Digital MarketingCustomer intent in the nonlinear modern marketing funnel

Customer intent in the nonlinear modern marketing funnel

Google looked at thousands of users’ clickstreams and found the modern funnel is fragmented, mobile, nonlinear. How tech gives insight into customer intent.

Remember when the customer journey was a linear, step-by-step process? Neither do I (wink wink).

According to a recent article by Allan Thygesen, President of the Americas at Google, “no two journeys are exactly alike, and in fact, most journeys don’t resemble a funnel at all.”

Google looked at thousands of users’ clickstream data and found that indeed the traditional funnel is a thing of the past.

Now, journeys are incredibly fragmented, real-time, mobile-first and resemble pyramids, diamonds, hourglasses, and more. Understanding, anticipating and delivering against evolving customer behaviors and needs is key to driving business growth.

There’s an old saying in the data world: “mo data, mo problems”

The more digital and mobile customers become, the more data they produce. As they go through their journey, they switch between sites and apps and also devices to find relevant information that moves them in a productive direction.

At the same time, customers are moving faster and faster, expecting real-time assistance at every step. As a result, it’s becoming increasingly difficult to keep up with the diversified array of signals via nuanced customer segments.

The good news is you can now use technology to stay ahead and predict customer intent at each stage of the journey

Thygesen believes that for brands to be successful, they must start with predicting intent and anticipating needs — throughout the entire customer journey.

No matter how nonlinear, complex and unique, today’s customer journeys are defined by customer signals, activity and are rich with intent. To modern marketers, these signals represent opportunities to deliver relevant, useful and real-time experiences that help customers take their next step.

Furthermore, in an era of machine learning, modern marketers have the ability to assess signals of intent to predict behavior and deliver what customers want before they express it.

To help, Google published a list of three pillars of which to build a predictive engine upon. The result, according to Google, “is an assistive experience for customers and new opportunities for their business.” Furthermore, predicting customer intent also becomes an important driver for business growth.

  1. Align marketing with business outcomes

Instead of focusing on traditional media metrics, such as clicks or impressions, Google recommends optimizing your media to drive the business outcomes you care about.

In its research, Google learned that 89% of leading marketers use strategic metrics, such gross revenue, market share, or CLV, to measure the effectiveness of their campaigns.

  1. Embrace customer lifetime value (CLV)

I’m a big believer in revising the role of marketing to shift from purported “top of the funnel” activities toward engagement that helps customers make decisions and also builds loyalty and lifetime value.

The key to unlocking this shift is to understand a customer’s overall value. And doing so informs bidding and helps build audiences.

“CLV measures the value a person brings to a business across all of their interactions over time,” according to Google. “We’ve found that when marketers focus on CLV, they begin to attract more of the customers they care about, stoke ongoing engagement, and ultimately, minimize churn.”

  1. Integrate automation and machine learning into the marketing (and CX mix)

It’s OK to say that marketing cannot keep up with the growing onslaught of “big” data or the constantly evolving digital customer. However, in an era of machine learning, marketers don’t have to just keep up, they can get ahead.

In research led by Google and MIT SMR, it was learned that most marketers believe their KPIs could be better achieved with greater investment in automation and machine learning technologies.

Additionally, Google and MIT SMR found that measurement leaders were “more than twice as likely as their measurement-challenged counterparts” to report that they are already investing in automation and machine learning technologies to drive marketing growth.

Machine learning isn’t some distant, futuristic, expensive proposition only available to elite scientists

Machines can now readily help marketers get ahead of customer expectations and preferences by recognizing, identifying and building upon meaningful customer patterns. Doing so automates creative, audience, bidding, attribution, and budgeting tools to help drive success.

Marketers have a real shot here of not only improving the role of marketing within the organization, but also becoming a significant contributor to business growth and customer lifetime value. By optimizing search and media to deliver more useful customer engagement in important, real-time moments, marketers end up driving profit and satisfaction.

To get there, marketers need to build a predictive engine to understand and predict customer patterns and then use data-driven attribution to help marketers understand the intent, interactions, and signals that drive long-term growth.


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