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Ultimate Guide to Digital Experience Transformation

Discover how the world’s leading companies harness the latest web technologies to stay ahead of the competition and delight their customers online.

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CHAPTER ONE

What is Digital Experience Transformation?

Digital experience transformation refers to the radical improvement of a company’s websites and / or apps. Its success depends on a significant shift in how a company both thinks about and acts on customer experiences online.

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Companies can no longer afford to have a one-size-fits-all digital strategy, fed by a ‘we know what our customers want’ attitude. By investing in digital experience transformation, they can instead garner total visibility into how customers interact with their digital properties, and execute compelling personalized campaigns that upgrade customer experiences and drive customer retention as a result.

Regardless of where you sit on your digital experience transformation journey, this guide is designed to give you everything you need to better measure, understand, and improve experiences on your website or app.

Customer Experience is Your Greatest Differentiator

Customer expectations have never been higher. We live in a time where ordering immediate private transportation to an exact location involves a couple of taps on a mobile device; where complex financial transactions can be managed by tapping on that same device; and where long-term, intricate trips abroad can be organized within a one-stop, convenient virtual portal.

Thanks to brands like Amazon, Uber, and other applications we all know and love, be it in-store, on web, mobile, or native apps, modern consumers expect a fast, flawless experience – and have no problem going elsewhere to find one.

In fact, 89% of customers stop doing business with a brand after a bad experience. The result? In 2016, poor customer experiences cost US businesses $62 billion, and UK businesses $48 billion.

No wonder, then, that Gartner states that customer experience is a brand’s greatest competitive differentiator. Today’s businesses no longer simply compete on product, price, and advertising space: the experience they provide to customers has become incredibly important, in terms of brand reputation, winning initial business, and establishing customer loyalty.

89% of customers stop doing business with a brand after a bad experience. The result? In 2016, poor customer experiences cost US businesses $62 billion, and UK businesses $48 billion.

And, if customer experience is a brand’s greatest differentiator overall, then businesses cannot discount what this means when it comes to the quality of their digital offering.

CHAPTER TWO

The Need for Change

The digital landscape is chaotic. Customers land on websites and apps from all directions in contrasting emotional states, acting on unique motivations, wielding different devices with which to engage brands online.

For businesses this results in, at best, a set of substantial challenges and, at worst, a mess. How can processes be optimized if customer interactions can’t be properly measured? If conversions can’t be reliably attributed? If there’s not even any visibility into customer sentiment?

The Biggest Digital Experience Challenges Faced by Businesses

Companies that acknowledge the importance of providing good experiences face a number of key challenges, revolving around (but not limited to):

  • Strategy. Customer experience initiatives aren’t embraced by the whole organization.
  • Process. The means & methods to ensure strategy is carried out effectively are lacking.
  • Skills. Difficulties in combining marketing with analytics and technology.
  • Technology. Lack of resources leads to little visibility into digital experiences.

While these challenges are significant, they can be overcome by companies that commit to digital experience transformation. Those that increase their organization’s visibility into digital experiences – and consequently help align business processes around customer sentiment – are reaping the rewards.

Optimizing websites and apps around customer insights is a lucrative strategy for companies looking to get ahead of their competitors, but how do they do it? Success depends on two things:

1. Visibility into digital customer experiences (i.e. understanding how customers behave and feel)

2. The ability to action these insights throughout the organization (i.e. being agile with improvements)

The Value of Better Digital Experiences

LexisNexis, a leading global publisher of legal and business research, increased the year-on-year revenue of its UK website by 81% after committing to a digital experience transformation project.

UK Gambling company The Rank Group, meanwhile, achieved £2.5m uplift in revenue and 400% ROI on its investment into digital experience transformation.

While these brands are from entirely separate industries, their impressive results come from adopting the same mindset: building digital experiences on websites and apps around the customer.

Taken together, these two things represent a brand’s digital experience maturity. And they feed each other: the more visibility brands get into digital experiences, the more they can align their business around the insights that matter.

And, if visibility feeds business alignment, then alignment feeds greater investments into initiatives that grant more visibility.

