The Smart Way Forward: Artificial Intelligence And Machine Learning

AI and ML are no longer futuristic technologies. They’re now increasingly integral capabilities for any brand that aims to keep its customers engaged moving forward into 2018 and beyond.

The Smart Way Forward: Artificial Intelligence And Machine Learning

by Michael Plimsoll

Posted on 12-16-2017

This article is part of CMO.com’s December series about 2018 predictions and trends. Click here for more.

Artificial intelligence (AI) and machine learning (ML) are no longer futuristic technologies. They’re now increasingly integral capabilities for any brand that aims to keep its customers engaged moving forward into 2018 and beyond.

Two key trends are driving this uptick. First, customer experiences have become more complex across an ever-growing range of digital and physical touchpoints—all of which are generating huge volumes of data. Second, customer expectations have continued to grow, in turn demanding deeper, broader, and more relevant contextual content to be served.

We have gotten to the point where there’s so much data that it’s nearly impossible for a human being to sift through it all in order to uncover insights. But, getting the insights from data is imperative to creating content that resonates with and engages your audience. So what’s a digital leader to do? AI and machine learning to the rescue.

Anomaly Detection And Intelligent Audience Segmenting

One of the biggest opportunities for companies lies in the use of AI and ML technologies within their analytics to avoid surprises.

Let’s take the following hypothetical example: You wake up early one Thursday morning to learn your company’s conversion rates are suddenly lower. Courtesy of machine learning and anomaly detection, real-time analytics have alerted the appropriate team to the drop, as well.

But don’t be fooled: Anomaly detection is about more than just sending alerts when something out of the ordinary happens. It is about looking back over 30, 60, or 90 days, tracking an event hour by hour, and understanding daily, weekly, and monthly variances. These types of analyses, which involve large clusters of data, require ML.

This means that same Thursday morning, not only are you alerted that something is off with your conversion rate, but your analyst, who was also alerted, is already leveraging contribution analysis, AI, and ML to automatically scour the data to determine which events, variables, and factors contributed to the drop. All of this happens in minutes.

And your analyst can already tell you, for example, that the issue was due to a large proportion of visitors coming from an affiliate website and searching for a specific type of offer. Then on further inspection, you find that the affiliate is promoting a specific offer, which was mistakenly removed from your site’s home page.

Your analyst has also created several new intelligent audience segments for you, using AI to understand their propensity to engage and convert and which are most valuable to you. Then he used look-alike modelling to find potentially similar audiences in third-party data.

You haven’t even finished your coffee yet, and already you have actionable intelligence at the ready. Next step …

Content Creation Using Automated And Intelligent Tools

So now you have all these audiences to engage with, right? Next you share the new audiences with your editorial team via integrated workflows. You instruct them to come up with new content for each audience to ensure that when they next engage with you, they’ll receive the most relevant experience and offer.

In the past, this content-creation process took days, if not weeks. But now AI and ML technologies are embedded in the editors’ tools, giving them access to dynamic content and assets. That includes preapproved templates, where messages, images, and calls to action are laid out differently, millions of potential images are at their disposal, as are a variety of potential lead messages.

That’s still a ton of potential combinations to ponder. But by using automated personalization, image understanding, and content intelligence, the team can automate the creation of thousands of personalized messages and present them in real time.

But also consider: While this delivers great, contextual experiences to new visitors to the site, what about all those people who visited in the morning and received a poor experience? Well, ML and AI enables you to reuse your content and engage with the same audiences via email, SMS, or push notification. ML and natural language processing perform subject line optimization, ensuring the best possible open rates.

It is not even lunchtime, but you have managed to identify an issue, find the cause, and create new experiences to not only engage new visitors, but also to re-engage visitors who had poor experiences.

Giving Marketers Control Of The Conversion Process

Let’s review. In the past couple of hours, you have leveraged a host of AI and ML capabilities, from anomaly detection and contribution analysis to subject line optimization and content intelligence. None of these capabilities removed the marketer from the equation, but rather they did the heavy lifting of the mundane tasks (sifting through data and content) and allowed the marketer to be more creative and increase speed to market and content velocity.

Through the use of AI and ML, particularly in the forms of techniques such as the ones discussed here, marketers can remain in control. Instead of simply watching trends change, they can actively anticipate those trends, pinpoint the causes, and take proactive steps to mitigate risk. Are you ready to do the same?

Editor’s Note: Plimsoll will be discussing AI for driving efficiency and cost savings at the Den Live Summit. Click here to learn more.

Topics: Insights & Inspiration, Experience Cloud, Insights Inspiration, Digital Transformation, Digital Foundation, Analytics, Information Technology, CMO by Adobe

Products: Creative Cloud, Experience Cloud, Analytics