Though primitive by the standards of modern navigation techniques, dead reckoning is still required training for would-be pilots. The process of using a known, fixed location and estimates of air speed and heading to plot a course to a chosen destination can be accomplished with little more than a pencil and a compass. By using a continual influx of new data points – a new fixed location, a change in velocity or drift – dead reckoning can be a lifesaver compared with the alternative of “flying blind.”
It’s the same with having a data strategy in the realm of customer experience. The data is there and providing continual signals, it’s just a matter of figuring out what the data can tell us in relation to a desired outcome. If you know where you want to go, you just need to know what to look for. Whether the goal is to improve customer lifetime value (CLV), acquisition, retention, etc., once the outcome is known the next steps are to figure out what data is needed to support it, how to organize it, and – once complete – how to determine if it met the standards for success. Just as successful dead reckoning helps other pilots navigate the same route by providing additional fixed data points, a successful data strategy builds on its success by homing in on effective techniques and discarding what didn’t work.
Understand the Customer
For any desired outcome, customers leave data points – signals – every time they interact with a brand. One of the two key steps to achieve an intended outcome is to analyze what the data in a customer journey reveals about the propensity to reach that state. If the goal is retention, a brand will need to ask questions of the data related to that specific outcome. What is the customer’s monthly spend, is the customer active in a loyalty program, or did a customer post a product review might be some of the questions that indicate a customer’s propensity to stay with a brand. More detailed analysis might involve comparing a particular customer’s monthly spend with the monthly spend of loyal customers, or with those who churned.
Analyzing how a customer proceeds through a customer journey as it pertains to the outcome in question is the customer-focused part of a data strategy. A complete understanding requires analyzing data from every source, recognizing that every bit of data may be important in forming a clear picture of a customer’s likely journey as it unfolds.
Understand and Map Data to the Customer Journey
The second component in a data strategy is to look at the data through the lens of the data itself. Is it fit for purpose? If the first component is to understand the customer journey, the second is to ensure that data maps to that understanding.
Ensuring that data is ready for prime time, if you will, includes the standard data quality steps such as matching, cleansing, and merging, but also requires checking cadence and availability: Is data smoothly flowing in from every needed source? A high-quality golden record that meets the needs of the business, and is in a cadence that matches the cadence of a customer journey, is fundamental for meeting a customer with a next-best action geared to optimize the preferred outcome.
For a simple example, consider a customer journey that includes interactions with customer service and returns. If a journey is optimized for retention, a brand will need connectivity into CRM history, returns process, perhaps even accounting /transaction history from a commerce system to provide reps a real-time view into the journey as it unfolds in order to take an appropriate action. A rep may see one customer more likely to churn than another. With a real-time connectivity and visibility, a rep will know for example which channels and at which frequency to engage with a customer to optimize for the intended outcome.
For every possible way of analyzing a customer journey, there is a collection of data points that map to the kinds of information that reveal important details about what’s happening in that journey. There is also a collection of channels – direct and indirect – through which a brand can mediate the action that will propel the journey to its desired conclusion.
Direct channels refer to actions and decisions such at what image(s) or content to display to a customer on a branded website, or deciding what advertising to buy that will best influence the desired outcome. Indirect refers more to human-mediated options, such as providing a customer-facing employee – a front-desk clerk, a customer service agent, a healthcare provider – with the needed information to help guide the customer along their journey.
Close the Data Loop for Continuous Improvement
A detailed analysis of success metrics is a key step for closing the loop and refining a data strategy. Brands want to be experimental and innovative to transform not only a customer’s experience, but also to transform how the brand interacts with customers in the future. This requires a certain nimbleness, a combination of agility – the ability to move quickly – with a firm understanding of what’s happening in the moment and whether or not what’s happening contributes to the direction a brand wishes to go.
The difference between mere agility and being nimble requires closing the loop on data by ensuring that all needed data is collected to form a deep understanding of a customer, and that the data is fit for purpose. Any brand is capable of agility, but moving quickly means very little if a brand is moving in the wrong direction, “flying blind” as it were.
To plot the right course, a brand must know that every checkpoint is accurate. We’ve written about the snowball effect in a previous blog, and interestingly enough compounding problems is the biggest drawback of dead reckoning in navigation. Any misunderstanding of a fixed point or a miscalculation of incoming data will skew results.
To avoid making this mistake, a complete data strategy requires continually assessing whether a customer journey or journeys are moving to the intended outcome, figuring out what worked and what didn’t in steering them toward that goal, and knowing what data was important to ultimately produce a next-best action.
Being nimble presupposes a recognition that a complete data strategy is never fixed, but rather defined by continuous improvement, whether that improvement aligns with the cadence of an individual customer, or at scale for customers with similar journeys.
As Maya Angelou once said, “You can’t really know where you are going until you know where you have been.” I doubt she was referring to data quality, but the sentiment most definitely applies.
Related Content
Trust, But Verify: Make Bold Marketing Decisions with Full Trust in Data Quality
The Role of a Golden Record in Providing a Consistently Relevant, Personalized CX
Build a Comprehensive Customer Understanding Through Perfect Data
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