For today’s businesses, committing to a digital transformation project that improves customer experience is a key priority.

Increasing digital experience maturity enables a company to get the most value out of its technology stack, save resource by breaking down departmental silos, change the perception of stakeholders when it comes to customer data, base decisions on customer sentiment – and consequently deliver fantastic digital customer experiences to improve its bottom line.

The more visibility brands get into digital experiences, the more they can align their business around the insights that matter.

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CHAPTER THREE

The Digital Experience Maturity Curve

The Digital Experience Maturity Curve outlines where companies sit when it comes to the level of sophistication they have in measuring and improving digital experiences. It ranges through five maturity phases, from companies that have minimal visibility into digital experiences and siloed departments, to those with maximum visibility and total business alignment.

The 5 Phases of Digital Experience: Maturity Curve

digital-experience-maturity-curve

The curve shows a positive correlation: as brands obtain more understanding into how customers feel, the more agile they can be with improvements. The five maturity phases are representative of the data a company collects, its alignment around this data, buy-in of its stakeholders, and the optimization philosophy it uses.

The following chapter of this guide explores each phase in detail, enabling you to discover where your company sits, as well as explore how businesses can mature to the next phase and ultimately achieve digital transformation.

CHAPTER FOUR

Quiz: Where Does Your Company Sit?

To plot where your company sits in relation to the five digital experience maturity phases outlined in the previous chapter, take the quiz below now.

Explore the phases below to learn more about what your results mean, and to garner a deeper understanding of how to advance your digital experience transformation journey to the next stage.

Phase 0: Descriptive Reporting

Businesses in phase 0 of digital experience maturity are in a premature state. They may have an analytics set up that reports on quantitative metrics such as website and app views, bounce rates, and so on, but this set up does not extend beyond the basic, out-of-the-box configuration.

Visibility into digital experiences is therefore limited to the point where it cannot really inform any decision making. Rather, it is there to satisfy occasional curiosity in terms of how many people view or spend time on different pages of the website or app, and so on.

In this phase, the strategy for optimizing digital experiences falls entirely to the opinion of key stakeholders. Website and app layouts, key messaging, personalization: everything served to the customer is based on a ‘we know what our customers want’ attitude, rather than actual customer data.

In early phases of digital experience maturity, everything served to the customer is based on a ‘we know what our customers want’ attitude, rather than actual customer data.

In order to get started on the path of digital experience maturity, the organization should dedicate a team to configuring and unpacking insights from the analytics set up.

Phase 1: Quantitative Silos

Visibility into Digital Experiences

A brand in the Quantitative Silos phase of digital experience maturity inherits the quantitative analytics set up from phase 0, but tasks a dedicated individual or team to configure it to unpack insights.

This grants shallow visibility into the behavior of website and app users. Teams use analytics technology to track reporting metrics such as website or app page views, session duration, bounce rates, conversion rates and so on, and may visualize on-page behavior – where users hover and click – with heatmaps.

This gives a general overview in terms of the ‘what’ of user behavior. Teams can investigate things like most-viewed content, where errors are occurring, where most users drop out of the funnel, and so on. This enables them to form a broad impression of website or app performance, and reveals areas that need more attention.

Business Alignment

The digital focus for brands in this phase typically lies outside experience optimization – focusing instead on driving more traffic to a website or app through high-spend advertising campaigns, for example. There is a lack of understanding around the value of experience optimization, and therefore little organizational buy-in.

In early phases of digital experience maturity, digital analytics is seen as a reporting layer on top of - rather than a fundamental part of - how the business operates.

As a result, the analyst or team of analysts attempting to unpack insights from quantitative data is siloed off from the rest of the organization: digital analytics is seen as a reporting layer on top of – rather than a fundamental part of – how the business operates.

The Difference Between Quantitative & Qualitative Data

A quick and easy way to distinguish between these two data types is to think numbers for quantitative and descriptions for qualitative. The former is objective and easy to analyze, while the latter is subjective and can be more challenging to measure at scale.

Quantitative: data expressing a certain quantity, amount, or range.

