The wisdom of using data to map the customer journey
(Editor's note: This is the final article in a three-part series).
The modern customer journey is dynamic and asynchronous. It develops through multiple channels and (potentially) over the course of multiple days or weeks. If it’s a new car, the journey could take several weeks; a pair of shoes may take less than 10 minutes … or vice-versa.
These shifts in the customer journey have forced businesses to evolve. The challenge is only amplified with the increasing number of channels that customers can use. As this number grows, the customer experience is becoming more fragmented across time, touchpoints and messages.
Customers often start their purchase journey in one channel, with that channel’s set of messages and offers, and then finish it in another channel that has a differing set of messages and offers. This complicates the work of brands looking to engage with current and prospective customers.
Understanding the customer journey, the customer path-to-purchase if you will, is a valuable exercise. Marketers need to realize the major and minor touchpoints where customers engage with their brand. Acting on this knowledge, marketers can test and manage the effectiveness of touchpoints. By testing and learning over time, marketers can craft more effective touchpoints and vastly improve the customer experience.
By having a data-driven engagement platform, marketers can describe, measure and optimize customer journeys. Optimization adds a cyclical component—like a feedback loop—to the monetization of customer data. If brands can apply their learnings about customer behavior and preferences over time, then they can be said to have achieved “wisdom.”
The “Wisdom” of Customer Journey Mapping
The wisdom layer at the apex of the DIKW pyramid isn’t the wisdom of ancient philosophers or poets. Rather, achieving wisdom involves applying data, information and knowledge over time in a goal-driven cycle. Data generated from customer interactions are given context through goal-driven analytics and then acted on to create more data. As time goes on, brands collect more data about customers, add deeper context and, eventually, take better actions.
It is ultimately this history of consumer interactions with the brand that allows marketing to shape the next engagement and engagements for look-alike consumers. This time component is where wisdom begins to be applied. To achieve wisdom in the context of the DIKW pyramid, organizations need to build the correct technology ecosystem.
The correct ecosystem includes all the internal processes and controls that enable you to collect the right data, add the right context and take the right action to build long-term interactive relationships with your customers. To win in this environment, businesses must understand this larger context and be able to act within it.
The Customer Journey of a Thousand Miles
The philosopher Lao Tzu wrote that the journey of a thousand miles begins with a single step. In terms of the customer journey, a “single step” can be thought of as collecting the point-in-time data of a single interaction. This data point begins the journey toward more fully understanding the customer. It is the starting point in the ability to make better customer engagement decisions. With better understanding of customer motivations and goals, brands achieve better ROI (better monetization) of their customers and more efficient operational spend.
More accurate customer engagement creates a virtuous cycle. As brands learn more about their customers, they become better able to market to those customers. The problem is that many companies don’t currently have the right tools in place to act upon their customer data and to build the kind of knowledgebase about customers that move them into the Wisdom layer of the DIKW pyramid.
The framework of the DIKW pyramid is important to understand in the age of the empowered customer. Businesses that adhere thought processes to this schema can consider everything from an objective perspective – collecting the right data, connecting the data and adding context to create information, acting on that information to build knowledge, and repeating the cycle to inevitably build wisdom.
The DIKW pyramid provides a framework to evaluate technologies and methodologies at each level of the customer engagement journey. It does not confuse data with business capabilities (although it grounds the business in the data) or analysis for action (although analysis underlies activities). It also does not end in a single cycle. Rather, it uses a goal-driven strategy to drive ongoing improvement.