Machine Learning for Marketing Analytics: Scale Everything, Everywhere

In our latest guide, Data, Insight, Action: Machine Learning & AI for Marketing Analytics, we dig deep into how machine learning enables personalisation at scale. Here are a few key ideas to get you started.

If you’re looking for a hint of what you can do with machine learning for marketing, there’s one movie you should watch: The bizarre and beautiful Everything Everywhere All at Once.

It’s the story of an ordinary person who gains incredible power by exploring the multiverse. She does so with the aid of cutting-edge technology and the willingness to take risks, even fail, and continue learning.

But ultimately, the point of the story is how you can make genuine human connections by being there for people when and where they need you.

There’s no better metaphor for what modern marketing should be. Marketers need the capacity to give customers everything they need, everywhere they’re looking for it, and serve all of them at once. It’s the only way to build a lasting relationship.

And it’s only possible with the right technology.

Machine learning can make marketing smarter, more efficient, and ultimately more effective. When you combine intelligent analytics with automated systems for acting on insights, the results can be amazing.

5 Ways that Machine Learning Powers Up Marketing Analytics

In our latest guide, Data, Insight, Action: Machine Learning & AI for Marketing Analytics, we dig deep into how machine learning enables personalisation at scale. Here are a few key ideas to get you started.

#1: Intelligent Segmentation

Marketers rely on customer data to reach the right group of people with the right message. But with more consumers opting out of sharing their data, it can be challenging to sort them into relevant segments.

Machine learning can analyse anonymous activity to spot trends, then use these insights to create intelligent segments for personalised marketing. This helps ensure that no potential customer has a sub-par experience simply because they chose not to share their personal data.

Intelligent segmentation and personalisation make it possible to reach anonymous people with hyper-relevant messages, which in turn helps build trust with your brand.

#2: Churn Reduction

Customers generally don’t leave a brand relationship without warning. It’s just challenging for marketing to spot the potential red flags in an ocean of customer data.

Machine learning can help here as well. Algorithms can analyse your customer data to identify the behaviours associated with at-risk customers. You can use these insights to find and fix problems in your customer experience and support, as well as develop targeted ways to turn these at-risk customers into raving fans.

#3: Increasing Lifetime Value

Would your business rather have 100 loyal, repeat customers, or 1,000 who buy once and vanish? Most marketers would prefer the former. Long-term customer relationships take fewer resources to maintain than you would spend bringing new people in. And your biggest fans will do the work of finding new customers on your brand’s behalf.

Intelligent marketing analytics can help you identify your most valuable customers and identify ways to better serve and delight them. Whether it’s finding the perfect upsell opportunity or knowing the perfect moment to send a personalised message, machine learning can uncover the insights to make it happen.

#4: Enhanced Conversion

Marketers typically look at conversion rates en masse: How many total people they have reached, and how many took a next step. This is a reasonable way to gauge the overall success of a campaign, but it doesn’t tell the whole story.

For example, marketing might have a high conversion rate for downloading a specific asset. But it may turn out that a low percentage of those who downloaded it go on to request a demo. Another campaign with a lower conversion rate might have a higher percentage of conversions who match your ideal customer profile.

With machine learning, you can analyse conversions of specific groups of people, segmented by behaviour, all the way down to individual consumers.

#5: Contextual Personalisation

Modern marketing is all about delivering the right message to the right person at the right time. The last part is the most challenging: the “right time” might be just a few seconds, or even less. Identifying these moments and acting on them before they pass takes superhuman insight and speed.

Intelligent marketing analytics can identify these micro-moments and refer them to an automated system for turning that insight into action.

For example, a customer with your app on their smartphone might walk past one of your retail outlets in a city where they haven’t checked in before. Real-time analytics can spot this behaviour and trigger a workflow that sends a personalised message welcoming the customer to the city, with a discount voucher and recommendations for local food and entertainment.

Building a System for Intelligent Marketing Analytics

All the above examples show how machine learning can give marketers superpowers of personalisation at scale and across channels. But it takes more than a few smart algorithms to truly reach people everywhere all at once.

Machine learning for marketing analytics is one part of a suite that operates together to consolidate data, generate insight, and take action on that insight. Here’s how it works on Adobe Experience Platform:

There’s a multiverse-worth of customer data out there. But marketers need intelligent analytics to turn that data into insight that can drive business results. Read Data, Insight, Action: Machine Learning & AI for Marketing Analytics to begin your journey to everything, everywhere, all at once.