In a series of two articles, we examine at the state of current CX analytics and identify the gaps in insight left by traditional metrics. And we look at a new kind of metric that can fill those gaps in our understanding of CX performance.

Channel engagement metrics

This diverse set customer engagement measures includes digital metrics such as email engagement, page views and digital conversion funnels; contact centre KPIs such as first contact resolution ratios, average resolution times, complaint volumes; in-store engagement metrics such as footfall, sales volume and mystery shop audits.

Digital engagement metrics can be developed to a high degree of granularity with a well-integrated and comprehensive tech stack.

Reliance on internally available data means that bespoke predictive models can be made, which will more accurately reflect the specifics of an organization’s market, sales and services. The line between commercials and engagement can be more robustly modelled and tested.

Challenges of channel engagement metrics

Data overwhelm
Rapidly improving data-capture technology has led to an ocean of data being available to companies. But identifying the metrics that matter — which data is significant — can be a challenge.

Siloed data
Different technology is used for different metrics; Google Analytics for web views, EMS for email engagement and so on. But linking these channels of engagement is still superficial. A customer who appears not to be engaging with emails might be responding to social media advertising, but this link can be invisible to companies. Following a customer through their journey to purchase is challenging.

Invisible customers
With physical store captured metrics, the chief challenge is understanding what led to a buyer walking out without purchasing when they cannot be profiled or identified directly.

Customer lifecycle measurement

Tracking the complete customer journey from initial awareness through to repeat custom includes purchase behaviour metrics, time series metrics and transactional KPIs such as RFM, retention and churn rates.

These metrics are predictive at cohort level and great for generating transaction-based target segments geared towards retention. This is particularly valuable in organisations where multiple variants exist at the SKU level.

Challenges of customer lifecycle measurement

Only predictive at macro level
Customer lifecycle is predictive to an extent at the cohort level, depending on volumes of purchasers relative to potential market size, but not at an individual level.

Information gaps
Purchase behaviour data leaves the root cause for purchase unknown.

Limited to known customers
CRM and Martech stacks generally gather data from customers who have raised a flag to make themselves known. But while this technology is well designed for this purpose, it raises challenges when you need to move to wider view. To achieve growth, you need to work beyond the bounds of the identifiable customer, appealing to people who are not yet customers. But you can only hazard a guess on how to do this based on the data you have on known customers.

Limited acquisition strategy
These metrics are not suitable for generating acquisition hypotheses, particularly in unpredictable markets or where a lot of competitive force exists.

Effect of competitive entrants
Customer lifecycle measurement can’t predict or compensate for the effect of new competitive entrants to market.

Doesn’t account for changes in context
The underlying hypothesis for growth with these metrics relies on past performance being indicative of future behaviour. It requires constancy in purchase context and customer needstate over time, which may not exist.

Additional analytics required
For creative activations, transaction data needs to be heavily supplemented with cohort research, emotional engagement and attitudinal research.

A new metric to plug the gaps

Why do customers choose one retailer over another? What really influences their choices at the moment of decision? These are the questions where current CX metrics leave you guessing.

Relative Attractiveness (RA), Uncrowd’s new analytics metric, measures how retailers or brands perform at the point of customer decision, observing and analysing the customer experience environment itself. RA does not rely on canvassing customer opinion; instead, it uses objective observation of the CX environment, bypassing the bias inherent in customer surveys.

As a result, RA is a new category of customer analytics tool, plugging those gaps in insight across all measures. RA delivers an overall market view, describing how different competitors show up at decision points.

RA can help you understand why your customers choose you, or a competitor.

Uncrowd is on a mission to improve every customer experience on the planet through a unique combination of CX observation, quantitative measurement and comparative results. Our data is objective, empirical, and always shows your next best action.