Why Engagement? Isn't Loyalty Enough?

For the last decade, customer loyalty and experience metrics such as Net Promoter Score, Customer Effort Score, and Forrester’s CXI have become focal points on management scorecards.  The power of these measures allowed companies to identify improvement hotspots and drive investment in customer experiences that would drive stickier, more profitable relationships.

While firms have found tremendous success in using these loyalty metrics to successfully grow customer relationships, they are lagging indicators and provide little help in providing immediate insight into whether or not improvements and initiatives are moving the needle.  It is not uncommon for improvements to customer experience to take 12-24 months to reflect in overall satisfaction measures.

Complementing loyalty measures with a more real-time understanding of how effectively you are engaging with customers can help provide immediate feedback to tailor and evolve programs and initiatives as they unfold to maximize their benefits.  Furthermore, these engagement metrics do not require surveys to capture information, and instead of relying on sampling, engagement can be tracked, measured, and targeted for improvement at a granular and detailed level for all customers.

Like loyalty measures, engagement metrics also tie to key business success metrics like Share of Wallet and Reduced Attrition. Recent research shows that engaged customers have[1]:

[1] Sources: Gallup, Fiserv


higher revenue

less likely for customers to attrite


the number of investment, insurance, and advisory product purchases

Using a system of engagement and loyalty measures can create a symbiotic and complementary set of measures that help deepen customer relationships and drive customer loyalty.

The value of engagement has been capitalized on by leaders in other industries like Facebook and Google. The more customers engage with them and the more time they spend on their sites, the more data they have about customers and their interests to be able to enhance and tailor customers’ experiences.  This virtuous cycle is what data-rich organizations should strive for.

A more targeted use of engagement metrics closely correlated with P&L-linked successes can help make engagement less of an operational metric and a metric that measures the success in driving deeper, more entrenched customers.

The Complexities in Measuring Engagement


Engagement is multi-faceted and increasingly complex in our new omni-channel world.  In an effort to truly understand the depth and breadth of customer engagement, there are a number of key questions that must be answered:  With what frequency do they interact? Do they mostly interact to manage administrative activities, or are they exploring deeper advice or product information? Does their interaction span a breadth of needs or goals, or is it focused?  What are their channel preferences?

While most organizations have the data to measure the answers to all of these questions, defining a company-unique ‘engagement lifecycle’ and analytically deriving the economic value of each aspect of the customer engagement lifecycle is critical to drive successful relationship growth with customers.



Given the breadth of the customer engagement lifecycle and proliferation of available data available to support, organizations have quickly developed robust dashboards with a plethora of detailed metrics, particularly for the digital channel. A quick Google search for ‘Engagement Metrics’ and in just the top 5 articles, there are over 20 engagement metrics a company “should be tracking” if they want “to measure engagement the right way.” Among them, metrics include engagement rate, click rates, open rate, daily active users, number of logins, bounce rate, time on site, pages per visit, conversion rate, abandonment rate, reach, shares… and too many others.

However, despite the robustness of the detail that exists, it can often be difficult to truly understand the business value or their impact on the bottom line. A 2017 survey indicates 62% of marketers are too overwhelmed with too much data and analysis, with 85% still believing they cannot utilize them fully.

With so much detail at many different levels of complexity in channels, activities, types of customers and more, understanding the bigger picture of engaging customers is a major challenge for companies.  Aggregating individual interactions across channels and the interaction lifecycle into an overall strength of customer engagement is challenging. But what if there was a simpler, holistic, and omni-channel way to measure successful engagement?

An Innovative Approach to Measuring Engagement


At Kepler Cannon, we developed our Customer Engagement Index (CEI) as a holistic way to measure the business impact of customer engagement.  To address the evolutionary  nature of how the depth and breadth of engagement evolves over time, all customers are classified into the following universal engagement classifications:


Customers are unaware of or uninterested in their relationship with the company, and have not been engaged in any form for a long time.


Customers are aware of their relationship with the company, but ignore communication. Customers may have been engaged in the past, but do not engage regularly.

Passively Engaged

Customers are aware of their relationship with the company, and are not opposed to engagement. Customers may check in regularly, but do not take action.

Actively Engaged

Customers are well aware of their relationship with the company and welcome communication. Customers take action regularly.

The score is then derived by netting the percent of customers in the bottom two classifications (Dormant and Unengaged) from the Actively Engaged’ Customers’.  This netting approach creates focus on moving customer behavior up the engagement spectrum in meaningful progressions.


Variations in product offerings, channel maturity, and value propositions create different engagement lifecycles, and as such, CEI can be tailored for each business model.  There are two aspects of CEI that benefit from Customizing customization:

Engagement Lifecycle Definition

Mapping the engagement lifecycle of a customer is foundational to ensuring that metrics are being captured throughout the full spectrum of customer interactions.  Every organization has a unique and attractive value proposition that dictates this lifecycle, and aligning on organizational goals at measurement and mapping and identifying current metrics being used to capture success at all stages.

Behavior Definition, Selection, & Classification

Once the engagement lifecycle is defined, it is necessary to narrow down to a preliminary set of engagement behaviors, these metrics should be:

  • At the Customer-level: Traced to the customer level, rather than aggregated to a segment or population.
  • Long-term Indicators: Indicative of broad behaviors, rather than related to specific actions or solutions.
  • Behaviors not Attributes: Indicative of behaviors and interactions as exhibited by the customers, rather than existing classifications based on customer behavior.

Links with business performance measures (Share of Wallet, Attrition, etc.) can then be analytical tested to shortlist the most promising measures for inclusion in the aggregate Customer Engagement Index.  Each shortlisted measure can then be modeled to identify key cutoff points for categorical or binary measures.


The customization of the Customer Engagement Index presents unique challenges for each company that adopts it. However, at the core, powerful analytics capabilities such as Latent Clustering, LASSO Regression, and even machine learning are core to enabling robust measurement, attribution, and aggregation.  Ensuring organizational collaboration and alignment exist across the customer engagement lifecycle and cross-collaboration between channel owners is required to measure ‘engagement’ in a channel agnostic, customer-focused way.

At this point, companies should be able to answer:

‘What is the contribution margin impact of moving customers from Unengaged to Actively Engaged?’

Answering this question will help tie engagement, typically driven by marketing and other customer efforts, to financial metrics that help executives make top-down decisions.

"At the core of customer loyalty, powerful analytics capabilities such as Latent Clustering, LASSO Regression, and even machine learning are essential  to enabling robust measurement, attribution, and aggregation."

Read More