While most companies see the value of Customer Success and are at least vocally enthusiastic about adopting Customer Success principles in their operations and workflow, enthusiasm alone cannot carry the day. Implementing a Customer Success operation is hard work and many companies fail or are disillusioned when it comes to actually buckling down to it. Learn from them and plan for success by reading about these four common mistakes:
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No Executive Buy-In For Customer Success
Customer Success is not something that can be implemented in a silo, by itself. There is virtually no area of your business that will remain unaffected – in the early days engineering and operations will need to clean up and provide data that is the basis for which changes will be made to customer support, billing, even the product itself. Since it’s not a new function but a modification of existing functions, this will not happen without significant, explicit behavioural change. And that requires explicit leadership. Even worse than not providing that leadership is pretending to, or making the right noises and then not following through with the required support. For example, if your newly minted Customer Success Team needs your Customer Support department to pick up some on-boarding slack, and the Support department feels like this isn’t their job, you need to step in and convince them otherwise. Without that executive buy-in, nothing will happen.
Most companies either don’t track important data or, if they do track the data, don’t present it in a hygienic manner. Before you can see the real picture of where your customers are in terms of likelihood to churn, or opportunity to upgrade, data of a sufficient quality needs to be loaded and collated. This isn’t hyperbole, it’s literally the minimum required step before you can do anything. Almost every action you take will depend on the existence of data that can be examined to check your hypothesis.
Presenting a business app that can use the data in a powerful new way can often serve as justification for that data-cleanup process. But that process has to be accounted for because it will eat up time and resources. When making the business case for predictive analytics tools, be sure to incorporate data cleanup as part of the implementation.
Trying To Do Too Much Too Early
A lot of executive teams see the high up-front cost of cleaning up data, the massive headaches caused by aligning existing systems with a new Customer Success philosophy and think that all this work needs to pay off immediately. The fact is that because implementing the Customer Success philosophy is a human, psychological change and not a systems change, companies react better to slow, incremental changes rather than large immediate ones. Especially with larger companies, it might be better to take a more sanguine view and focus on iterative, time-bound approaches where each phase of change is designed to deliver specific business value. Even if the value is small, it should be immediately visible. An example would be cleaning up data to see at-risk customers. If that’s the only task for the quarter, it makes it a lot more palatable.
Leaning On Predictive Analytics
Predictive analytics tools will tell you everything you need to know about your customers and leads, but they will not solve anything. They are at best prescriptive tools that indicate a problem area. It then takes your ingenuity and your tenacity to dive in and try to find out the cause and solution to that problem.
Unfortunately, because predictive analytics tools are marketed like they’re the best thing since sliced bread, there’s a misconception that just by installing the tool you’ll instantly see benefits. Not without acting on the insights the tool provides!