It should come as no surprise that for seven out of ten business leaders, understanding customer behaviors is the number one challenge1. Over the past two decades, linear and single-channel interactions between customers and companies have evolved into omni/multi-channel engagement journeys that require much more thorough analysis to understand and even more so to monetize.
Enter journey maps. Originated back in the 1980s as service blueprints2, they graphically depict a customer’s journey from the early stages of awareness and initial contact all the way to purchase, ongoing support and retention. As we speak, more than 50% of companies have begun to map out their customers’ journey, and more than 20% are actively mapping mobile journeys.
The purpose of journey mapping is straightforward: shed light into what really matters to prospects and customers as they engage with your business. What is not so straightforward is the data journey maps need to capture and contextualize to help decision makers recognize gaps in their businesses’ core enablers, such as people, processes and technology.
Up until a decade ago, capturing a customer’s emotions, motivations and frustrations along the engagement lifecycle (pared down to different phases) was sufficient to provide a fresh outside-in perspective and justify the value of digital experience design initiatives. These days are over. Business leaders are currently requiring much deeper insights to make decisions and champion change. Gartner predicts that, by 2018, 60% of digital commerce analytics investments will be spent on customer journey analytics. To stay relevant, teams in charge of digital transformation or modernization must embark on “Deep Journey Mapping” that combines the standard mapping methods with Deep Data (in the form of a focused set of data streams) that, when contextualized along the customer journey, can yield greater business value than the typical high-level journey narratives. Below, I expand on the three most impactful attributes that should be included in Deep Journey Maps.
1. Key Activity Classification
Whether mapping high-touch journeys common in B2B environments, or high-frequency routine journeys usually representing B2C engagements, understanding the nature of activities taking place throughout the journey is essential to optimizing your infrastructure for effectiveness and/or efficiency. An effective way to contextualize activities is based on the Action Interaction Automation (AIA) classification scheme:
The extent to which key activities in the customer journey are driven by people, processes or technology (or a combination) serves as a contextualization layer that can help detect if something is fundamentally wrong with how the journey is currently enabled. For instance, one would expect a B2C high-frequency routine journey to involve a lot of automation since repetitive activities are usually good candidates for automation (in such cases, automation can accelerate the journey and potentially reduce the overall cost to acquire and/or serve customers). If the journey map reveals that the opposite is true, the journey needs to be re-designed.
2. Actual and Perceived Experience Variability
As customers interact with your company time and time again along their journey, variability (the sister metric of inconsistency) has emerged as one of the most important focus areas of contemporary customer experience design. 87% of customers think brands need to put more effort into providing a consistent experience3 . Simply, variability analysis aims to find the optimum balance between team members following Standard Operating Procedures (SOPs) and, on the other end, going “beyond and above” in their efforts to attract, engage and serve customers. The question for experience designers is how much flexibility to build in the system.
Journey maps ideally depict two different types of variability:
In general, high variability implies inconsistencies in the organization’s infrastructure, internal support systems and, very possibly, other components of the end-to-end value chain.
3. Customer and Team Member Effort Levels
The Level of Effort (LOE) is the combination of the required skill and time put forth by customers and/or team members to complete key activities as the journey progresses. The latest research suggests high effort levels hurt customer satisfaction and undermine growth by depressing repeat purchases4. Seven out of ten customers think that valuing their time is the most important element to consider when designing digital experiences5.
The challenge in assessing and contextualizing effort levels is double-sided, and Deep Journey Maps address both equally well:
Partial or generalized understanding of your customers’ journey can easily mislead you to suboptimal investments. Common errors include underestimating or overestimating the role technology plays in enabling customer-facing activities, attempting to automate relationship-building interactions that heavily rely on the human factor, missed opportunities to lower costs by automating routine processes, and misdirected employee training or real-time agent guidance. Although traditional journey mapping approaches have come long ways in providing fresh outside-in perspectives on how customers behave, they often come short in terms of specificity, prompting business leaders to question their end-of-day value as decision-making tools. Deep Journey Mapping combines visual narration and advanced analytics to make insights discoverable, and help decision makers interpret the increasingly complex dynamics of customer journeys with utmost confidence.
1. The 2016 State of Digital Transformation (Altimeter)
2. G. Lynn Shostack
3. Kampyle Research (2016)
4. Customer Executive Board (CEB) Research
5. Forrester Research
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