Qualitative: data describing attributes or properties.

Optimization Philosophy

For companies that refer only to quantitative data, the go-to optimization philosophy is conversion rate optimization.

At its heart, conversion rate optimization is a model that turns more website or app users into customers. Digital teams research areas of opportunity for improving particular metrics on their websites and apps - conversion rates for monetary transactions, form completions, content downloads, for example, or other metrics like page views and session duration. They then hypothesize how to improve the metric they’re focusing on, and test the suggested changes. If the changes result in uplift, roll them out; if not, start the process over.

As conversion optimization revolves around tracking specific metrics and carefully testing hypotheses, it’s therefore a good approach to ensure that – even with little insight or support from across the business – any improvements made are backed by data and based on solid scientific method.

The Conversion Rate Optimization Process

Conversion optimization cycles through four stages: research, hypothesize, test, and evaluate.

Research. The first stage in the conversion optimization process is discovering where a website or app’s problems are. This involves teams looking at which areas have high bounce rates, low conversion rates, and other key metrics.

Hypothesize. Once a team has identified where problems are, they can begin to think of fixes and improvements. These changes are then formalized into testable hypotheses, tying the performance of the change to a specific, trackable metric. For example, the basic structure of a hypothesis might look like this: ‘based on this data, I believe making this change will lead to an increase in this metric on this page.’

Test. The team tests its hypothesis by making the suggested change and then splitting website or app traffic between the original and the page with the change. The performance of each page is then tracked, and whichever one leads to more instances of desired user behavior is declared the winner.

Evaluate. If the change wins, great – roll it out. If not, it’s back to the drawing board – now armed with more data from the failed test. The whole process is continuously repeated, the idea being that websites and apps are continuously optimized – and it’s all backed by the scientific method of testing.

Challenges

Brands in the Quantitative Silos phase face a number of challenges, generally arising from a lack of visibility into digital experiences (beyond broad, shallow metrics like page views, bounce rates, and conversion rates).

This lack of insight leads to a lack of buy-in from across the business: the meaningfulness of customer data can be difficult to express when it’s in the rather inaccessible form of charts and tables. This is compounded by an enduring belief that optimizing ads – rather than on-site experiences – is the best way to drive traffic and more conversions.

Combining minimal visibility into digital experiences with little organizational buy-in results in a reliance on conversion optimization as the dominant optimization philosophy. Conversion optimization is based on scientific method, and is therefore the only way to make solid improvements with little support elsewhere.

Relying on conversion optimization, however, puts testing hypotheses at the front and center of improving digital experiences, which is inefficient in that it’s time intensive and expensive – especially if the insights hypotheses are based on are not all that compelling.

Conversion optimization focuses on optimizing conversions, not experiences: it is persuasion-centric, not customer-centric. It is therefore limited as a guiding philosophy for brands looking to improve digital experiences.

Furthermore, conversion optimization focuses on optimizing conversions, not experiences: it is persuasion-centric, not customer-centric. It is therefore limited as a guiding philosophy for brands looking to improve digital experiences.

A new model for digital experience optimization must take into account all challenges that analyst and optimization teams face. It should maintain the reasoned and scientific approach of conversion optimization – as well as its focus on the bottom line – but be applicable to any and all situations that concern the optimization of digital experiences. This model will be explored later.

The Limitations of Conversion Rate Optimization as a Long-term Strategy

Conversion rate optimization is an extremely effective short-term method when applied to something specific, like a landing page or call to action – or even a smaller satellite site that serves a single objective.

But applying it as the dominant long-term solution optimization to an entire website or app, which might serve a multitude of purposes and be presented through a deluge of different designs, is premature.

Conversion optimization does not reflect how to actually go about improving websites. It paints a rather idealistic vision of websites and apps. It implies that the only problems optimization teams face are conceptual, and require testing to be addressed.

In reality, websites and apps throw up a whole host of challenges for teams to take on. These can range from technical, like JavaScript errors and page loading speed, to experience-oriented, like smoothing the customer journey and personalization: these issues do not fit too comfortably into the conversion optimization cycle.

