Traditional Customer Journey Mapping is Over (As We Knew It)
Over a decade ago, traditional customer journey mapping made sense. Websites were much simpler, and customers could only take so many routes through a company’s site. Opens and click-throughs used to be enough to tell a linear journey with little or some variation.
Now, channels and entry points continue to flourish for every digital experience. Today’s consumers, who shop on tablets and smartphones in addition to desktop websites, abandon carts, wait for sales, and encounter ads on Google, Facebook, Twitter, and elsewhere. Traditional customer journey mapping is over as marketers know it. Gone are the days when triggering an email or two is enough to drive conversion rates. And forget the traditional marketing funnel.
Hyper-personalization has led to new, erratic online buying habits. It’s not just about acquisition, but fostering brand loyalty from the first touch point, which usually occurs in the digital realm. Each journey is as unique as the customer is.
The bottom line? Stop marketing to the average customer and start marketing to key segments, preferably by automating this process with machine learning.
Today’s top performing brands are using “technology that prioritizes the use of zero- and first-party data, decisioning and next-best-action that enhances customer engagement with a unique value exchange, repeatedly throughout the customer lifecycle.”
We have devices that provide immediate answers. Even the most complex purchase–such as researching a vacation that requires expenses such as airfare, luggage, hotel, passport, Visa, tourist attractions and more–can be accomplished from your fingertips.
That said, organizations need to align marketing to business outcomes. Impressions and clicks are just the start. KPIs such as revenue, gross margin, and conversion rates are the real deal.
The traditional approach
In the traditional customer journey approach, a customer sees your product–perhaps a new accessory for your iPhone–on TV, searches for the product on Google, and clicks through to find the product page. They engage with product pages and detail, add the item to the cart, try several promos they found on Reddit or another website, and eventually abandon the session when the codes fail.
A few days later the customer might see an influencer using the product on their iPhone. All of a sudden their dream comes true: a targeted ad from one of your top competitors appears, with a 10% discount to boot. Without hesitation the customer clicks the ad and purchases the accessory at the lower price.
In this example traditional customer journey analytics would simply (and deceptively) tell you that the product page to cart journey referred from Google is an unhappy journey for this segment “bargain shopper.”
How Quantum Metric approaches customer journey mapping
Quantum Metric recognizes that there is value in understanding the customer’s journey, but that it’s more important to analyze specific pain and success points within the journey. Taking note of these points enables brands to better influence customer decision making during crucial inflection points.
With Quantum Metric’s help, brands have successfully leveraged the Continuous Product Design platform’s real-time insights and webhooks to activate their experiences in order to influence behavior and drive desired outcomes.
Infinite customer journeys
No customer journey is the same, and there is no predetermined path. In fact, there are an infinite number of journeys customers can take between the first touch point and completing a purchase.
With hundreds, or even thousands, of unique entry points, touch points, and channels, it has become difficult to track and trend each journey from a clear-cut and linear sequence. Social influencing–including paid and organic social media–has left a major impact on how customers discover brands.
Despite this, digital product and marketing teams continue to pour a ton of time in trying to predict the most common patterns customers exhibit in their customer journey analysis.
Let’s think back to our “bargain shopper” scenario. Quantum Metric could help the company that lost the customer by providing notifications of customer intent and streaming this data via real-time webhooks to other tools and teams, prompting subsequent action–and fast.
The platform provides data for the full journey analysis that can connect to visualization tools like Looker or Tableau. This data streaming, known as Quantum Activate, could use your CMS or experiment tool to offer a promotion that works in the moment.
Quantum Metric could also make your marketing/CRM tools aware of a high intent “bargain shopper” and position a peremptory ad or remarketing campaign to potentially winback a purchase.
Nowadays organizations must deliver fast and frictionless digital experiences. Machine learning helps predict intent in ways that simply aren’t feasible manually, making it easier to find missed or unexpected connections between customer goals and habits of key customer segments.
Are you interested in learning more about how Quantum Metric can supercharge your approach to customer journey analysis?
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