No one says, “Hey, let’s haphazardly collect some data from some customers, glance at the numbers, guess at what it might mean, and then either do something or do nothing.” That is, unfortunately, how many companies handle their customer experience research and strategy. No standards. No discipline. No rigor. No science. They might as well use a Ouija board. Science, by contrast, is built on the foundation of a systematic process of observation/measurement, analysis, testing, and logic. While we usually think of this as the “stuff” of the natural sciences, the same approach applies to business, economics, and human behavior, each of which is involved in customer experience.
The idea of using the scientific method in business may seem far-fetched to some. The real-world environment is more demanding than the controlled environment of a laboratory, where do-overs are possible. In business, there are no do-overs except perhaps for golfers with Mulligans. The stakes are high; the cost of failure can be measured in terms of lost revenues, customers, and opportunities. Conversely, the value of success is reflected in the gains in these critical metrics. By employing a scientific approach to Customer Experience (CX) research, businesses can enhance their chances of success. Let’s explore how CX research can be systematized like the scientific method using a structured six-step process.
Identify the Issue, Problem, or Question
The first step in systematizing CX research is to identify the issue, problem, or question at hand. This step is crucial because accurately identifying the issue sets the stage for every subsequent step in the process. The issue can be broad, such as “how can we improve the customer experience?” or more focused, like “we are seeing more churn among customers than expected.” It can even be hyper-specific, such as “too many visitors to our website are abandoning their shopping carts or simply not buying anything.” Clearly defining the issue helps in framing the research question more precisely.
Identifying the issue might seem straightforward, but it requires careful consideration and often a multidisciplinary approach. Cross-functional teams can provide diverse perspectives that might uncover underlying issues not apparent at first glance. For example, while increased churn rates might initially seem like an issue specific to the customer service department, it could also be connected to product features, pricing strategies, or the overall market environment. By bringing in insights from various departments such as marketing, sales, product development, and customer support, a more comprehensive understanding of the issue can be achieved. This holistic approach is the first step toward a systematic and scientific CX research process.
Define the Question Clearly
Once the issue is identified, the next critical step is to define the question clearly. This step might seem redundant, but it is essential for narrowing the focus of your research to obtain actionable insights. The customer experience, churn, or shopping cart abandonment mentioned earlier are examples of outcomes or dependent variables you want to explain or predict. Clearly defining what you want to investigate ensures you understand the potential causes or input, also known as independent variables, that might explain or predict these outcomes.
Defining the question requires specificity and clarity. For example, if you’re facing higher customer churn rates, your question might be, “What factors influence customer churn rates among our premium subscription users?” This level of specificity helps in targeting the right variables and designing the research methods accordingly. Ambiguities in the research question can lead to misinterpretation of data and misguided strategies. A well-defined question acts as a guiding star for the research process, keeping the focus sharp and the methods aligned with the objective. Understanding that you don’t directly “fix” the outcome but improve performance on variables that drive the outcome is a pivotal realization in this step. This mindset will guide the subsequent data collection and analysis processes.
Gather the Necessary Data
With the question precisely defined, the next step in systematizing CX research is gathering the necessary data to address that question. This step involves collecting both qualitative and quantitative data, although quantitative data is more often emphasized due to its structured nature. Do you already have the customer experience data, be it from internal or external sources, needed to address these issues? If not, it’s time to design a structured approach to gather the necessary information. Qualitative feedback can provide essential context and insights, but quantitative data is typically more reliable for systematic analysis.
Designing a structured approach for data collection often involves creating detailed surveys, conducting focus groups, or utilizing existing customer service interactions and feedback forms. The key is to gather data that is representative and comprehensive enough to answer the research question definitively. This might require investing in advanced data collection tools or platforms that can aggregate and analyze large volumes of customer data efficiently. Regardless of the methods employed, the objective should be to collect data that can be directly linked to the issue or question at hand, providing a solid foundation for the subsequent analytical steps.
Combine Input and Outcome Data
The fourth step in the process involves integrating the data on the inputs and outcomes. This means linking the customer experience data (independent variables) that have been collected with operational, outcome, or behavioral data (dependent variables). This merging of data sets forms the bridge between experiential insights and operational metrics. For instance, customer satisfaction scores might be linked with purchase frequency, average order value, or churn rates. This connection is crucial because it allows for the measurement of the impact of various inputs on the outcomes, providing a clearer picture of what drives certain customer behaviors.
This step often involves sophisticated data analytics tools and techniques to ensure accurate and meaningful integration. It might also require collaborating with data scientists or analysts who can employ regression-based Key Driver Analysis to analyze the relationships between variables. These models help identify, quantify, and prioritize the inputs or variables that have the most significant impact on the outcomes. While the results might only explain a portion of the outcome variance (typically 50%-70%), they still provide valuable insights that can guide strategic decision-making. Understanding that human and organizational behaviors are inherently complex and not easily predictable helps set realistic expectations for this stage of the process.
Draw Conclusions, Create Plans, and Take Action
Drawing conclusions based on the data and models is where many companies falter. The data, while rich in insights, is inert by itself and does not automatically translate into action. This step involves interpreting the results and formulating actionable plans based on those interpretations. For instance, if the data suggests that making customers feel more valued and connected is critical for improving customer experience, the next logical step would be to develop strategies that enhance customer engagement and loyalty. This might include personalized communication, special offers, or loyalty programs.
However, the insights derived from data do not always point to straightforward solutions. Companies often need to brainstorm and develop innovative ways to implement the strategies suggested by the data. This might require cross-functional collaboration, drawing insights from frontline employees, or even looking at competitor strategies for inspiration. Leadership often expects detailed, prescriptive actions from data analysis, such as sending personalized emails or providing specific training for staff. But it’s important to remember that the models guide what needs to be accomplished, not necessarily how to do it. Testing different approaches in a controlled, iterative manner can help refine strategies before full-scale implementation.
Measure the Impact and Reassess
Measuring the impact of the implemented strategies and revisiting the entire process to reassess and adjust is the final, crucial step. This involves tracking the relevant metrics to see if the actions taken have resulted in the desired outcomes. It’s essential to maintain a continuous feedback loop where the effectiveness of strategies is evaluated and improved over time. By consistently measuring the impact and reassessing, businesses can ensure that their customer experience efforts remain aligned with their goals and responsive to evolving customer needs. This ongoing process helps to refine strategies and solidify a scientific approach to CX research and implementation.