Pure conversion optimization is not a long-term strategy. Say an ecommerce store has a sale, reducing the price of all products by 50%. Accordingly, its conversion rate rises dramatically. Good news. But what happens when the sale ends? Customers leave, and the conversion rate returns to its former level.

Increasing conversion rates like this in the short-term has no impact on longer-term revenue drivers like customer lifetime value. This is because conversion optimization focuses on driving more immediate instances of desired user behavior. It is persuasion-centric, not customer-centric.

So, though it is related, the conversion optimization process is not directly focused on delivering better digital experiences, and therefore misses the mark when it comes to generating longerterm revenue drivers like customer loyalty.

Getting to Phase 2

While brands in the Quantitative Silos phase acknowledge the importance of measuring and acting on customer data and are in a good position to progress, they are lacking in one essential department: understanding customer behavior and sentiment.

Quantitative data provides the ‘what’ of user behavior and website performance, but focusing only on reporting metrics like page views, bounce rates, and conversion rates results in a significant knowledge gap in understanding how to really measure and improve digital experiences.

Quantitative data provides the ‘what’ of user behavior and website performance, but focusing only on reporting metrics like page views, bounce rates, and conversion rates results in a significant knowledge gap in understanding how to really measure and improve digital experiences.

This leads to running time-consuming and expensive tests based on trial and error, rather than on genuine understanding of customer behavior and sentiment.

In order to get this understanding and empathy – and the increased business buy-in that comes with it – brands in the Quantitative Silos phase should attempt to unpack qualitative data about digital experience. This manifests itself in directly asking customers for feedback, watching back session replays of actual user sessions, and more techniques that we’ll discuss in the next phase.

Phase 2: Qualitative Debates

Visibility into Digital Experiences

A brand in phase 2 of digital experience maturity complements quantitative data – the ‘what’ of website performance – with qualitative insights about customer behavior. This can take the form of direct customer feedback, video replays of user sessions, user testing, and anything else that involves deep, direct insights into customer sentiment and motivation.

Session replays are unique for providing a window into the raw user experience. Site visitors are not being influenced by tasks that you have provided them or changing their behaviour. They’re doing exactly what they’d usually do. The result? Session replays take out the bias from website evaluation, removing how you believe a website visitor will behave from the equation. None of our other conversion rate optimization tools can provide this in such a direct way.

Dustin Drees
Growth Optimization Expert
Market8

Due to the deep, rich nature of qualitative insights, they are typically from a much smaller sample size than quantitative insights. There are only so many resources teams who don’t harness data science can dedicate to watching session replays, for example – and not every customer will respond to an on-site feedback survey. Indeed, only 1 out of 26 unhappy customers actually complain. The rest just leave.

So, while brands in phase 2 of digital experience maturity have broad insight into shallow metrics about customer behavior like page views, bounce rate, and so on, they have narrow insight into deeper metrics like customer sentiment, frustration, and engagement.

Session recordings can provide incredibly in-depth insights when used effectively. In fact, they’re often the easiest and most cost-efficient way to get into the minds of your customers. Session recordings allow you to get very granular; they tell you parts of the story that, for example, heatmaps and scrollmaps may be missing.

Michael St Laurent
Optimization Strategist
WiderFunnel

Combining quantitative and qualitative is therefore a careful balancing act. One customer directly telling you they’re extremely frustrated, for example, doesn’t justify a full redesign of a checkout process that serves 100,000 daily users. How brands process and action this mix of data is an important factor in how useful they’ll find it.

Business Alignment

The nature of qualitative data – rich, direct, immediate insight into customer sentiment – means stakeholders begin to take more notice: this is visibility into the customer experience that quantitative data simply cannot provide. Perceptions are changed, empathy for users is generated, and the wheels for real change are set in motion.

As LexisNexis’s Global Head of Web Optimization, Mark Fassbender, says: “nothing makes decision makers take notice like sending them playback sessions of struggling and unhappy users.”

