Customer Experience Analytics: How to analyze behavior

What is customer experience, and why do we need to analyze it?

Customer experience is the sum of all a customer’s interactions over time. Naturally, some of these will be more significant or positive than others, and recency is an important factor. Everything considered, the experience spans the lifetime of a relationship: starting with early awareness, through to the engagement stages, and across the active use of a product or service. For the most part, customers now expect experiences that meet or exceed their expectations – it’s the basis for them to become satisfied and loyal. And of course, valuable experiences create brand advocates who then support organic acquisition. So, to understand how our customers are interacting and engaging across touchpoints, it’s vital for us to understand the experiences we’re delivering. And for this, we employ customer experience analytics.

The landscape has evolved: 2020’s events accelerated the proliferation of digital channels in particular, confirming that ‘behavior’ isn’t a static concept. The instantaneous, always-on nature of digital channels has made consumers far more aware of the sheer number of choices available. Additionally, the ‘digital shift’ means we must better understand customer wants and needs far earlier in the lifecycle and across all channels. To foster continuous engagement and drive customer loyalty, brands are therefore compelled to ensure that all connections deliver appropriate and relevant experiences, tailored to individual needs.

We measure performance against expectations both from a customer and a brand perspective. Many customer experience analytics solutions tend to fall short here, merely providing static snapshots of whether a customer had a positive experience in the sales process. But things need to be a little more nuanced; what is the cost of delivering experiences (discounts and incentives) vs. the positive impacts on customer experience (think saves them time or rewards them)?

In this article, we’ll explore why customer experience analytics solutions are often limited in their approaches. What needs adapting to help brands appropriately understand and support positive engagement to create better business outcomes?

Why current approaches to customer experience analytics aren’t working

Until recently, it’s been difficult to deliver seamless, end-to-end experiences across the customer lifecycle. The gap between customer expectations and the reality of those we deliver has widened as we struggled to interpret and identify customer wants and needs in an increasingly complicated network of channels. No surprise that organizations often misalign their value propositions to customer expectations.

The reality is that very few brands are built to engage effectively with consumers on their terms. This is understandable, partly because they were created to support products and organized by divisions and functions. But a more comprehensive, holistic and customer-led approach is needed – and clearly, we must analyze customer interactions to end this trend. The focus of “experience analytics” should then consider the real experiences a customer has, rather than what we think these should be.

An increasing number of businesses are investing in how they understand their customers, with many drawing on customer experience analytics to fulfill this. CX analytics is a very powerful concept in principle. But there’s a challenge: the outcome tends to be diagnostic in nature – as opposed to driving action and change. Put another way, many analytics methods focus only on what’s wrong, rather than how those experiences can be improved.

This situation is exacerbated by many brands asking solely for customer feedback on a product or service, rather than taking time to build an improved understanding of how these impact on the progress customers are trying to make in their lives (so-called ‘jobs to be done’). Often, when we focus on the buying experience (over whether a product meets its intended needs), we observe less relevant data and analytics. The organization will then (incorrectly) use the data collected and conclusions drawn as a foundation of its CX analytics, encouraged to pursue inappropriate corrective action.

The problems impacting many CX analytics approaches can be boiled down into four areas

  1. Only focusing on experiences during direct interactions (owned communication touchpoints)
  2. Focusing on analyzing experiences at an individual touchpoint level, instead of analyzing the entire customer journey
  3. An inability to measure how actual experiences during the ‘product use phase’ correlate to the initial customer intent
  4. Separation of measuring performance towards business goals and delivering value based on customer expectations into two discrete practices

Let’s dive into these in more detail:

1. Only focusing on experiences during direct interactions

There is an overwhelming emphasis on tracking and analyzing behaviors and experiences that occur when customers are directly interacting with a brand – like a conversation with customer service, or making a purchase. (This is distinct from indirect interactions, which – for the purposes of this post – occur when using a product.)

The focus of CX analytics programs, therefore, often relies heavily on the interpretation of these touchpoint experiences. However, since they only represent a small part of a customer’s engagement with a brand, this misses the smaller, indirect touchpoints that often happen in the initial awareness stages. This might be seeing a post on social media or clicking on a link in the search engines.

The most prominent blind spot, though, is that brands often forget to monitor ‘use experiences’ (how a customer uses a product), therefore failing to understand if the customer’s initial intent when buying a product or service was satisfied. Crucially, does the experience match their initial expectations and desires?

Changing the focus of CX Analysis Diagram
Brands are disproportionately focused on tracking purchase journeys. But clearly, the entire journey matters. So, this can overlook the genuine intent of individual customers – or whether this is being satisfied. The solution is to refocus efforts on tracking the experiences customers are having with our products and services to frame our customer experience analytics programs.

This forces us to consider the balance between our need to sell products or services against a desire to provide consumers with ways and means to improve their lives. Are we fulfilling their intended job? To create brand advocates, we must be delivering value that aligns with customer value expectations, at every step of the customer journey – and everywhere.

2. Measuring touchpoints (not journeys)

Traditional methods of measuring experiences (generally, one touchpoint at a time) stem from the days when first- and last-touch attribution was essential for understanding which interaction could be assigned to a successful outcome.

