The changing customer journey may be one of the most disruptive trends in digital. How can machine learning help you take advantage of this crucial advertising trend and outpace your competitors?
One of the biggest changes to digital is the changing customer journey. What’s the customer journey? Forrester Research defines the customer journey as follows: “The customer journey spans a variety of touchpoints by which the customer moves from awareness to engagement and purchase. Successful brands focus on developing a seamless experience that ensures each touchpoint interconnects and contributes to the overall journey.”
Customers Are in Control Now
So what makes the customer journey one of the biggest trends in the advertising industry? It’s in a state of flux. The “classic” customer journey - a virtuous circle that used to proceed linearly from consideration to “bond” to purchase to experiential usage to advocacy has changed.
Instead, customers go through a lengthy consideration and a lengthy evaluation stage that involves an increasingly larger proportion of digital research. 69% of 18-36 year-olds have “webroomed,” meaning they have browsed a digital storefront rather than a brick-and-mortar storefront, while 71% of 37-48 year-olds have webroomed. But 2/3 of the evaluation stage is customer-controlled, including browsing online reviews, soliciting word-of-mouth and pre-purchase browsing.
What does this mean to you as a marketer? It means that shoppers are more fickle, harder to please and more quick to tune you out if your message isn’t relevant to them right this second. Usage of online ad blockers is up 30% in the last year. Search behemoth Google is taking the proactive step of adding a built-in ad blocker to Chrome, the most popular web browser in the world.
Adapting to the Changing Customer Journey
One of the best ways to avoid being screened out by customers is to go where customers are actively looking for you. This means deeper-funnel, intent-driven channels, such as search marketing.
Searchers are significantly more likely to be in-market for your products and services. 4 out of 5 searchers actually prefer tailored search ads for their ZIP code. This means that in contrast to swatting away your annoying, irrelevant ads, searchers are actually more likely to want to hear your message…if it’s properly targeted to them.
Machine learning-driven solutions
So how do you ensure that your message is properly targeted to the right audience at the right time in the right stage of your sales or conversion funnel? You might need machine learning to help you, here.
Machine learning is a form of artificial intelligence (AI) that involves technology learning over time based on observations made from huge pools of data. While it may seem like a bit of a non-sequitur, machine learning is actually a very relevant solution to the challenges of digital marketers looking to connect with increasingly disaffected customers.
Why? Because machine learning draws its insights from data - the same massive pools of data that you, as a digital marketer, may already be drowning in. In paid search alone, there are potentially hundreds of thousands of data points that can be associated with a single click on a single set of keywords. Somewhere in the millions of permutations of ZIP codes, device types, times-of-day and demographic information lies the optimal path to getting your message in front of the right customer exactly when they’re ready to hear what you have to say.
But how can even the most talented search marketing professionals manually track and manage data for the thousands of clicks they receive on their thousands (or millions) of keywords?
You probably already have the answer - or even better, an even smarter challenge to this question. Namely, does it even make sense for marketers to manually pore over million-row spreadsheets in search of these data-driven insights? No, it probably doesn’t. As a marketer, you excel in creativity, strategy and innovation. As for crunching gigantic amounts of data - that’s where machine learning comes into play. Here are some specific business use cases of machine learning at various stages of the changing customer journey, and how it’s helping companies find, engage and retain customers along the way:
Natural language processing (NLP)
Machine learning can empower digital advertisers, particularly in search marketing, to predictively model the performance of data-scarce keywords by using machine learning-powered NLP to semantically model the performance of similar keywords. This is especially important with long-tail keywords that signal high buying intent from customers who are right about to open their wallets. A predictive advertising optimization solution using machine learning with NLP can ensure you capture every high-intent click.
Chatbots / Voice assistants
Many businesses are using machine learning-powered chatbots to handle customer service by creating more-conversational experiences. 44% of surveyed respondents stated a preference for chatbots to provide customer service, provided the user experience could be perfected.
Companies such as Urban Airship and Microsoft Azure use machine learning to predictively model the most common pain points in a post-purchase customer journey. By figuring out the most likely points at which customers might leave, and then working backwards to address the pain points that led up to that point with special offers and loyalty rewards, companies can reduce attrition and drive higher retention.
With the changes in the customer journey and the rise of digital channels, there are more ways than ever to try to reach customers - and more ways than ever for customers to screen you out. Smart businesses can adapt to this challenge by ensuring their marketing programs include both intent-driven channels - where customers are more receptive to your messaging - and machine learning technology to decrypt those mountains of marketing data and turn them into powerful acquisition and retention tools.
About the AuthorMore Content by Charles Studt