Published 28 Dec, 2023 5 minutes of reading
Data-driven decisions when designing your customer experiences are critical to the success of your digital products. One in three customers will leave a brand they love after just one bad experience. In Latin America, negative experiences seem to have an even stronger effect, as one in two customers said they would leave, as reported by PwC in a report on customer experience as part of their Consumer Intelligence Series. In fact, as of 2020, a Walker study found that customer experience has overtaken price and product as a key differentiator for organizations.
It’s clear that building experiences that people love are key to the success of digital products. However, many companies make their design decisions based on limited information, copying competitors, and relying on the opinions of a few stakeholders.
How can we adopt a more structured and reliable approach to making customer experience design decisions?
In this article, we’ll go over a four-phase approach to using data to improve your decisions when building your customer experiences:
Understand the problem you’re solving.
Identify the gaps in where you are, and where you want to be.
Test your user experience proposals.
Continuously improve with a solid measurement plan.
Understand the problem to solve
Every new solution, be it a public-facing website, a customer onboarding and origination system, or a transactional application, are all tied to specific business objectives. Unfortunately, that objective is not always clear across an organization.
The real problem being solved might not be apparent to the teams charged with the design and development of the solution. They can often lack the context for what the business objective is, and why it's important. The “what” provides clarity on the tasks necessary, while the “why” acts as a driving force. The “why” can seed ideas for innovation and steer design and development toward more impactful solutions and better results.
In other words, it’s important that the team not simply focus on the deliverable itself—rebuilding a landing page, or a digital onboarding—but rather on the results that need to be achieved through that new set of customer experiences. For instance, the experience design team should understand the answers to certain questions and concepts:
What are the business objectives? % increase in conversion? % increase in cross-selling opportunities? (Quantitative)
What motivates the community or segment this journey is serving? What is the objective they are trying to achieve? (Qualitative)
Who are our references and competitors? (Supplementary, aids in understanding trends, best practices, and potential opportunities; enhances the quality of decisions within a broader data-informed framework.)
This information serves as a guide for teams on what specific data to source for design considerations and define a design hierarchy. It’s also useful in defining a measurement plan for the experiences to build.
Identifying the Gaps in Where You Are, and Where You Want to Be
Once the problems to solve are well understood, it's time to source data regarding where your current experiences stand. If the product or service we are trying to build is already offered by the company in any other form, it would be useful to gather customer-driven data such as:
Client Support ticket records
Net-promoter Score records
Feedback forms records
Historical Behavioral Analytics
If there isn’t any data, a gap diagnostic can still be executed through tools and frameworks such as:
Heuristic Analysis: although not specifically a direct data-gathering technique, heuristic analysis can help evaluate an interface's design, and highlight usability issues based on predetermined guidelines. This helps identify flaws internally before involving users.
Jobs-to-be-Done(JTBD): this is a framework for gathering qualitative insights in order to better understand user motivations when engaging with a product or service. It employs various data-gathering techniques such as interviews, observations, and surveys.
Psych Framework: leverages qualitative methods like interviews, observations, and surveys to gather psychographic data. By understanding how users think, feel, and behave, designers gain insights to create experiences that deeply resonate with customers on a psychological level.
Testing UX Proposals
Once there’s a shared understanding of the business objectives and the gap analysis, you can craft a proposed solution informed by gathered data and expertise. At this stage, data can be collected through:
AI-driven Eye Tracking: Employing AI to predict user behavior offers quantitative insights into where users might focus their attention, their visual patterns, and cognitive load, aiding in optimizing designs before testing them with real users. This isn’t a modern replacement for anything, it simply helps accelerate testing.
Usability Testing: This method collects quantitative and qualitative data, observing how users interact with a design, revealing pain points and areas of improvement.
A/B Testing: Using statistical analysis on different design variations, this method generates quantitative data to determine which design performs better.
Enabling Continuous Improvement
With the constant evolution of technology and the way users interact comes a constant challenge for user experience evolution. As such, it will always be important to continuously measure and analyze your customer behavior through their interactions with your organization in order to effectively prioritize CX investments.
At this point, we want to close the cycle by being able to create a solid continuous measurement plan and connect it to organizational adjustments so that we can change and improve to meet the organizational objectives.
Data Collection Techniques:
Website Analytics: Monitoring website traffic patterns, user behavior, and engagement metrics to derive insights for iterative enhancements.
Keyword Performance Analysis: Evaluating keyword effectiveness and relevance in driving traffic and engagement on the platform.
Search Engine Visibility Metrics: Assessing the platform's visibility and ranking on search engines to optimize discoverability.
User Feedback Mechanisms: Create surveys, NPS programs, and web forms to gather user opinions and expectations for informed decision-making.
Emphasizing Iterative Improvement:
Continuous Iteration: Stress the iterative nature of improvement by consistently applying data-driven insights. Highlight the ongoing nature of this process, showcasing adaptability to user needs and market shifts.
Organizational Alignment: Connect the continuous measurement plan to organizational objectives, enabling adaptive adjustments that align with overarching goals.
Takeaways
Crafting exceptional customer experiences isn’t a shot in the dark; it’s a science. Relying on data-driven decisions rather than gut feelings or hierarchical opinions is pivotal. Understanding your business objectives and customer motivations through quantitative and qualitative data forms the cornerstone of that data. Then, identifying gaps through various analysis methods, from Heuristic to Jobs-to-be-Done frameworks, can clarify your customers’ needs. Testing proposals using a blend of AI-driven insights and user testing can then help drive design decisions based on empirical evidence.
Continuous improvement isn’t a luxury—it’s a necessity. Establishing a robust measurement plan connected to organizational objectives allows for iterative enhancements. Leveraging website analytics, user feedback mechanisms, and aligning these insights with overarching goals ensures the evolution of customer experiences remains in sync with organizational objectives and ever-changing market raising expectations.
Data-driven approaches to decision making can make or break the process of creating compelling and successful customer experiences that stand the test of time.
How Modyo can Help
The Modyo platform powers the experiences of millions of customers for large organizations across banking, insurance, wealth management, telecommunications, and e-commerce. Our Modyo experts help organizations just like yours to create innovative solutions following best practices and processes that help accelerate and evolve your digital channels.
The platform’s flexibility allows for rapid changes for testing, experiments, data capture, and application of theory like that of this article in order to drive better and better experience design based on real feedback. With tools such as Google Analytics integration, forms, Modyo Stages for multi-environment testing, and numerous other features, Modyo’s clients today are differentiating themselves through better customer experience design and achieving their objectives.
To learn more about how you can use the Modyo platform to create better customer experiences, reach out to us, and let’s discuss your next product or project.
