The Customer Experience Analytics to Increase Customer Lifetime Value and Retention


  • The goal of customer experience (CX) analytics is to systemically drive improvements to the CX. Even small improvements are shown to deliver significant and sustained financial benefits.
  • Improved CXs directly impact customer lifetime value and customer retention; two dominant drivers of company revenue growth.
  • Analytics connect data, insights, action and outcomes. This empowers people to proactively and confidently alter business processes for improved CXs and company profits.
Johnny Grow Revenue Growth Consulting

Customer experience analytics are needed to measure and improve the CX and achieve the downstream financial effects of increased customer lifetime value (CLV) and retention.

To accomplish these tasks the analytics must do 3 things:

  1. Apply data to measure the CX – at each customer touch point and a consolidated score for each process and customer
  2. Harvest large volumes of customer data to pinpoint the factors that most influence the CX
  3. Deliver data-driven and actionable recommendations to the people empowered to improve CXs

To do these tasks systemically and at scale, we apply a Customer Experience framework. Below are three customer experience analytics best practices from the framework that you can apply for your business.


Measure the CX

Customer experience research found that most companies measure customer satisfaction (CSAT), net promoter score (NPS) and other indirect performance metrics, but do not calculate a customer experience score. And as the old adage says, if you can't measure it, you can't manage it.

The two primary reasons companies are without CX scores are that their CRM systems don't provide it and they don't know how to calculate it. Here's how to fix both those challenges.

First, define the CX score calculation.

In working with clients for more than two decades to design CX analytics, I've learned that companies have very good reasons to calculate CX scores differently, in part because they track different data and have different goals.

But I've also found that CX scores are best calculated using weighted factors that are most associated with financial outcomes. That means the factors that most impact customer lifetime value and customer churn.

Some examples include purchase recency, frequency and monetary amount (RFM), customer engagement (including AES score, online behaviors and CRM activities) and satisfaction (CSAT, NPS). It's also important to understand the causes of customer churn, measure them and include these metrics.

While CX scores are normally numeric values lying in a range from 1 to 10, they can also be more generally characterized into tiered values such as Very Satisfied, Satisfied, Neutral, Dissatisfied and Flight Risk.

While a CX score is calculated for each customer, the results should also roll up to customer segments to be reported at an aggregate level. You want to see if the experience varies by type of customer, product, line of business or geography.

However you define it, you need to then next use your CRM software to calculate the score, make it visible at the account record, and assign some workflow rules to make the data actionable.

The CRM system will monitor customer behaviors, digital footprints, engagement activities and other customer data to update CX scores, and when necessary, send alert notifications for downward movements or variances that need quick resolution.


Transform Data into Insights

Data is an asset. But to yield value, the data must be converted from a raw material to a finished product of information or insight. Only then will the data provide answers to powerful questions such as which customer interactions elevate or deteriorate CXs, and by how much?

Simple information reporting can be created with queries and reports accessing CRM software tables and columns. However, for more powerful insights you will need to create a data model.

You will also need to define the data extract, transform and load (ETL) process and supporting tools. Data extraction retrieves data from defined source locations. Data transformation filters, cleans, modifies, normalizes, appends, formats or otherwise processes data. Data load inserts transformed data to a destination, normally into a presentation-ready format, so it can be acted upon.

Customer Experience Data Transformation
Customer Experience Data Transformation Pipeline

To engage your staff, you need to evolve from simple or broad information content to meaningful insights. You need to provide information that customer facing staff don't know but need to know and advance reporting from being merely interesting to inducing action.

The point of creating Insights is key. Insights are not data, facts or statistics, these are all knowledge. Insights are the reasons, behaviors or learning behind the data, facts or statistics. The dictionary defines the word Insight as "seeing below the surface". It's new learning, something that teaches and induces action.

The most successful companies are defined by their ability to collect and convert data into insights that drive differentiating customer experiences. They apply analytics to make insights actionable at every customer interaction or customer decision point, that is those points where customers choose whether to do business with the company (what we often call customer moments of truth).

Converting data into insights is a complex undertaking which is why those who succeed will achieve competitive advantage over those who don't.


Use the Right Customer Experience Tools

The goal here is to identify the best tools to convert raw data into actionable insights and route those insights to the people that can use them to remedy a variance, implement a course correction, make a more informed decision, or otherwise improve a CX.

And the two most effective tools are dashboards and predictive analytics.


Customer experience dashboards deliver the right information to the right person at the right time. That means the people empowered to better engage customers, solve for customers, delight customers, and grow customer lifetime value. And that means pretty much every customer facing staff person.

Good dashboards focus on the most important customer experience metrics and prioritize that information based on what's most important to each user. They show what should be done, in a sequenced order, to aid time management, create a work rhythm cadence and maximize staff productivity.

Customer Experience Dashboard
Customer Experience Dashboard

Predictive Analytics

Predictive analytics shift information reporting from hindsight to foresight. They apply propensity or pro forma models to show how a combination of customer interactions will improve CXs and how improved CXs will impact company revenue growth.

Customer Experience Predictive Analytics
Customer Experience Predictive Analytics

The below dashboard is a Johnny Grow predictive model that shows how lower levels of execution contribute to both customer measures and the company's priorities.

Customer Experience Predictive Model
Customer Experience Predictive Model

And it's not just predictive, it's interactive to support dynamic modeling and What-If scenarios.

This model can forecast the company's financial impact if you were to improve customer engagement, lower customer churn, elevate the CX or modify other actions. Or on the flip, the model will show the how much the company will lose if it doesn't improve these things.

The predictive pyramid is helpful because no customer program operates in a vacuum. Each has cascading effects that impact many areas and those impacts must be considered when making tradeoffs. This visualization is helpful in determining where to invest your limited time to achieve the biggest uplift.

See how Customer Experience Analytics can be used to delight customers, increase Customer Lifetime Value and improve customer retention.

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