Editorial Open Access

Machine Learning 2018: Applying big data analytics and machine learning in precision marketing- Santosh Godbole -SSN Solutions Limited

Abstract

Creating and utilizing purchaser personas isn't new. Advertisers have been experiencing carefully long approach to comprehend and characterize shopper persona for their items. Further, they experience a mind-boggling procedure of characterizing and executing elaborate crusades to secure purchaser data and guide the equivalent to required personas. Considerably in the wake of spending large segment of their financial plan, advertisers face different issues in connecting with the correct purchaser: Data obtaining is a costly undertaking, commonly information isn't valid or later; this begins to influence the change pace of the business making the ROI an implausible dream. Regular methodology utilized in information procurement and persona creation experiences different issues: Most personas manufactured today are static. Truly, the act of refreshing buyer profile occasionally is useful however not perfect. Second, there are simply such a large number of variables (characteristics) associated with the consumer???s dynamic procedure. Marketer???s approach of restricting purchaser to hardly any personas is very constraining and wrong. The response to these mind-boggling issues is to construct multidimensional shopper profile that is consistently cutting-edge. This is conceivable by drawing in the shoppers at different stages during their day, be it online scenes, for example, informal organization, audits, sites, sentiments, reviews or disconnected settings, for example, studies, exchanges, logs, etc. Building up a multidimensional profile that is modern is definitely not a basic errand. It is the sort of issue where devices, for example, huge information, information examination and AI can be utilized most viably. Big Data is that the biggest hame-changing opportunity for marketing and sales since the web went mainstream almost 20 years ago. The data big bang has unleashed torrents of terabytes about everything from customer behaviors to weather patterns to demographic consumer shifts in emerging markets.\ The world has become excited about big data and advanced analytics not simply because the info are big but also because the potential for impact is big. Our colleagues at the McKinsey Global Institute (MGI) caught many people’s attention several years ago once they estimated that retailers exploiting data analytics at scale across their organizations could increase their operating margins by more than 60 percent and that the US healthcare sector could reduce costs by 8 percent through data-analytics efficiency and quality improvements.1 Unfortunately, achieving the level of impact MGI foresaw has proved difficult. True, there are successful samples of companies like Amazon and Google, where data analytics may be a foundation of the enterprise. But for most legacy companies, dataanalytics success has been limited to a few tests or to narrow slices of the business. Very few have achieved what we might call “big impact through big data,” or impact at scale. For example, we recently assembled a gaggle of analytics leaders from major companies that are quite committed to realizing the potential of massive data and advanced analytics. When we asked them what degree of revenue or cost improvement that they had achieved through the utilization of those techniques, three-quarters said it had been but 1 percent. In previous articles, we’ve shown how capturing the potential of knowledge analytics requires the building blocks of any good strategic transformation: it starts with an idea , demands the creation of new senior-management capacity to really focus on data, and, perhaps most important, addresses the cultural and skill-buildingchallenges needed for the front line (not just the analytics team) to embrace the change.

Biography:

Santosh is the Co-Founder and Chief Product Officer at SSN Solutions Limited. At SSN, his role is to define product and technology roadmap. Prior to SSN, he was a Senior Director of Engineering at ARRIS (India). At ARRIS, he has managed a large team of engineers that was spread across multiple countries. He also held various senior level positions such as Director Product Management at Cisco Video Technologies, Vice President Product Management at NDS Services Pay-TV Technology Pvt. Ltd., Co-Founder and VP Engineering of Sensact Applications and Co-Founder and Architect at Metabyte Networks. He holds Executive General Management Program (EGMP) certificate from IIM Bangalore, MS in Computer Science from IIT, Madras and BE in Computer Science, MS from University of Baroda.

Santosh Godbole

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