The customer journey map represents what customers see and feel, and how the business supports the whole process. Customer journey analytics can be used to validate how customer behavior fits into the map, show what the company needs to do to measure the critical steps, and through predictive modeling, determine the causes of customer actions.

Underlying everything is a business that needs to delight customers and stay profitable. The layers are distinct, yet related. Good strategy, good execution, and good monitoring keep it all tied together.

The business supports all of the points along the journey, but customers feel personal ownership of some steps, while only the business is exposed to other steps. A complete customer journey map accounts for both parts.

The business needs to remember to “hear” the customer in the customer’s vocabulary, and then translate that into business metrics. Listening to customer vocabulary can sometimes uncover tensions between the delighted customer journey and the unhappy customer journey. And, business metrics may not be aligned with customer journeys and perceptions. The business strategy needs to stay aware of the customer’s perception while still pushing towards the business goals.

Use Customer Journey Analytics to improve CX

Analytics are used at each touchpoint to make the process better. Whether it’s a customer interaction or employee satisfaction, when people are happier, the business runs better.

Customer journey map

Business analytics map

See online advertisement Create demand, which segments to target
Purchase product Acquire customers, cost per customer
Track best campaigns, best segments
Receive welcome letter Onboarding customers
30-day / 90-day expected behavior model
Get weekly email, visit website Send and track communications
Behavioral tracking
Make more purchases Track sales
KPI dashboard
Predict and measure customer lifetime value
Purchase in response to an ad about a related product Cross-sell models target related items
Upsell models target more profitable items
Interact with customer service Customer service staffing, hot topics, time it takes to get the right answer
Receive satisfaction feedback survey Customer satisfaction measurement
Experiences with happy employees Employee satisfaction and management
Refer a friend Customer loyalty
Switch to a competitor, or reduce spend with us Customer retention models, messaging, actions
Receive email with coupon and switch back Win-back models and targeted campaigns

Want to learn more about our Customer Loyalty and Retention Analytics services? Click here!

Understanding the “analytics” in Customer Journey Analytics

Going one layer deeper, the analytics tasks that relate to the customer journey look like this:

Customer journey map

Customer Journey Analytics roadmap

What you get

See online advertisement Cluster analysis on existing customers. Limit inputs to demographic fields that are available to purchase, or to those that are also available in Google or Facebook. Find the right segments to target.
Purchase product Track campaign success rates; compare campaigns using t-test, chi-square, and survival analysis Find which campaigns are good enough to re-use.
Receive welcome letter Build predictive models using linear regression, logistic regression, or decision trees to get a quick understanding of what behaviors to expect from customers. Find out when customers don’t interact enough so you can encourage more responsive behavior.
Get weekly email, visit website Gather data to build customer profiles that will be used in machine learning models. The ability to learn from customer behavior and put new tactics into play.
Make more purchases Track key business and operational goals.

Use purchase data to estimate customer lifetime value. Use customer lifetime value to determine best customers. Keep your eye on your best customers.

Keep track of your business goals and delight your best customers.
Purchase in response to an ad about a related product Use models to rank customers who are most likely to buy related items, or buy more expensive items. Use a mixture of communications and incentives to get customers to increase the share of wallet that they spend with you. Increase the amount of money each customer spends with you. Tighten their brand commitment.
Interact with customer service >Use topic and sentiment analysis to get a view of what customers are saying. Understand and fix problems sooner.
Receive satisfaction feedback survey Use surveys to track satisfaction by plotting data over time. When satisfaction changes, look at what’s going on in the business (and the marketplace). If possible, add data to your models to reflect changes in the world. Then update your predictions and take action where needed. Watch when change happens and figure out how to react.
Experiences with happy employees Employees are part of customer experiences. Use the same tools that you use to acquire and keep customers, to acquire and keep employees. Delight customers with happy employees. Maintain the brand image.
Refer a friend >Track loyalty with the NPS or by looking at purchases for shopping consistency. Review regularly so you can see when big changes happen and figure out the causes. Monitoring over time shows changes in brand perception and is a warning system for problems.
Switch to a competitor, or reduce spend with us Use machine learning to figure out why people left and to anticipate who’s next. Get insights into what might be causing people to leave. Anticipate weak spots in the business model and take corrective action. Provide incentives that solve key issues and encourage people to stay.
Receive email with coupon and switch back >Use demographic data and past history to target those customers most likely to return, and then focus on them. Use segmentation models to understand which groups of people leave, and which subgroups are likely to return. Bring old friends back to the brand. If a negative experience is not what caused them to leave, they might come back.

There are plenty of opportunities to add value by adding customer journey analytics to gain insight into why customers behave the way they do. Then you can determine key actions based on likely causes to achieve better outcomes for you and your customers.

Missed part 1? Read it here: Analytic insights along the customer journey (part 1)


See more from Beyond the Arc

Why link the customer journey with analytics

Personas and journey maps: strategic tools for improving customer experience

Data Science

Beyond the Arc has resident experts in data science, passionate about customer journey analytics. They specialize in the use of statistics and machine learning, delivering actionable business insights to drive improvements in customer acquisition, churn, cross-sell, segmentation, loyalty, and revenue optimization.

Interested in learning more about financial services predictive analytics? Let’s start a conversation.