Customer Success is arguably one of the more significant shifts in business thinking today. More and more companies are making the shift from a customer service only approach (reacting to the needs of customers) to customer success (anticipating the needs of customers, building long term relationships with them and ensuring they maximize their value from your product or service).

One of the many reasons this shift has come about is the emergence of new technologies that has allowed a more meaningful connection with the customer. Software that empowers your staff to get a 360 degree view of all customer touch points is not anything new, there are a plethora of business intelligence solutions that have been out there for years. What’s new in this space are the tools and solutions that  give you the ability to be more proactive in managing the customer health and satisfaction.

At its core, is the amalgamation of different types of customer data that help us start to piece together the customer success puzzle. We’re collectively getting better at creating consolidated data files on our customers even though that data is coming from multiple areas across your SMB or Enterprise – CRM systems, point of sale systems, marketing tools, product usage, customer service and other myriad sources.

The limitation however is that all this data summarizes past behaviour. They represent lagging indicators on customers that have already left, requiring you to have to then interpret to improve future customer retention.

So how successful can you really be at providing customer success?

Any considered strategy to improve customer success needs to be supported by predictive analytics. Harnessing the power of  machine learning can give you insights that enable you to course-correct negative future outcomes. Predictive analytics can help forecast future customer behaviour which can in turn, provide your Customer Success teams with the missing pieces of the customer success puzzle. Knowing, for example, when your customer is about to leave, before they actually do, puts you at a distinct advantage to try and convince that customer to stay.

So, when you’re making the strategic shift to embrace customer success as part of your overall business strategy, ensure you have the right solutions in place. Tools that deliver insights on what the customer has done in the past as well as what they are highly likely to do in the future.


Steve Emanouel is Managing Director at Spyglaz and is based in the Melbourne, Australia office. He is passionate about bringing change management to organizations that are prioritizing customer retention as a key objective.


Spyglaz delivers churn management software. We use machine learning algorithms to identify which customers you’re likely to lose before you actually lose them. Our proprietary algorithms analyze your customer data and identify trends that have historically led to customer loss.