As Johnson, our visionary technical co-founder, has mentioned in “What is Qualitative Intelligence?” there is a vast difference between what qualitative and quantitative data is. The importance is the significant relationship between the insights and value that can be derived from each type and when one is able to find the correlation between the two, “it’s like adding color to a black and white painting.” - described by multiple customers from different organizations. To begin delivering Qualitative Intelligence, we are focusing on what we know best and what we believe to be one of the factors to successful, customer-centric organizations is the intelligence derived from all customer interactions. Not just tickets, phone calls, NPS, feedback forms, or churn without explanations - but every data point in aggregate. We pair this complete data set with grounded theory frameworks to eliminate as many biases as possible and guided analysis to produce the most explanatory story behind the numbers that customer-facing teams can use to replicate, stop, or dive deeper into.
One of the obvious reasons why Adaptive Pulse exists is the time it takes to arrive at these invaluable insights is painfully long (it takes approximately 3 hours of analysis for every 1 hour of data collection e.g. 60 minute customer phone call or user interview). Being the innovative and ambitious people that we are, we decided to try to leverage and create our own Natural Language Understanding models to help customer-centric professionals arrive at these critical insights faster and at scale. Potentially more detrimental if misinterpreted or missed, is another reason why we started Adaptive Pulse, it is to understand the true meaning behind the numbers. To try to get the full story that hasn’t been distorted by our own confirmation bias (the tendency to search for, interpret, favor, and recall information that confirms or supports one's prior personal beliefs or values - think of someone distorting the data to fit their narrative to promote feature x when feature y is the trending theme behind the drop in renewals) or selection bias (the selection of individuals, groups or data for analysis in such a way that proper randomization is not achieved, thereby ensuring that the sample obtained is not representative of the population intended to be analyzed - think of someone using data from customer a, b, and c who are all early adopters to launch a campaign which will not appealing to the majority of the customers) or any other type of cognitive bias humans are prone to.
Why we're passionate about what we do.
Aware of automation bias, we were hoping it would come into play in our target market, but we’ve actually had the opposing effect where our initial prospects and users were worried that leaving this to machine learning would in fact miss some crucial data analysis and insights. We should’ve known if you’re looking into us, it’s because you are thorough. So to build credibility, a reliable platform, and to be valuable to our users, we’ve introduced a human-in-the-loop process, tested, and are constantly refining our proprietary models to ensure the insights we deliver are data-driven and accurate. We’re also developing tracking tools to measure the impact of our insight-driven action items and recommendations and to continuously improve our models.
Work in color.
Thorough professionals who use quantitative customer behaviour and product metrics as a benchmark, will also dive into the transcriptions, calls, and feedback to understand why and how the numbers came to be. We also thoroughly believe surfacing the correlations between what customers do and what leads them to do it is the only way teams and organizations can truly get the full, explanatory story to unlock the intelligence to drive up customer LTV, engagement and advocacy. We call this Customer Intelligence and we want to bring color to all the black and white stills for customer-centric organizations.