Expert Talks with Maavrus | Analytics, AI and Transformation

In conversation with Vinodh Ramachandran, Head of Data Science & Analytics, Neiman Marcus Group | Ep 17

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For Analytics & AI professionals in GCCs to be successful, developing domain knowledge and context is very important. I really think that it has to come from within. I was always intrigued by retail as a domain and curious about how things operate in the business. And I was always trying to make sense of what the numbers were telling me and What does it mean?. So I was always trying to put myself in the shoes of the business. And that's something that I enjoyed.  – Excerpt from the interview with Vinodh Ramachandran

 

Today is Episode 17 of the Interview series on Expert-Talks, with Thought Leaders in the Analytics, AI and Transformation space.  For this episode, our CEO Mahadevann Iyerr (Mahaa) is in conversation with Vinodh Ramachandran, Head – Data Science & Analytics, Neiman Marcus Group.  Prior to Neiman Marcus, Vinodh was Site Leader and DVP at Saks Off 5th, where he led all their business functions including analytics. He has also held Analytics leadership roles at Lowe’s , Target and Genpact. Vinodh frequently shares his thoughts at Industry forums.

 

We are sure you will benefit greatly from listening to his perspectives. A few key points from the interview :

 

  • Vinodh articulated the key factors that help make analytics initiatives successful.  1. A well-defined problem statement 2.  Alignment with the organisation's goals  3. Sponsorship from Top leadership. 4. Level of data maturity as measured by single source of truth and 5. The ability to explain the solution & insights to the business in an understandable and practical manner.

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    • To develop domain knowledge and context, reading financial performance reports about the company and competition, and understanding the company’s organisational structure & processes from the company’s intranet pages, helps to get an overall perspective of business.

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      • Exploring the data structure in a warehouse to understand product hierarchies, exploratory data analysis to understand customer behaviour and breadth of offerings, and then validating them in discussions with business stakeholders also helps in further building contextual knowledge.

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        • Usage of external & outside-in data, apart from helping an analytics professional build trust and connect with the business, also helps develop strong hypotheses when looking for insights; for eg number & density of pawn shops in a retailer’s catchment, could have a correlation to a electronic / luxury retailer’s store shrinkage.

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          • Generative AI is being used by many retailers, beyond generating content for customer engagement. It is also finding usages in other areas like customer service, where it is being used to summarise feedback from thousands of customer reviews to give a quick 80-100 synopsis to prospective shoppers.
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            Expert Talks with Maavrus | Analytics, AI and TransformationBy Maavrus