“The future is independent of the past, given the present”
-Andrey Markov
Big Data has informed our world with insights and has brought technology closer to people and customers in meaningful ways. Not only are we able to understand more about behaviors and how to personalize experience but we are actually able to learn from these behaviors and predict what can reasonably occur next. The opening quote from Markov describes a memoryless property within statistics that was initially discounted in the late 1800’s when first theorized but has in the Big Data age enjoyed a renaissance of sorts. From speech recognition (as an example) to other machine learning applications we have come to understand a probabilistic future not in terms of a known past but a catalogued present. Through automation and deep learning we are able to rationalize the present as it occurs and extrapolate a broader real-time sample set that informs a probabilistic future.
The above has enormous reach and implication on how we build software and solutions that will over time enrich experience based on a more personalized and known set of user behaviors. As it stands today, if you are not leveraging these predictive models you are missing an opportunity to understand what is happening as it happens. In the world of SaaS, this methodology is a key tool to help align your technology and business focus on real derived value and where you can advance customers in the success of your solution. Today customer success teams are focused on the front lines of building blueprints, scorecards, and working with customers without often having key data and insights that are needed to help progress customers along their journey. Broad general trends are followed and all too often time is spent reacting to customer needs and expectations which expire as soon as they are considered. The customer journey evolves in real-time which means customer success teams need to stay ahead of the curve, to anticipate what can occur next and have an answer. This goes beyond understanding NPS scores and retention (also important) to focusing on workflow completion percentages, abandonment rates, and other key conversions against outcomes. It is these points of friction that require the most consideration and often indicate areas that need direct engagement or complete redesign.
It’s an exciting time to consider the possibilities of recasting traditional customer lifecycles and leveraging Big Data to further our insights and proximity to customers. The time is now to innovate and build new strategies based on predictive measures and to push the boundaries of technology and what it can help individuals and organizations achieve. Is your organization taking a leading position or on its back foot?
work-in-progress 