However, while qualitative insights add a whole new dimension to a brand’s visibility into digital customer experience, collecting both it and quantitative data can lead to a situation that exacerbates the problem of silos as outlined in the previous phase: customer service teams, for example, access and look after user feedback; UX teams conduct user testing; and analysts investigate quantitative data.

Optimization Philosophy

The dominant optimization philosophy for brands in phase 2 of digital experience maturity remains conversion rate optimization. This is because it can be difficult to reconcile opinions from different departments and datasets, so conversion optimization is adopted to ensure that any improvements made are based on solid scientific method.

The advantage brands in the Qualitative Debates phase have over those in phase 1 in using conversion optimization is that in the research phase quantitative data is now colored by qualitative insights. This helps inform hypotheses and run better tests. It also aids the evaluation phase: customer sentiment as well as traditional metrics can be used to measure the performance of new and updated content.

silos

The success of all this, however, naturally comes down to the level of access optimization teams have to different datasets, and how easy they are to reconcile.

Challenges

While certainly in a stronger position to tackle customer experience than brands in the Quantitative Silos phase of digital experience maturity, those in the Qualitative Debates phase still face a number of significant challenges.

The addition of looking at qualitative insights in website analysis is a huge boon to getting more visibility into digital experiences. It certainly encourages key stakeholders to start paying attention to the data. However, it also paves the way for an exacerbation of silos, as different departments look after and work from different datasets.

The addition of looking at qualitative insights in website analysis is a huge boon to getting more visibility into digital experiences. But simply adding more data will not fix a business structure unoptimized for agile change.

Simply adding more data will not fix a business structure unoptimized for agile change, especially if that data is not accessed by everyone and therefore difficult to reconcile.

Furthermore, while stakeholders become interested in the potential of the data, manually unpacking qualitative insights at scale is time-consuming. Limitations in resources may render the pool of available qualitative data too small in scale to enact wide organizational change.

In addition to these challenges, a reliance on conversion optimization means brands in the Qualitative Debates phase use an optimization philosophy not directly suited to improving digital experiences.

Getting to Phase 3

The main difficulty brands in the Qualitative Debates phase face is reconciling different datasets. Qualitative insights are useful, direct representations of customer sentiment, but is a small sample size enough to justify a major change to your website or app?

To be fully aligned and structured around customer experience, brands should aim to harness data science to measure qualitative insights – at quantitative scale. This paves the way for breaking out of conversion optimization into a new, more suitable model for improving digital experiences, which will be discussed in the next phase.

Phase 3: Diagnostic Alignment

Visibility into Digital Experiences

Brands in phase 3 of digital experience maturity measure qualitative insights at a quantitative scale. This is achieved through utilizing data science: automating the detection of trends and patterns in customer data. This can be done with data science departments in-house or by leveraging the right technologies.

For example, by harnessing technologies that analyze session replay data with machine-learning algorithms, teams can quantify the on-site or in-app experience of every single user.

By harnessing technologies that analyze session replay data with machine-learning algorithms, teams can quantify the on-site or in-app experience of every single user.

By focusing on measuring the right behaviors and metrics that represent customer sentiment – and having the ability to discover what is good or normal for their website or app – digital teams can automatically establish trends in user behavior and surface opportunities for improving customer experiences.

Detecting Digital Body Language: Smart New Experience Metrics

At Decibel, we applied data science to 2.2 billion user sessions, and the insights we gained into how customer digital behavior reflects the quality of their experience were compelling. Here are some of the key findings:

  • User sessions containing behaviors that indicate frustration are far less likely to convert. Sessions with multi-click behaviors have an 82% lower conversion rate, while those containing bird’s nest behaviors complete less than half as many funnel steps.
  • Sessions containing behaviors that indicate engagement have a much higher conversion rate. Both sessions with reading behavior and those with scroll engagement behavior complete over three times more goals than average users.

This kind of automated analysis combines the rich, deep customer insights of qualitative data with the broad scalability of quantitative data, granting maximum visibility into digital experiences. Brands get direct, timely insights into how customers really feel without having to ask them a question – and without having to dedicate much resource, as algorithms do the heavy lifting.

