What is Customer Intelligence and how to better your company's value.
Johnson, our visionary technical co-founder, has mentioned in the blog, What is Qualitative Intelligence? there is a vast difference between qualitative and quantitative data. The important thing is the significant relationship between the insights and values that can be derived from each type of data, and when one can 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 focus 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 obvious reason why Adaptive Pulse exists is that 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 out, is another reason 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 customers a, b, and c who are all early adopters to launch a campaign which will not be appealing to the majority of the customers) or any other type of cognitive bias humans are prone to.
Aware of automation bias, we were hoping it would come into play in our target market but we had an opposing effect where our initial prospects and users were worried that leaving this to machine learning would miss some crucial data analysis and insights. We should have known if you are looking into us, it is because you are thorough. So to build credibility, a reliable platform, and to be valuable to our users, we have 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 are also developing tracking tools to measure the impact of our insight-driven action items and recommendations and continuously improve our models.
Thorough professionals who use quantitative customer behavior 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 are 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.
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