In the past few years, coinciding with the rapid rise of AI, the field of predictive intelligence has been growing quickly. Today, predictive intelligence is not about theory — it’s about practical application in sales and marketing that solves problems and leads to better business outcomes.
So what is predictive intelligence exactly? How can it help you reach your business goals? And why is predictive intelligence one of the most promising technologies to change sales and marketing in the near future?
What Is Predictive Intelligence?
As with any new technologies, the definition of predictive intelligence varies somewhat depending on the source you’re reading. Often, it even gets bundled up together with other “predictive” terms, such as predictive analytics, predictive recommendation engines, and predictive marketing. Considered broadly, all of them essentially describe the same concept.
Predictive intelligence is the process of collecting and processing behavioral data (related to customers, leads, or visitors) from a variety of sources to accurately foresee future actions.
All the data that predictive intelligence collects is interpreted automatically, with the help of algorithmic models that can take into consideration millions of data points and arrive at data-backed and action-ready conclusions in mere seconds.
Note that most of the predictive intelligence models in the marketing space are limited by the hypothesis or model that the engineers created, instead of having the self-learning capacity of true AI.
The essence of predictive marketing is then leveraging complex mathematical models and vast amounts of data to inform marketing communications and ultimately reach your business goals.
One of the most common customer intentions that predictive intelligence can foresee is churn. Armed with this insight, you can create new marketing campaigns, reorganize your company (e.g. hire a customer relationship manager), and make changes to your offering.
Another example that predictive intelligence can solve is estimating click-through rates on advertising. Predictive intelligence is about constantly analyzing data and helping you decide on the best-performing visuals, tone of voice and medium to use in your marketing campaigns. Finally, you can personalize your communications at scale.
How Well Does Predictive Intelligence Work?
We now know what predictive intelligence does. But how effective is it at actually moving the needle?
A Forbes Insights report surveyed 308 executives in North America working for companies with at least $20 million in revenue, and found that 86% of them saw their ROI increasing due to predictive marketing initiatives.
The efficacy of predictive intelligence is two-fold. On the one hand, it works based on raw facts and data, initially removing the emotional (human) component from the equation when it comes to interpreting results.
On the other, it’s able to process much greater pools of data - both in terms of quantity but also complexity. Such analysis could never be done by a human in such a short amount of time. This leads to much faster analysis and ultimately, predictions that can inform and improve marketing initiatives.
What Is the Future of Predictive Intelligence?
Many predictive intelligence tools work in the B2B market, where they can rely on a wealth of customer data from a variety of sources and use it to come up with accurate insights, score leads, and determine buyer intent.
Every year, the models that guide predictive intelligence solutions are improving - however the biggest improvement seen so far has been from the introduction of machine learning and AI.
This differs from what we consider “predictive intelligence” in that it improves its own model in order to “learn” and reach a specified success state. This is true machine learning and not based on a hypothesis.
In order to do this, and make it work effectively, the initial machine learning model needs to be fed vast quantities of data and scenarios to guide it.
This also allows machine learning to process more subtle cues from unstructured data or seemingly “disconnected” data points that a predictive intelligence model created by a human might not see or account for.
The leaders in this market aren’t necessarily large, well-known companies like IBM or Salesforce. There are lots of smaller players — each one aggregating and specializing on data for its own niche. For example, when it comes to identifying anonymous buyer intent, there’s nothing more powerful than Lift AI.
Lift AI is the best-in-class anonymous buyer intent solution that can accurately pick out the most promising leads in the sea of your website visitors.
Relying on its proprietary machine-learning model trained on more than one billion data points and 14 million sales engagements, Lift AI is able to predict how likely every single visitor to your website is to buy your solution, even if they are completely anonymous (up to 98% of any average website traffic). Plus, it all happens in real-time.
Here’s how the Lift AI works to increase your website performance:
- Visitors land on your website
- Lift AI predicts their buyer intent (with 85% accuracy) by matching their displayed behavior to modelled behavior
- High-scoring visitors get connected directly to your sales team for conversion through online chat
- Medium and low-scoring visitors are assigned to a nurturing bot or a self-help guide
Lift AI has found that on average, 9% of website traffic has high buyer intent and is ready to try your solution. By connecting that 9% of high intent traffic to your best conversion channel possible (real-time chat with a live salesperson), you can significantly increase your website’s performance. In fact, it works so well that within 90 days most companies see their chat conversions increase by two to 10 times, since BDRs are mostly talking to people who actually want to buy.
Now you can see how predictive intelligence can improve long-established sales and marketing processes in seconds. By letting machine-learning models do what they do best, you can reap the benefits of higher conversion rates.