of this value as a service. Enabling marketers to extract value from data and science without having any sort of extensive services engagement is not easy. Few companies will come out of this space providing the power of data through accessible means.