Energize Your CX Program with New Methods for Old School Root Cause Analysis

I think most would agree that the overarching purpose of any Customer Experience (CX) or Voice of the Customer (VoC) program is the identification and resolution of points in the customer journey that have a negative impact on brand perception. When a customer has a bad experience, there’s almost always an underlying reason that can be pinpointed. That reason could be a poor experience with a product, a support function, a company policy or even a PR statement like a blog, article or advertisement.

Getting to the heart of the issue is paramount for the success of these CX/VoC teams. You don’t have to be J. Edwards Deming to know that identifying the actual problem is the most important step in resolving it. With that said, why is getting to root cause so difficult?

Root Cause… just saying it out loud feels so 1990s. I could offer up a whole dissertation about TQM or “The 5 Whys” but I won’t because everyone reading this blog knows that root cause analysis requires deep drill down well past the surface. But that’s really the issue facing CX/VoC teams today – how does one get past the high-level themes, that are paraded around today as insights, to find the true root cause of customer issues? When I say the ‘true root cause,’ I mean something so concrete that action and accountability can be tagged to it. A team can only affect change if it can identify the root cause of an issue and the proper course of action to resolve it. Otherwise, they’re left discussing “interesting” findings, or focusing on symptoms of a larger problem; neither of which will result in positive change.

 

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What does root cause look like through a CX/VoC lens? Typically, CX/VoC teams are looking for macro-level trends in customer interaction data to detect inflection points that can be indicative of a change or problem. The next step is to review the metadata over that timeframe to see if there is a particular call or chat type, social channel, customer cohort or marketing campaign that is driving the change. Next, the unstructured data from the interactions must be evaluated for contextual information. This can consist of sentiment analysis and specific topics that are tagged to interactions. This aggregated data can reveal the “what” and the “why” of the trending issue. The “what” is usually either a specific product/service or a support function such as billing, shipping or digital. The “why” is the actual problem the customers are encountering. This could be a defect in a product, a service disruption, an inaccurate bill, missing shipment, a problem on the website, etc.

Analytic tools that will provide the level of granularity needed to identify the true “what” and “why” are the key to getting to this point. It’s one thing to know that customers are complaining about shoes falling apart, but the path to true root cause requires knowing the model of the shoe, the part of the shoe that is defective (sole, toe box, heel, etc.), the specific material that has the problem, the description of the problem (rip, tear, falling off, etc.) and even the usage of the customers (serious runner, casual wearer, etc.). That’s the context that is necessary to begin framing root cause.

After understanding the model of shoe, part of the shoe, material, description, customer usage, and the number of complaints matching the criteria at hand, the CX/VoC team is ready to hand off the data to a product team that can identify the actual flaw in the shoe’s design and make the necessary changes.

As stated above, it’s important to have the right analytics software to make this process fast and repeatable. Topbox’s Conversation Analytics platform aggregates conversations from every channel and classifies them with rule-based and AI techniques to provide that level of granularity that’s so critical to root cause analytics. If your CX/VoC team is struggling with root cause, give us a call!

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