No single tactic will deliver on the ultimate customer experience (CX) vision. Just like most matters in business, it takes the right mix of people and processes or methodologies to deliver a meaningful experience.
Customer journeys are at the core of understanding your customers, their needs or issues they may be facing so you can take action to deliver a great customer experience. Customer experience problems vary in complexity from simple to difficult, and the persistent ones are often complex because they occur over time, over channels, and fall between the cracks of organizational silos.
Combining customer journey mapping (CJM) with customer journey analytics (CJA) enables companies to track and analyze customer interactions, making it easier for enterprises to identify and act on complex customer experience issues across the customer journey.
By building a multi-dimensional CX program that encompasses CJA and CJM, companies can best leverage the benefits of quantitative and qualitative insights. It's critical you incorporate both CJA and CJM because data without context or understanding can easily be misinterpreted, and qualitative insights can be dangerous without an understanding of the data behind them.
So how does CJA complement CJM to create a more powerful CX program?
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The Difference Between Journey Mapping and Journey Analytics
To start, I think it's important to clarify the distinction between journey mapping and journey analytics. Based on my experiences across many verticals and enterprises, I summarize the two as:
- Customer Journey Mapping: a practice of documenting consumer behavioral flows and trying to design ideal experiences for common activities.
- Customer Journey Analytics: a program of connecting data across channels and time to measure all behaviors (not just 'happy-path' experiences).
At its core, CJM is a qualitative exercise, while CJA is a quantitative program. Journey maps can provide a great starting point for journey analytics, but without the connected underlying data, journey mapping is nothing more than well thought-out theory.
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Combine Journey Maps With Journey Analytics to Optimize Decision Making
For example, think about mapping out the ideal route to drive from New York to San Francisco. A compulsive planner may consider multiple sources like Google and Apple Maps. They may also speak with a couple of friends who have previously made the drive. At the end of the planning stage, the result would be a series of directions for getting from one point to another.
Extend that example now to a CJA perspective: imagine having access to how every person made the trip — car type, route, average speed, traffic, etc. Using that data, you could not only plan out the optimal route (side note: optimal can vary by person — e.g., fastest route, most scenic, etc.) but you would also understand in detail where and why other people had issues (e.g., 45-minute delay at mile marker 230 due to construction). A good CJA program would continuously update with the latest data (near real-time or at least daily) to enable ongoing validations of prior findings and offer dynamic decision making. In this car trip example, you could get updates on any accidents that have occurred that may cause you to take a different route. What good is having access to all of that data if it isn't used to optimize decisions?
While utilizing CJA has many measurable benefits, combining analytics with CJM creates even more value.
By understanding the key journeys (moments of truth, experiences, intents) your customers have, CJA can be more efficiently deployed and utilized. From a deployment perspective, it is helpful to prioritize data sources that contain interaction details for the primary journeys. A thorough CJM exercise will provide either the exact data required to measure performance (like which pieces of the experience can be analyzed manually) or provide a head start in knowing where to look (like which service channels are involved in each journey).
From a utilization standpoint, the journey maps can serve as the starting blocks for analysis on the finite list of primary things (a.k.a. key journeys) your customers need to do. While individuals or groups will still be able to conduct any ad-hoc analytics they desire, the qualitative understanding of the key journeys will accelerate the initial analytics build-out to understand performance.
A Key Ingredient: Listening to Your Customers
An integral part to any five-star customer experience is listening to customer feedback and using it to improve their experience. This is done by companies through collection of structured and unstructured feedback from customers, typically in a survey format. These programs are frequently used to measure various satisfaction/NPS metrics, from both an overall company performance and a specific interaction level.
Using this data, companies can then drill into underperforming areas (i.e., poorly rated Agents, or underperforming interactions) manually or through a text analytics solution. This allows them to better understand the reasons for any customer frustrations and address practical solutions for improvement. While measuring and understanding customer satisfaction is important, this approach is ultimately limited to the surveys received and the self-reported details provided by the customer. Unfortunately, this is where most companies stop.
Pairing this customer data with CJA can bring additional value to both programs in several areas. First, by connecting surveys to journey data, you can now analyze actual behaviors proceeding the survey and not rely on the user-reported survey data alone. Since customer interactions are complex and often involve multiple steps and activities, there are two primary approaches to this attribution exercise. One is to attribute everything the customer did to the survey and then rely on aggregated data analysis or multivariate analysis to glean insights on the true drivers of the survey. The other is to identify the related detailed events within the journey data and then compare survey response reasons against the identified behaviors. If a customer is accurately stating their primary survey reason, then it is a reasonable assumption that they would have exhibited some of those behaviors in a prior interaction.
For example, if a customer says their primary survey reason is for "payments," then you would expect to see some payment-related activity in their behavioral history (e.g., start a payment online, view amount due, receive a bill). Regardless of the findings, potential insights can be gleaned that would not come from analyzing customer feedback data alone.
The survey scores can also be used as a labeled dataset to build a model for predicting satisfaction based on journey data. The key benefit is that with an effective model you can now understand which customers are likely dissatisfied, regardless if they’ve taken a survey.
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5-Star Customer Experiences Are Within Reach
To stand out in today’s hyper-competitive, multi-channel market, brands that want to differentiate themselves and deliver five-star experiences should use a combination of CJA and CJM to uncover areas of improvement. By carefully listening to customer feedback and using the right analytics, you can anticipate hurdles and rectify them proactively, thereby delivering a seamless CX experience to current and future customers.