[Micro Podcast] Customer Success teams can become more customer-centric by better understanding customer data—in all its variety and velocity—and having a clear line of sight on what they want.
The point about data is not the data, the point about data is the ability to use it to make good decisions, and that’s not as easy as it sounds either.
So… so Prithwi what about in Customer Success Management; what’s the impact of this rise of the digital world and, everything being now digital, being stored as data; how is this impacting Customer Success and how is it helping Customer Success?
You see the differentiation or the competitive advantage one wants to achieve by becoming more customer-centric… starts off by understanding the customer even more or as much as you can. All that means is you are trying to now get and store and analyze more and more information and data about the customer at a very foundational level then decide on… you know… what makes sense to you and your business and depending on which direction you are skewing and then drive appropriately your business processes within the organization triggered by that data and the underlying intelligence.
Now within Customer Success, lot of moving parts, so… You know the variety of data is very high and so is the velocity of data… right? So, the quantum of data increasing day on day in a particular Customer Success function will be very very high.
A bigger challenge is with the variety because you have financial data, you have sentiment data, you have customer data, you have… you know… value-related data, you have stakeholder-related data, you have touch point-related data and so on and so forth. You know all your customer interactions, contractual needs and stuff like that. Now… to a Customer Success team, at times it becomes a challenge actually, whether you know too much of data is good or too much of data is bad? So, you have to take that optimal path… what is right depending on you know where you are in your organization. So the impact, Rick, in short, is very high provided you have a clear line of sight on you know what you want to do with that data and how you want to approach sieving through this large ocean of data, if you will.