Artificial Intelligence and Data-Driven Marketing

There has been a lot of noise around artificial intelligence (AI) this past year, and how it’s being used in marketing and sales. True AI, as defined by Forrester, is the ability for a machine to think and act like a human. We aren’t there quite yet. But two significant AI building blocks – machine learning and predictive analytics – are technologies that you can put to use right now.

Predictive analytics aggregates data from many sources, analyzes it, and allows you to predict what will happen based on what the data is indicating. This informs your marketing strategy, and allows you to deliver highly customized programs. Machine learning processes the feedback from each of your programs and learns from them, honing in on what works, and letting go of what doesn’t.

Put AI-Assisted Technology to Work for You

Here are ways in which AI-assisted technology can help your business:
Smarter decisions. Machine learning enables marketing teams to make sense of massive amounts of data from a multitude of sources. It then filters that data in real time with an algorithm and then uses those datasets to inform customer engagement tactics – from display advertisements to direct mail to inside sales calls and beyond. Each round of marketing outreach becomes stronger than the last as the machine makes smarter and smarter predictions based on feedback it ingests each time customers engage – or don’t – with the marketing tactics delivered to them.
Speed. The intent data  you’re able to derive from predictive analytics is constantly streaming, but it doesn’t age well. When interest is high, research spikes. Marketing needs to take action immediately, but that requires the marketing response to become programmatic so that you can react immediately to spikes in intent. AI-assisted technology not only analyzes data faster, but also continuously processes information as it arrives. Additionally, the self-learning aspect of AI ensures that insights are applied almost instantly to campaigns and interactions. For example, if a prospect moves from one buying stage to another, she will automatically receive content associated with her most current buying stage.

Continuous performance improvement. Predictive analytics extracts data from various sources such as ad exchanges, web trackers, and third party data providers, and uses it to predict trends and buying patterns.  Machine learning can use algorithms to learn and improve through automatic feedback.  These two technologies work well together, and in fact, make each other smarter.  Here is a great example of how this can work:  imagine you have one algorithm that makes forgeries of Picasso’s paintings, and another that tries to detect them.  As you pit these two algorithms against one another, the outputs of each iteration feeds into the inputs on the other side.  Both algorithms become smarter and predictions become continuously more accurate.

Transform the customer experience: It’s the age of the customer, and if you want to come out ahead, your marketing activities need to be customer-led, insight-driven, and fast. You need to know what prospects are interested in, where they are in the buyer journey, and then craft your message accordingly. Today’s market requires us to be more deliberate about how we drive customer acquisition. With predictive analytics, the data tells the tale, allowing you to respond to an account’s buying intent with the right level of engagement, the right message, and the right allocation of funds and resources to convert them to pipeline. This invaluable insight informs and transforms the customer’s experience from the very earliest stages of the buying cycle.

The possibilities for AI are endless. It will enable machines to learn, reason, solve problems, and understand language. But those capabilities may be years away. AI assisted technology – predictive analytics and machine learning – are here today, allowing marketers to use intelligence to detect, learn, and optimize operations.

Businesses Using AI Technology To Drive Success

Companies using AI-assisted technology to drive success.

Below are examples of how some of the world’s leading technology organizations are using predictive analytics and machine learning to be faster, smarter, and more successful.

  • A California-based, multi-national computer technology company uses predictive analytics to flow leads to the sales force that are further down the sales funnel and convert more quickly to pipeline.  Strong targeting and digital multi-touch tactics have resulted in better quality leads, richer sales follow up, and a conversion rate that is twice as high as non-predictive campaigns.
  • A market-leader in enterprise application software is using predictive analytics and machine learning to identify white space accounts for a sales team with a very specific market niche.  A team with an expensive solution to sell to a very few accounts needs access to as many leads as possible.  The account data in the predictive analytics platform was fed into a machine learning algorithm, resulting in a “look alike” list of accounts that matched the profile of the reps’ pipeline accounts, providing them with even more potential opportunities.
  •  This world leader news and information provider wanted to look at their recent wins and losses to better understand which the buying behavior of both. The company used predictive analytics to conduct a look back analysis to its 25 most recent wins and 25 most recent losses.  The algorithm analyzed the key words being researched, the relative success of each tactic, and whether or not they won the deal.  Now the company has a profile of the type of account most likely to convert, as well as which tactics are most effective.  The knowledge gained from this look back analysis will inform the company’s future marketing efforts.


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