Journey Mapping: A Data-Driven Approach to Accelerating Growth

Most enterprises with whom we work have a strong interest in understanding how to find and engage with prospective customers, how those prospects become customers (or not), and what those customers buy when they engage.

Modern customer data platform (CDP) systems and tools, coupled with what one might call engagement instrumentation tools, simplify the process of journey mapping to understand these crucial factors. 

We interact with our clients and customers across many platforms and through many communication media today. What do we know about our customer's buying decisions and timing? Which opportunities do our customers value the most? How can we gather information respectfully, in a manner complying with laws and customs, without being intrusive?

It turns out that there is much data available to us that helps us understand the factors in a buying journey, which can be used and analyzed in a way that keeps our customers' trust in us. 

To make the best decisions on how to create, price, and fulfill orders for our customers, we need to understand as much as possible about the paths the customers take along the way—from identifying a need or want to making an actual purchase with us. While it is common to use campaign metrics such as read rate, open rate, and click-through rate to gauge interest and engagement around specific promotions or messages, we need to equip ourselves with more data to understand which message is more relevant. We often see cases where we “over-message” our customers on premise that “if the message is a match, send it.” Even if multiple opportunities could be of interest to our customers, which one would they value most at this moment?

To help us differentiate the likely significance each message might have, we need to dig deeper into what we know about our customer, our business, and the general environment. We want to efficiently explore the interactions our enterprise has with what we might call our ‘base of followers’: our interested clients, customers, partners, prospects, and fans. We use the term in a broad sense (beyond the social media context) to signify the interest each has in our offerings, solutions, and experiences.

For enterprises with a large group of employees or volunteers, we believe it is important to add these folks to our follower base as well. Our clients often tell us they can be among the most passionate supporters, even ambassadors for us.

What has changed with the advent of customer analytics tools and the CDP (customer data platform) is the scope, accuracy, and timeliness of the data available to shape our understanding of our "follower" base. 

With a well-structured CDP processing data from our interactions, messages, tools, and environment, we can learn faster and gain insights on the many parts of the journey our followers take in engaging with us. Blending what we know about the full customer experiences with “macro-level” data on our business and the larger environment helps us form and test hypotheses about what our followers value most.

Journey mapping is the process of charting or diagramming the steps our followers take in a particular part of the customer conversion or sales process. 

We might focus on “prospect-to-first-transaction” to improve our relevance to our base of followers, convincing them to make the first transaction with us.

Or, we might follow the path to the second transaction in an effort to understand how we build successfully on the first interaction. Whether seeking to improve outreach, interest, affinity, engagement, transactions, mindshare or walletshare, we can frame a decision tree around the steps our followers take (or don't take), reviewing the “passthrough rate” and velocity through different decision or action points. 

We might want to track action or reaction: what moved our follower to take a step forward with us? We might want to track the proverbial decision junction to understand whether the follower continues with us or with a competitor. For highly transactional enterprises, we need business intelligence and automation to understand these journeys.

Once we identify the data required to measure or monitor the appropriate decision points, we need to collect, transform and integrate this into a single platform.

By overlaying this business intelligence on our journey maps, we can form a picture of velocity, which means speed/frequency and direction. Having mapped the decision blocks, whether to act at all or to make a specific choice among directions, we can now instrument this picture to see how we are doing.

How fast or frequently do our followers take the leap through the decision hurdles? How often do they head our way versus going somewhere else? Where and when might they give up along the way? What were we promoting over this timeframe? How was our customer purchase rate running? We have all learned the importance of good data, often illustrated through metaphors of exploring unknown territory or monitoring a complex environment. 

When we collect, transform, and integrate data across our landscape into a CDP in near real-time, we can literally build and use a "console" that shows the movement through our journey map in a manner akin to monitoring the systems in a factory.

Sounds wonky or overly “techie” to analyze in this way? I think back to what a mentor taught me about process improvement, saying:

"To improve, we must monitor and test. To monitor and test, we must measure our performance. To measure our performance, we must instrument. To instrument, we must decide what data is necessary to tell us how we are doing."

While the lesson for me was framed in engineering terms (reflecting my background), the same basic idea applies to our customer intelligence journey. We can and should design journey maps that stretch across the process of interest (interest-to-prospect, first-time-customer-to-repeat, etc.), using all the relevant data available to us. When we do this and add actual measures that we track and report frequently (even in real-time for some highly transactional enterprises), we can now test new ideas and new approaches much more efficiently.

Did messaging campaign A or B elicit more responses? Which offer from last week brought more second purchasers? Asking these questions is not new. What is relatively new is the efficiency and precision with which we can act and respond. Journey maps, supported by the CDP, tooling, and appropriate data science, give us much higher confidence both in our current state and in deciding the best actions to take as we innovate and improve.