Marketing can often be a creative path to customer engagement. You work with both external and internal marketing teams, come up with breakthrough ideas, marketing campaigns, ads, and designs that you think will resonate with your target audience and bring you valuable marketing outcomes, such as brand image and brand recognition.
In other words, most marketing actions involve 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 customer engagement and no marketing goals achieved? There’s nothing worse.
The logical desire to ensure that marketing efforts hit all the right notes laid the foundation for early data-driven marketing — analyzing sets of customers and prospects to find similarities, and tailor future marketing 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 third-party data, from purchasing history to demographics. Data access has also become democratized through various cost-effective marketing automation platforms online. Finally, the unprecedented computing power of the cloud made it possible to improve predictive technologies for data-driven marketing practically overnight.
The ultimate result of these innovations was the rise of predictive marketing solutions. In fact, as far back as 2015, up to 91% of top marketers were already committed to implementing predictive marketing strategies in their organizations.
Let’s explore what predictive marketing is, how you can leverage it to improve sales performance, customer experience, and marketing processes at your company, and what the future of predictive marketing software could look like.
What Is Predictive Marketing for B2B Consumers?
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 solutions is to help you make better marketing decisions, improve customer service, and create more effective marketing strategies.
Predictive marketing is actually a derivative of other predictive technologies, such as predictive analytics.
Predictive analytics is an algorithmic model that uses hypothetical projections and rules engines based on a limited number of data points. The hypothesis it’s working on 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, predictive analytics is limited by our brain’s capacity to compute all the variants and our human bias to optimistically express marketing outcomes. That’s why AI and machine-learning is paving the way to a next generation of predictive marketing solutions that can go far beyond what any 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 strategy at larger companies.
How Predictive Marketing Helps Find Leads
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, to email marketing actions, 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 predictive solutions 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 able to suggest products that you’re most likely to buy as well. Such marketing intelligence often improves customer experience and results in better sales performance.
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 data-driven marketing approach 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 for Customers
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 email marketing campaigns, for example, that target specific customer segments and have a much higher conversion rate. You can redesign your website to streamline your customer experience or increase the average order value. You can forecast with greater accuracy, optimize prices, develop new products, and work on other predictive marketing strategies that were not accessible to you before.
The underlying idea of predictive marketing automation 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 with Predictive Marketing Content
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 software doesn’t have the ability to examine thousands of data points in real-time, nor does it know if those data points will be informative or causal to how consumers actually behave.
Only AI can assess billions of data points and then compare every permutation to train predictive models to detect favorable marketing outcomes with unprecedented accuracy.
Lift AI is a great example of this as a buyer intent model. It is 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.