Business Alignment

With the reconciliation of quantitative and qualitative datasets comes the reconciliation of the departments who look after them: by working from one centralized pool of data, brands break down the silos between teams. The automatic detection of problems in digital experiences also frees up resource to be used more productively elsewhere.

By working from one centralized pool of data, brands break down the silos between teams. Furthermore, by combining the richness of qualitative data with the scale of quantitative, brands have compelling insights into customer experiences that cannot be ignored.

Stakeholders, by this phase, are fully bought in to the meaningfulness and importance of the data. By combining the richness of qualitative data with the scale of quantitative, brands have compelling insights into customer experiences that cannot be ignored.

Optimization Philosophy

With insights into the customer experience as compelling as those that the combination of quantitative and qualitative provide, brands in the Diagnostic Alignment phase of digital experience maturity can go beyond pure conversion optimization into more sophisticated models of digital experience optimization.

Digital Experience Optimization Models: Basic Formula

Measure. For brands in the Diagnostic Alignment phase of digital experience maturity, the measure stage is almost fully automated. Teams monitor their behavioral analytics platforms for alerts into UX or technical issues, and investigate trends in user behavior.

Understand. Once a potential problem or bottleneck in the customer experience has been surfaced, it’s time to start honing in with specific analysis. Watch back session replays and review customer feedback to gain a fuller understanding of the problem, and use quantitative analysis to investigate its scale.

Improve. The initial problem has been thoroughly examined with quantitative and qualitative analysis. There’s either a straight fix, a hypothesis for a test, or an opportunity for personalization. This experience optimization model puts the focus on the customer and removes the emphasis on guesswork from optimizing digital experiences.

Depending on the departmental set up of each organization, digital experience optimization models differ in the details. However, they each share in incorporating the best aspects of conversion optimization – a scientific approach, a focus on the bottom line – while also considering digital experiences, the optimization of which is essential to establishing customer loyalty.

Indeed, while the singular focus of conversion rate optimization is on increasing conversions – i.e. it is conversion-centric – digital experience optimization focuses mainly on improving experiences: it is customer-centric.

While the singular focus of conversion rate optimization is on increasing conversions – i.e. it is conversion-centric – digital experience optimization focuses mainly on improving experiences: it is customer-centric.

Conversion rates are of course still crucial in this model, but increasing them is recognized as a natural consequence of creating better experiences, rather than as the only thing that matters, resulting in a host of longer-term benefits.

The LexisNexis Experience Optimization Model

LexisNexismodel

Operating as an internal consultancy, the Global Digital Business team at LexisNexis facilitates change across the organization’s many digital properties. They deploy a proprietary framework, called the GRIT (Governance, Reporting, Insight, Testing), which acts as an engine for evidence-based decision making.

Challenges

While brands in the Diagnostic Alignment phase of digital experience maturity have a sophisticated set up and gather compelling insights into customer experience, challenges do remain.

Figuring out the best experience optimization model to adopt is a key one of those challenges. Every organization is different, and most don’t have the luxury of being able to build a digital set up entirely from scratch: legacy systems and processes may rear their head.

Furthermore, adopting smarter metrics to gauge digital body language and sentiment could lead to educational issues throughout the organization: configuring digital experience analytics platforms to send the right alerts at the right time requires an understanding into the kinds of insights that are useful.

Getting to Phase 4

For the most part, phase 3 is the ideal phase to aspire to when it comes to optimizing digital experiences. Analysis is automated, insights are compelling, and the business is aligned around improving digital customer experiences. How, then, can a brand progress from here?

One way is to continually enhance the experience optimization model used by the organization. Another – arguably more exciting – is to look ahead towards how technology will grant even more visibility into customer experiences in future.

Phase 4: Real-time Optimization

The next step for increased visibility into digital experiences is to move from historical to real-time insights. This means, rather than waiting for bad experiences to happen and reacting to them to ensure they don’t occur again in future, brands can proactively intervene in poor experiences as they happen.

