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Five Ways to Pump Up ROI with AI Marketing Technology

Congratulations! You’ve made the decision to invest in technology that is going to help you anticipate customer needs and deliver on the personalization those customers now expect. Chances are you have already recognized that humans just can’t analyze customer behavior quickly enough or at the level of detail to truly personalize and adapt to changing patterns.

Taking advantage of the new technology — as with any technology innovation — requires a rethink of the underlying processes. Instead of the traditional batch-and-blast marketing model, your new platform allows for a continuous learning environment. The trick now is to reconceptualize your entire marketing process so that you are taking advantage of the new capabilities.

So how do you make the most of the technology?


Don’t try to make a square peg fit into a round hole; redesign your campaign process

The traditional campaign process was created when marketing campaigns were still executed manually. It follows a linear path and has very specific rules. It was logical, thoughtful and practical for the time, but it no longer applies.

Instead of just bolting on the latest technology, marketing leaders should step back and redesign the campaign process from the ground up. The key is to use today’s technology to automate repeatable tasks in a way that best leverages the marketer’s judgment and expertise. Marketers set the goals and provide the messaging content, but the remaining tasks in the process can be automated and built into a closed loop.

Suddenly, the marketer is released from the back-breaking mechanics of setting up journeys and has a new day-to-day role of consuming insights and ideating.


Throw out the vanity metrics in favor of CLV-driven KPIs

Cutting-edge marketing platforms will automatically optimize campaigns for whichever goal you select; they can move the needle quickly and dramatically. But goal setting is an art. The best KPIs are those that strongly correlate to customer lifetime value (CLV). When you optimize for a CLV-correlated campaign goal, you can learn things quickly that would otherwise have remained hidden.

Take this example: to encourage loyalty members to take full advantage of their benefits, a client optimized their campaign for increasing free content downloads. And it worked. Download numbers grew, but revenue numbers declined and retention remained unchanged. It turned out that being really good at getting loyalty members to pay attention to free benefits meant that a subset that typically paid for content was now opting for the free stuff. The campaign goal was being met, but it was creating an adverse result for the organization at large. Oops.

Before you kick off a campaign, spend the time to choose KPIs that will truly drive value for your organization.


Build a robust and scalable message library

One of the advantages of new technology is that the identification of additional customer segments, selection of appropriate messages and management of sound experiments and campaign execution are all automated. This allows the marketer’s role to shift away from the mechanics of campaign execution towards content ideation and variation.

And not to fret: 10,000 micro-segments does not mean 10,000 messages. Building a robust content library is much easier than it initially sounds. A single creative permutation may be highly effective with many unique micro-segments. And with all the elements of a message that you can vary – tone, imagery, offer, subject lines, etc. – your one base message can multiply rapidly.

With a limited amount of work, you can quickly build a robust message library that contains enough variation to appeal to each micro-segment.


Let machine learning and AI discover the what, how and when of campaign execution

In this age of personalization, marketers are realizing improved performance by testing a variety of creative content across smaller and smaller segments. It only makes sense that preferred delivery times for marketing messages will also vary by customer — whether it’s email, push notifications, SMS or any other scheduled outbound messaging channel.

The holy grail for marketers today is “always-on” messaging that is achieved with machine learning and AI technology. Batch-and-blast tools alone or with added send-time optimization won’t get you to true personalization. It is still making broad, albeit educated, assumptions about what, how and when your customer segments want to hear from you. In the “always-on” space, a wide variety of evergreen message campaigns with the same end goal are all active at the same time.

For example, a financial institution might have a goal of improving cross-sell into a new savings account. A wide variety of campaigns could contribute to that — educational messages, announcements of the new product, rewards available, etc. In the “always-on” world, these campaigns are at the ready each day and are automatically selected and sent out based on the probability that a specific message paired with a specific customer will achieve the cross-sell goal. Marketers set guardrails to define the boundaries for each campaign — things like ensuring new customers aren’t eligible for welcome-back messages – and then digest the learnings. The technology does the rest.


Keep experimenting to find the right answer

You have a new campaign design, CLV-oriented goals to track, a robust library of messages and the “always-on” technology has released the evergreen campaigns into the wild. You’re done, right?

Wrong. One of the other benefits of “always-on” technology is the constant learnings and insights that are generated. With an automated, closed-loop AI system, marketers have a tool that simultaneously optimizes and experiments, which means real-time insight into what’s working and what’s not and most importantly, the ability to react.

Traditional experimentation cycles are linear and slow. From the time the hypotheses are created to when the campaigns are out the door and insights are being generated is upwards of a few months. And you end up with learnings that become static “truths” even when customer behaviors change.
In the “always-on” world, experimentation becomes a living, breathing lifeform. Marketers can intervene quickly if they see adverse results – and more accurately pinpoint the source. Maybe the tone is off, maybe there was a huge market event, maybe the incentive is too low. In a matter of days, and without stopping progress, you can be up and running new experiments.

Now that’s using technology to make a difference.

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