Combining visual narration and advanced analytics to interpret the increasingly complex dynamics of customer journeys

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 a much more thorough analysis to understand and monetize.

Enter journey maps. Originating 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 that journey maps need to capture and contextualize in order 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, as well as 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. According to an Adobe survey, 65% of business leaders said improving their data analysis is a key factor in delivering a better customer experience.3 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 the high-frequency routine journeys that represent B2C engagements, understanding the nature of activities taking place throughout the journey is essential to optimizing your infrastructure for effectiveness and efficiency. An effective way to contextualize activities is based on the Action Interaction Automation (AIA) classification scheme:

  • Action: Activities requiring the customer or team member to take a single-handed (unilateral) step to achieve a specific objective (e.g. The customer “searches for contact information”)
  • Interaction: Activities involving direct “give-and-take” (bilateral steps) between the customer and the team member (e.g. The customer “asks a question” and the team member “provides an answer”)
  • Automation: Activities that are handled in their entirety by systems, applications and/or interfaces (e.g. “Live chat activates”)

The extent to which key activities in the customer journey are driven by people, processes or technology provides context to help detect if something is fundamentally wrong with the current journey. For instance, one would expect a B2C high-frequency routine journey to involve a lot of automation, since many of the activities are repetitive. 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 modern customer experience design. 87% of customers think brands need to put more effort into providing a consistent experience4. Simply put, variability analysis aims to find the optimum balance between team members by following Standard Operating Procedures (SOPs) and going “above and beyond” in their efforts to attract, engage and serve customers. The question for experience designers is how much flexibility to build into the system.

Journey maps ideally depict two different types of variability:

  • On the team member side, “actual” variability is the extent to which employees must deviate from SOPs to complete a customer inquiry or any other customer-facing activity.
  • On the customer side, “perceived” variability is defined as the extent to which the quality and nature of the experience varies from one instance to another.

In general, high variability implies inconsistencies in the organization’s infrastructure, internal support systems and 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 purchases5. Seven out of ten customers think that valuing their time is the most important element to consider when designing digital experiences6.

The challenge in assessing and contextualizing effort levels is double-sided, and Deep Journey Maps address both:

  • First, since effort is the combined outcome of quantitative (time) and qualitative (skill) determinants, one cannot identify activities that require high effort levels simply by measuring the amount of time they take to complete (i.e. staying on hold before getting connected to a company representative) or the amount of specialized knowledge that they require (i.e. completing a First-Notice-Of-Loss form). Deep Journey Maps include quantitative and qualitative assessments of LOE in the form of “layers” on the map, resulting in faster pattern discovery.
  • Second, assessing effort levels and tying them to specific activities is only the first step in optimizing your customers’ experience. What carries even more tangible value is contextualizing effort levels along the end-to-end journey and understanding how much of the burden is absorbed by the company versus the customer. True customer-centric companies take ownership of key activities, via automation, lean processes or other means to alleviate customer effort.

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 a long way in providing outside-in perspectives on how customers behave, they often fall short in terms of specificity, prompting business leaders to question their 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.

Sources
1. The 2016 State of Digital Transformation (Altimeter)
2. G. Lynn Shostack
3. Better Data Analytics is Critical to Improving Customer Experience (eMarketing 2018)
4. Kampyle Research (2016)
5. Customer Executive Board (CEB) Research
6. Forrester Research

AUTHOR
Stamos Kanellakis

Stamos Kanellakis

He is the digital transformation strategy leader at Compunnel Digital. He works at the intersection of business strategy, customer experience and digital technologies. Stamos helps businesses re-invent how they create, deliver and measure value.

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