Say, for example, a user demonstrates a certain behavior that signifies frustration, like multi-clicking on an on-page element. With real-time insights, brands can react to this immediately with tailored messaging that attempts to ease user frustration and generate positive website outcomes.

With real-time insights, rather than waiting for bad experiences to happen and reacting to them to ensure they don't occur again in future, brands can proactively intervene in poor experiences as they happen.

This kind of visibility into digital customer experience is not far away. At Decibel, our vision is for every user to have the best possible experience across every digital touchpoint. To achieve this, we’re on a mission to make digital customer experience a science.

The Amazon Way

Amazon Founder & CEO, Jeff Bezos: “If you’re competitor-focused, you have to wait until there is a competitor doing something. Being customer-focused allows you to be more pioneering…

“If there’s one reason we have done better than of our peers in the Internet space over the last six years, it is because we have focused like a laser on customer experience, and that really does matter, I think, in any business. It certainly matters online, where word of mouth is so very, very powerful.”

Powered by data science, our technology not only captures hundreds of unique, smart new metrics that truly represent user sentiment online - including mouse movements, device rotations, reading and scroll distance - it also learns what the optimum experience should be on a website or app and then automatically surfaces users who had an engaging, frustrating, or unusual experience.

With insight and alerts into what is or isn’t working, companies get the full picture of not only what is happening, but why.

Real-time insights are on the horizon - but only brands that have matured sufficiently in terms of their existing technology stack, as well as how they action insights across the organization, will be able to take full advantage of it.

Stay up to date with all the latest in online customer experience with Decibel's quarterly newsletter

CHAPTER FIVE

Achieving Digital Experience Transformation

Progressing through the phases of digital experience maturity is a key priority for brands looking to increase their market share and improve their bottom line online today, but - as we saw in the previous chapter - the journey from Descriptive Reporting to Diagnostic Alignment is not an easy one.

Simply adding more data will not fix a business structure unoptimized for agile change: aligning around and actioning existing customer insights is crucial for making the case to invest in technology that grants new ones.

Simply adding more data will not fix a business structure unoptimized for agile change: aligning around and actioning existing customer insights is crucial for making the case to invest in technology that grants new ones.

Digital Transformation at The Rank Group

Kerry Dawes, Head of Personalization Operations at The Rank Group: “A digital transformation is a significant undertaking. We knew the best way to start was to get the full picture of how all our users – not just customers – were actually experiencing our websites: information we previously had no access to. Decibel has been extremely powerful in granting us this visibility, and has changed perceptions across the business, allowing us to swiftly address areas for improvement while informing longer-term strategy.

“Its easy integrations with our analytics, personalization, and tag management technologies further equipped us to tackle our digital problems effectively – and we started to see real insights and achieve fantastic results very quickly. Without Decibel, the digital transformation that The Rank Group has undergone this year simply would not have been possible.”

Returning to an example from the introduction, The Rank Group went from phase 0 to phase 3 of digital experience maturity by committing to a year-long digital transformation project. The team supplemented quantitative analysis with qualitative insights to change the perceptions of and get buy-in from key stakeholders, whose support enabled the project to advance.

The result? The Rank Group achieved 400% return on its investment in digital experience maturity.

When it comes to digital experience maturity, the ultimate aim for brands is to get qualitative customer insights at a quantitative scale. This enables them to look beyond conversion optimization as the dominant optimization philosophy, and focus on developing a digital experience optimization model that is geared towards delivering fantastic digital experiences, as well as increasing conversion rates.

Doing so means such brands can cultivate longer-term revenue drivers like lifetime customer value, giving them a competitive advantage over companies whose sole focus is on increasing conversions in the short-term. With customer experience being the new battleground for brands, looking beyond conversion optimization is not just advisable: it’s necessary.

What phase of digital experience maturity is your business at? Whatever it is, look beyond conversion optimization, focus on being set up to optimize experiences, and reap the rewards that committing to digital experience transformation and putting the customer first can give you.

What phase of digital experience maturity is your business at? Whatever it is, look beyond conversion optimization, focus on being set up to optimize experiences, and reap the rewards that committing to digital experience transformation and putting the customer first can give you.

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