As many professionals charged with managing the customer experience at their organization will tell you, being able to effectively leverage Root Cause Analysis (RCA) can be crucial for identifying and addressing customer issues. Using RCA to look for common themes can help solve issues faster, as well as alert all the necessary departments when there is an issue.
However, is using root cause analysis a reliable way to leverage customer data for real, measurable, impactful change? Well, it depends on what type of decision or action you need.
Root cause analysis works great for simple “find and fix” situations. Customer feedback (aka Voice of Customer) can be filtered by looking at comments left only by people who had negative outcomes, such as failure to purchase. Applying open-ended comment analysis from text analytics, customer situations can be categorized by frequency, themes, sentiment, or other useful categories depending on your business. This kind of categorization streamlines the analysis process — making it easy to identify trends or pinpoint a specific problem, like an inability to accomplish a task. So for simple issue identification, such as finding a broken link or fixing a malfunctioning button, RCA is a great tool for immediate value.
RCA is less appropriate to use in isolation when you’re trying to make more strategic, wider-ranging decisions. RCA has its place and its value, but it’s also easy to overlook inherent limitations of root cause analysis.
For example, suppose one group of customers keeps commenting on problems with navigation and another group is making comments about site functionality. All the comments are valid issues, and perhaps both are issues your company has been contemplating for its road map.
But, how do you know which area should be prioritized? Either of the improvements require resources to deploy and will have a cost to the business. So do you invest in the area that had the most comments? Should you invest in the area users engage with most often? RCA is less useful in helping you prioritize time, resources, and investments toward issues with the biggest impact. In fact, RCA can actually be misleading.
By definition and design, RCA focuses on preventing negative outcomes. It does not provide a way to maximize future outcomes as a result of a change. Making effective strategic decisions requires a more holistic approach to know how changes will positively impact the customer journey—which ones to make first, how to make them, and whether or not the change resulted in the desired effect.
Where RCA falls short, a causal customer experience model can take over. Strategic analysis to identify and prioritize areas of improvement requires a model with a cause and effect relationship.
Put more simply: every interaction your customer has with your brand impacts their immediate and future behaviors toward your product, brand, or service. This is a causal relationship that can be quantified using proper research techniques, and individual elements of the experience that have the most impact can be easily isolated. Maybe the biggest driver of an overall experience is that broken link identified by RCA, or maybe it’s something else. You need to know. An example of this model for a store might look like this:
Using a series of equation modeling techniques, it becomes possible to isolate the impact of each driver to the experience and the impact of the experience to future behaviors. This gives companies a predictive approach to voice of customer data that informs strategic decision-making for how best to allocate resources for a greater return.