Are We There Yet? How AI Transforms Customer Journeys into Real Results


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In the realm of customer experience (CX) one of the hottest tools of the trade is the “customer journey.” In its most basic form, a customer journey is a record of all the various interactions an end-user has with a vendor, and encompasses both internal data (sales, marketing, service) and relevant external information (demographics, social media activity, etc.). So, why is a customer journey so valuable, and why are there literally hundreds of start-ups competing with the likes of Adobe and Oracle to sell their solution to Fortune 500 companies?

To an outsider, the first bit of surprising news is that customer journeys are a relatively new phenomenon in the corporate world. As companies grow, the amount of data amassed about customers piles up at an exponential rate, and typically ends up scattered across silos. The purpose of a customer journey then, is to extract and unify all the so-called touchpoints experienced by an individual person in their dealings with XYZ Inc. For major enterprises, these records often stretch back for years or even decades.

For example, I may have been a customer of the same wireless carrier for the last twenty years, and during that span I may have purchased a wide range of additional services for myself and my family besides a cell phone (full disclosure: I am a Baby Boomer and my customer journey predates the dawn of smartphones) including family plans, home Internet, a femtocell, cable TV and, **gasp**, a landline.

Are we there yet?

Think about that for a second. Not including all the actual purchases, payments and corresponding usage metrics during that span of time, imagine how much other interaction has occurred – hundreds or thousands of emails, direct-mail offers and bills filling up my mailbox, time spent by me on their website browsing their ever-changing portfolio of offerings, managing my account or troubleshooting and, as a last resort, calling a 1-800 number or visiting a retail store. That’s a lot to take in – but the good news is that all these events create digital footprints that can be filtered and sorted into unique individual histories.

So, once a big company has successfully wrangled their data into customer journeys, what happens next? Until AI and machine learning came along, the answer was, not much. The set of available tools in the typical analytics arsenal, even for a Fortune 500 company, could only swim in the shallow end of the value-added pool. AI changed all that, enabling pattern recognition and predictive analytics at scale – in other words, recognizing the common threads across millions of customer journeys comprising billions of events based on (no joke) trillions of calculations.

The latest AI-powered technology use customer journeys as the raw material to fuel the new science of customer journey analytics. Building customer journeys is the critical, mandatory first step. The result, however, is far more valuable than an accurate, complete set of customer records, which are helpful to be sure, but won’t get anyone promoted to senior vice president.
Using AI, the latest, best of breed models can transform each journey into a single, universal metric quantifying each individual’s commitment to the brand, based on the patterns in the data unrecognizable to human eyes, scored using dollars as the unit of measurement. Now you have a standardized scoring system in place that dynamically tracks a company’s relationship with each of its customers, personalized at scale.

To quote every infomercial ever ‘wait…there’s more.’ Up to this point, all the output would be rightfully classified as insights. Most customer journeys are glorified diagnostics, interesting, but not actionable – no next steps identified, no destination mapped out. To a human eye, the future still looks murky.

But to a fit for purpose AI model, customer journeys lead to predictions about which customers are most likely to buy, and what offer has the best probability of converting to sales. This is precision matchmaking on an enterprise scale.

Most of us are familiar with the proverbial advice that success is a journey, not a destination. But when you mix massive amounts of data with machine learning, that recipe turns success into a journey AND a destination – adding value to both sides of the next transaction.

Jean Belanger
Currently CEO of After graduating (LSE), I joined Wood Gundy. I left to start a VC fund, investing in start-ups. 3 went public. After 15 years in finance, I decided to run companies rather than finance them. The first, programming tools vendor, Metrowerks CodeWarrior, built most of the software used on the Mac in the 1990s. When Metrowerks was acquired by Motorola, I was named VP Biz Dev for their semiconductor business, where I invested over $450M in M&A in 14 months. After Motorola, I started data science supply chain software and IOT pioneer, Reddwerks.


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