How 3 brands are anticipating customers’ needs to drive growth

Jason Spero September 2019 Search, Experience & Design

Would you believe a recent review for a toothbrush on YouTube has been watched 21 million times? It’s true. And it’s proof that, for today’s shopper, any purchase — whether it’s a sports car or a toothbrush — warrants extensive research.

It’s also why the consumer journey is far more complex than ever before. The explosion of devices and channels has compartmentalized our consumer understanding into silos, making it harder to make sense of the myriad digital touchpoints in a journey.

With that said, the struggle for brands to show up in these moments — and influence purchase behavior — is real. We’re seeing organizations starting to adapt their talent, processes, and tools to deal with these levels of unprecedented complexity, but it’s one of the top challenges marketers tell me they face. But what if I told you that you could stop trying to just show up? What if I told you that you could get ahead of consumers and solve their problems earlier and throughout the customer journey? What if I told you that, today, you can anticipate your customers’ needs?

We’re on the brink of a new era, when the explosion of data and digital touchpoints is actually a good thing. Thanks to breakthroughs in machine learning, we’re able to process, pattern match, and deeply understand the individual moments that matter, as well as their collective role across the entire user journey.

Which means you can stop chasing intent in individual moments and use technology to understand what matters to people beyond the moment.

How? We’ve developed a formula — based on the same inputs that machine learning needs to solve any problem — to help you better anticipate customer needs, reach them in the moments that will influence their decision-making, and drive growth well into the future.

Set a goal focused on business growth

What do you ultimately want your business to achieve? I’d guess that most brands would unequivocally say, “growth!” But to achieve this, you have to set the right goal. That means looking past vanity metrics — like impressions, clicks, and online conversions — and connecting your media to sales, revenue, gross margin, and profits.

Arrow points at blue button. Look beyond just vanity metrics: Impressions, clicks, and online conversions. Bar chart shows growth in dollars. And connect to your media to: Sales revenue, gross margin, and profits.

Let’s look at the U.S. Navy. They’re not an obvious brand that comes to mind when you think of digital marketing. But, like any other brand, they also faces growth challenges. For the U.S. Navy, growth means attracting new recruits, especially in harder-to-fill careers, such as engineering.

The team was spending money on digital marketing, but, because they were only tracking online leads not actual recruits, they had no idea if it was working or not. So they took a closer look at internal data to better understand the specific types of content that was driving recruitment. Then they used machine learning to reach and convert hard-to-find audiences, both online and offline.

Machine learning algorithms can help you find and engage high-value customers who’ll drive growth for your brand.

By focusing on the right measurement goal, the team can now say with confidence that their digital investments are working. In fact, digital marketing is currently the No. 1 driver of enlisted recruits across all paid advertising for the U.S. Navy.

Bring the right data to the problem

With the right customer insights, machine learning algorithms can help you find and engage high-value customers who’ll drive growth for your brand — and help you avoid customers who won’t. But what do we mean by the “right” customer insights?

You need to move past demographics and to get to the crux of your high-value customers’ behavior. By drilling into segmentation insights and using that deeper behavioral understanding to fuel your machine learning, you no longer have to dig through a pile of data to create valuable segments. You can quickly uncover new, high-potential audiences and reduce marketing costs.

U.S. television provider Dish Network is one brand seeing great success doing this. The marketing team wanted machine learning to help drive customer lifetime value (CLV). But first they needed to understand what high-CLV customers looked like. They had several key insights to work with: They knew the attributes of customers who were worth 5X more than the average subscriber; they knew who had higher attrition rates; and they understood that people moved from online to offline. More than half of all subscriptions come through the call center, even when the research journey starts online.

Success meant being able to treat these segments differently, but there was no feasible way of doing it manually. The team connected offline conversion data to Google Ads, which allowed machine learning to understand the attributes of high-CLV users and find similar users at scale. Since then, the profitability of Dish Network’s performance campaigns has increased by an impressive 43%.

Automate across the customer journey

With machine learning, we no longer have to manually connect intent to marketing. Brands can anticipate consumer needs and deliver truly authentic and assistive experiences.

But the technology is designed to learn and improve over time. This means breaking down organizational and media silos within organizations, so machine learning can help you see, recognize, and build patterns across the entire customer journey.

For U.S.-based weight loss program Jenny Craig, machine learning has been a real game-changer. Traditionally, the brand focused its marketing efforts on people who were already on the lookout for a weight loss plan. But now, using machine learning, the team is able to reach people at any point in the research process, including when they’re just starting to consider healthier lifestyle options.

We can better understand [consumer] preferences, behaviors, and needs, and design profitable marketing programs that drive business outcomes we care about.

Google’s automated smart bidding lets the Jenny Craig team optimize for high-potential audience segments throughout the journey. They can reach more of the people who will deliver long-term growth for the brand — not just drive short-term conversions. And it’s worked. Jenny Craig has seen a 30% increase in appointment rates and a 10% increase in revenue.

Today, with machine learning in digital marketing, we can anticipate what matters to consumers. We can better understand their preferences, behaviors, and needs, and design profitable marketing programs that drive business outcomes that really matter. It can be challenging to get started, but with the right goals, the right insights, and automation across the entire customer journey, we can all get there.

Everything a marketer needs to know about machine learning