Companies are investing heavily in customer data hubs and integration strategies to build a full, 360-degree view of every customer. At the same time, marketing clouds are becoming more connected, enabling marketers to orchestrate their customers’ experiences in one place, across all channels.

However, as marketers work to map more complex and in-depth customer journeys, they tend to discover a scaling issue with these approaches: the more experiences, sub-segments, and models you optimize for, the more complex the entire orchestration process becomes. Ironically, this can lead to more manual work, where marketers need to create new content and copy, and manually run more campaigns. What marketers need is a solution that can automate the thousands of decisions around a customer’s journey, the experience they should be exposed to, and the content that enables this experience.

This is a paradigm shift for many organizations, and marketers will need new tools to manage the plethora of journeys. Our vision for this is through the use of AI-driven customer journeys.

What is an AI-driven customer journey?

Most modern marketers have come across terms like “next best action” or 1:1 personalization, where each individual customer might get a unique set of content or experiences to maximize their likelihood of conversion.

The challenge with these strategies is that they are usually limited to optimizing within a specific channel or picking from a set of options. In these cases, a marketer is only personalizing recommendations of products on a website or choosing whether or not to send a specific e-mail.

As Artificial Intelligence (AI) technologies become more versatile and complex, we will enter a world where the entire customer journey can be optimized across a nearly infinite number of options; it won’t be about picking products or triggering emails, but looking at all the experiences a customer can be exposed to. Imagine these scenarios:

  • Your Marketing AI decides whether to send a customer an SMS message, an e-mail or direct them to a phone call based on which channel they likely prefer.
  • Your Marketing AI generates a personalized website template that is image heavy for the creative left-brained visitor, and tones it down for those that prefer to read.
  • Your Marketing AI decides to chat directly with a customer on your website because it needs to learn more about their preferences and then recommends a fully-individualized bundle of products to meet their needs.

Sound far-fetched? Maybe, but with chatbots, natural language processing, and deep learning, this is all possible today.

What needs to happen to enable AI-driven customer experience optimization?

More generally, the promise of an “AI-driven customer journey” is that a customer’s (or potential customer’s) online and offline experiences will be determined through an automated, artificially intelligent algorithm. This means individualizing the specific time of communication, channel mix, content, and call to action all to the individual consumer.

A few things need to happen for this to take place…

Most marketers today still struggle to centralize their data into customer journeys

To do any real AI-powered analysis, algorithms need access to huge amounts of data. This is what enables the use of advanced analytic and algorithmic approaches like deep learning. Marketers have access to massive data sets if they know where to look. These need to be leveraged and organizations need to actually work to strategically make use of these datasets.

Marketing-oriented analytics tools need to shift from one-off modeling to AI-driven frameworks

Most analysis in marketing today is one-off in nature… Models are built manually to achieve one specific goal (e.g., recommend products for a web page, generate propensity scores for purchases, etc.). Marketers need new modeling frameworks on which to base their models and streamline the process. Ideally, modeling tools need to work as well with event logs (e.g., probabilistic graphical models), as they do with journeys.

Marketers need quantitative and ROI-driven mindsets.

In our experience, most marketing organizations still struggle with picking key performance indicators and setting quantitative goals. However, you can’t optimize or use AI without having a clear metric you are optimizing for. This is crucial before adopting any sort of AI-driven approach, as the models and algorithms you use need to know what they are optimizing for.

The marketing world is in flux right now, as new technologies, algorithms, and marketing approaches are constantly changing. At the same time, this is what makes the space so exciting – with these new technologies, not only will marketing become more complex in and of itself, it will also become more integral to revenue growth, product development, and the overall customer experience for all B2C companies.