In the realm of customer experience (CX, for the uninitiated), 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 startups 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 have been a customer of the same wireless carrier for the last twenty years, and during that span I have purchased a wide range of additional services for myself and my family besides a cellphone (full disclosure: I am Gen X and my customer journey predates the dawn of smartphones) including family plans, home Internet, a femtocell, cable TV and a **gasp** landline.
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 patent-pending Cerebri Values system uses customer journeys as the raw material to fuel the new science of AI-powered customer experience. Building customer journeys is the critical, mandatory first step. The end 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.
At Cerebri AI our model transforms 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, but wait…there’s more.
Up to this point, all of 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 machine-learning model like the Cerebri Values system, 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.s