But times have changed. Our ability to construct journeys and their associated interactions (with Real-time Interaction Management) at this level has significantly moved the goalposts. This means we can focus on measuring CX within the context of journeys. Over a lifetime, we understand how behaviors across a journey impact associated experiences, as well as what happens after that journey. For example, ask: does the amount of time a customer spends in the ‘knowledge’ stage impact the level of support they require in the “use” stage?

3. Measuring initial intent and correlating subsequent experiences to it

Brands need to understand every visitor’s intent earlier in the journey; they also need to improve their ability to provide highly relevant ‘in-life’ experiences that are consummate with the initial expectation.

Ideally, this would include the correlation of responses to third-party ad content with a visitor’s possible intent. After all, paid media needs to resonate, so appropriate messaging should draw on everything we know about a prospect. With this in mind, we find ways to match combinations of interest and intent, mobilizing first-party channels to validate our understanding of that intent. This process allows us to deliver an end-to-end experience aligning with needs.

4. Separating business goal analysis from customer expectation analysis

We engage with customers to support their own drive to achieve business goals and satisfy customer expectations. It’s important for these to be achieved simultaneously to avoid a risk of ‘imbalance’, with the success of one being detrimental to the other. With this in mind, a key part of CX Analysis should be our ability to understand how delivering great experiences impacts the brand in achieving its goals. Appropriate correlation of CX and Business goal analysis will allow an organization to quickly identify opportunities for more appropriate alignment in the future.

Best practice for Customer Experience Analytics: a holistic approach

Clearly, current approaches to customer experience analytics can be disjointed.

A better approach is a more holistic one, where all elements that are intended to deliver value for both the brand and the customer are combined into a single analytics platform. This allows us to fully understand experiences and their associated impacts, across the full spectrum of customer engagement. The main goal is then to identify behavioral patterns that lead to delivery (or non-delivery) of value: for both the brand and the customer. From here, we can then create action plans to maximize value and minimize negative outcomes.

At a minimum, holistic analytics provides analysis capabilities to enable:

  • The gathering of behavioral journey data to discover and understand customer intent
  • Measurement of when, where, and how business goals and targets are being met along a journey
  • An Identification of when, where, and how customer value expectations are being met along the journey (e.g. through surveys or the level of a product’s adoption)

Our analysis should then provide capabilities that correlate to and understand the dependency and impacts of each of these foundational elements on one other. This makes insights easier to harness to support appropriate customer journey orchestration, improving outcomes for both the business and the customer.

Holistic Customer Experience Analytics Diagram
Customer Experience Analytics requires us to incorporate four elements

Delivering Value with Customer Experience Analytics

Analysis without action provides little value, so the focus on using a holistic platform (as described above) is to use it to identify opportunities to:

  • Identify intent earlier in the engagement lifecycle
  • Use this intent to help customers along the most relevant and appropriate journeys
  • Ensure that customer value expectations can be met along the full engagement lifecycle while delivering value for the brand. This also improves our ability to determine business goals.

With these in mind, our analysis must be delivered in a way that can enable continuous action across the full customer journey. It needs to adapt in real-time when customer behavior points to changes in intent or value expectations.

A customer journey analytics platform needs to expose relationships and correlations between its foundational elements so that a business user can clearly identify how to best deploy improvements supporting an ever-increasing level of customer expectation and the business objectives. A holistic platform will be able to correlate insights, action, and value:

Insight Action Value Diagram

The benefits of a holistic approach to customer experience analytics

The holistic approach outlined here doesn’t merely consider touchpoints as isolated moments of interaction. It looks at the entire customer journey, including both direct and indirect touchpoints. Having a big picture view like this makes it far easier to determine what customers want from us, and how we can provide them with an experience that caters to their unique sets of wants and needs. This is the essence of true customer engagement. And the benefits are far-reaching:

  • Actionable outcomes stemming from real-life data
  • A closed-loop improvement system
  • The removal of behavioral and experiential blind spots
  • An ability to shift from selling products and services to selling experiences
  • Improved satisfaction, because a customer’s intent is understood far earlier (and can be realized sooner)

Customer Experience Analytics is a big deal

Until recently, it had been challenging to deliver seamless, end-to-end experiences across the customer lifecycle – especially in a way that will allow a brand to simultaneously achieve its business objectives. But times (and technology) have changed…

With their new abilities, brands need to ensure the value exchange is a continuous exercise, and one imbued in the DNA of all employees (and partners) that engage with the customer. Selecting appropriate customer experience analytics software is a critical decision. So, it’s probably useful to have a checklist to ensure that potential CX technologies being evaluated fit with the holistic approach described in this post:

  • Can we use data from all channels – direct and indirect – in our CX analysis?
  • Does our platform allow for insights to be immediately translated into action?
  • Does our platform include artificial intelligence and machine learning capabilities to deliver complex insights? (ensure they’re more than just diagnostics – e.g. delivering behavioral cohorts)
  • Can it then identify key behaviors that correlate to outcomes, and generate predictions based on those behaviors?
  • Does our platform find the association between intent, behaviors, business goal setting, and customer value expectation setting?
  • Can our platform be an integral part of a closed loop engagement system, to ensure that continuous learning takes place – and that corrective actions are identified in near-real-time?

Our customers are our biggest assets.

So, it follows that we need the correct technologies and processes to ensure they feel appreciated and achieve the expected value throughout their lifetime. Done right, customer experience analytics will ensure we meet customer expectations – and keep it that way – while supporting the business.

If you’d like to know more about how we analyze end-to-end customer experience at scale, do get in touch

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