Snap2Insight
Renish Pynadath has diverse work experience spanning several industries. Renish co-founded Snap2Insight in 2016, where they utilized image recognition and deep learning to provide insights on merchandising and trade promotions. Prior to this, they worked at Intuit from 2015 to 2017 as a Group Product Manager/Principal Product Manager. At Yahoo, they served as a Director of Product Management from 2010 to 2015, overseeing analytics, reporting platforms, and experimentation. Renish also managed new product development programs at Honeywell from 2003 to 2010 and led IT projects aimed at streamlining sourcing and reducing cycle time. Earlier in their career, they worked at Ivega Corporation as a Business Analyst/Senior Associate in IT services, and at Infosys Technologies Ltd as a Software Engineer.
Renish Pynadath received their education from various institutions. Renish began their education at Kendriya Vidyalaya, where they completed their Class I to XII with a science stream focus. Later, they attended Model Engineering College from 1994 to 1998, earning a Bachelor of Technology degree in Electronics Engineering. Renish then pursued an MBA (PGP) 2-year full-time program at Indian Institute of Management Bangalore from 2000 to 2002, specializing in General Management. In 2001, they had the opportunity to participate as an International Exchange Student in the MBA class at Mays Business School - Texas A&M University.
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Snap2Insight
One of the leading health and personal care brands had planned a promotional campaign offering bonus packs at a promotional price in the face of some intense competition from its rival brand. Their customer (Team Leader for the brand at a retail chain) had worked with a major retailer to put up pallet displays with eye catching graphic skirtsacross all its stores nationwide. Their customer has often seen poor sales lift across 30-40% stores for such campaigns in the past. Snap2Insight was tasked to verify quality of execution nationwide and identify stores with incomplete execution or no execution. Leveraging their partnership with a leading crowdsourcing vendor, they quickly gathered photos of displays from every single store and using their patent pending Deep Learning technology, they analysed them to recognise SKUs stocked, present of graphic skirt, promotional price and type of display put up. Using execution scores they provided for each store covered under this promotional campaign, their customer worked with their retail coverage partner (merchandiser) to prioritise and fix all stores with low execution score and high potential for sales lift, thereby improving sales and achieving break over for over 90% of stores.