One of the marvels that the age of data and technology presents is the ability to analyze and determine the minutest of details in the world today. Several of these innovative breakthroughs pass unnoticed under the gaze of daily life. Yet it is this dissemination of data and integration of innovation that is intrinsic the modern world. One field which has risen from the fore of the data deluge is ‘Decision Science’. Through innovation it combines analytics, with an inter-disciplinary approach to extract the best insights from data, and subsequently facilitate data-driven decision making. Decision Sciences, which holds the key to survival for the dynamic modern day business landscape, employs an interdisciplinary approach of business, applied math, technology, design thinking and behavioral sciences, to solve constantly shifting and ill-defined issues. However, it is not complete without its roots in cross pollination and constant innovation; drawing insights and learnings from several verticals and industries, businesses realize the tremendous potential of problem solving armed with cross industry knowledge.
Cross- Pollination of learnings has been flagged as a pillar toward category defining insights, in the fact that it draws knowledge from beyond the designated field of view. In 2009 for instance the outbreak of the unknown H1N1 was contained through the use of predictive and location analytics. It was done by using a clustering algorithm that monitored statistically unusual burst of incidences of flu in ER data, or over-the-counter sales in a geographically bounded region, to form geographic clusters. Subsequent localized factors were determined that were based on scanning countries, so as to conclude whether a particular disease occurrence was unusual in a population of that size, taking into account the minimum number of false alarms. Experts known as epidemiologists were effectively able to extract the movement and impact of the virus by employing predictive analytics.
This process of geographical clustering was employed in the energy sector, in order to designate and target geographic areas of customer attrition. Traditional analytics failed to determine which locations suffered the highest attrition, and the problem was solved through finding clusters of zip codes that could be tested for localized factors, as in the case of the H1N1 outbreak, that affected customer attrition. Finding clusters of localities at a greater risk of attrition is quite similar to finding clusters of disease incidences.
Epidemiology which is the science of determining trends in diseases is a field which has much to contribute to the world of business. It is indeed the need of the modern business landscape to draw from insights in other fields and industries, and subsequently translating this cross pollination into decision making insights. The use of predictive and location analytics stirred by epidemiology led to the discerning of clusters of zip codes.
The results of innovative learnings and cross-pollination answer the frequent Marketing Analytics question asked by marketers: “How do I determine which customers are at risk of attrition and how are they effectively addressed?” By drawing out similar problem solutions from parallel industries, CMOs are increasingly finding game changing solutions to problems that plague their businesses.
It is interesting to see the relevance of cross-industry learning in the business context. Whatever an organization markets, it is highly probable that it is sold to people. Therefore employing analytical techniques based on their usage in Epidemiology such as marketing, predictive
If certain metrics can be looked at on similar grounds, it may just show that like the correlation between customer attrition and percentage of flu diagnosed in a medical center, different industries all have problems which address the same core. Building solutions using clear problem definition and in understanding the overlapping principles of cross-industry knowledge, all of which are emphasized in the use of Decision Science, businesses today stand to change the industries in which they function.
So why not let ideas mix and integrate the learning from across the spectrum? The cross pollination of knowledge was never such a valuable asset, as it is today.