The Science & Art of Driving Business Adoption of ML Models
Hrishikesh Vidyadhar Ganu, Mohak Sukhwani
hrishikesh.ganu@myntra.com,mohak.sukhwani@myntra.com
Myntra Designs Private Limited
Bangalore, Karnataka, India

TALK SUMMARY
Myntra Data Science (MDS) is one of the largest Machine learning
teams in India. Myntra has been an early adopter of ML solutions
and hence our work is deeply embedded across (1) In-app recommendations at all stages of the customer journey (2) Inbound supply
chain: sourcing of products, quality checks etc. (3) Outbound supply chain-inventory allocation, route optimization, transit timeforecasting etc.
There are two types of challenges in adoption of ML models:(1) First time adoption of an ML model to replace manual decision making (Adoption)(2) Ensuring adherence to recommendations from an ML model
already in production (Adaptation/Adherence)
We have learnt that there is a different set of solutions for these
two types of challenges. The solutions involve a mix of analytical techniques and use of soft skills like persuasion/stakeholder
management.
We believe that through anecdotes and stories, we can provide
practical insights to help organizations navigate the complexities ofML model adoption and adaptation to drive sustainable success inan ever-changing business landscape. The target audience for thistalk will be practicing ML mangers/leaders and we believe that ourtips based on first hand experience of both failures and successes
will be useful to the ML practitioner community. The talk will havethe following sections:

1

FIRST TIME ADOPTION OF ML MODELSThe two primary steps we follow when working with businessteams on a new Data Science project are:
(1) Agreement on the success metrics or KPIs
(2) Agreement on the measurement framework for KPIs
Point 2 above would be easy if we could do a randomized trial(AB test) every time. However in our experience there are situations where a randomized trial is not feasible. A real world example
we will discuss is Product pricing. E-commerce companies are not
comfortable showing different prices to different customers at thesame time for the same product. In our example this led to a complicated situation where we wanted to measure the impact of anML driven pricing strategy on GMV and Gross Margin (GM) but
could not consider customers as the unit of randomization. We will
provide details of how we solved this unique problem through two
different approaches- one relying on an approximation scheme andgood stakeholder management due to which we gradually attained
business buy-in and another based on analytical techniques fordetermining causal effects even in observational studies.

2

BUSINESS ADHERENCE TO OUTPUTS OF A
MODEL IN PRODUCTION

Supply Chain Management/Operations is an area where continued adherence to ML based planning recommendations sometimes
becomes challenging. This is because when responding to on-theground situations like a shipment lane getting closed due to a traffic accident, operations supervisors have to override ML outputs.
Operations teams also require more explainability and guidance
when interpreting ML outputs, as they tend to rely more on theirexperience-based knowledge. We will specifically discuss our learning in the context of (1) Inventory allocation and (2) Last miledelivery projects where we often faced such issues. We also discuss
solutions that worked for us- establishing standard operating procedures (SOPs) early in the ML development cycle, or involving theoperations lead from the outset to ensure operational nuances areadequately considered during problem formulation.
Finally will also share our lessons in navigating the challenges resulting from the interplay between Business, Product and Engineering teams and tips on how to gain the confidence of non-technicalstakeholders to drive successful business outcomes.

SHORT BIO OF PRIMARY SPEAKER
Hrishikesh V. Ganu leads the Data Science team at Myntra. He has
18 years’ work experience in the areas of ML, Operations Researchand Statistics. Prior to Myntra, he has played managerial roles inML/DS teams at Intuit, Amazon among other companies. He hasa Master’s from Indian Institute of Science (IISc) and a full time
MBA from Indian Institute of Management (IIM), Kozhikode. He
has several publications in ML conferences like SIGIR, NAACL,KDD, Informs Conference on Business Analytics etc. He was anauthor of an accepted talk in the Workshop on Applied Machine
Learning Management at KDD 2022. He likes to learn as well ascoach people on the intricacies of building successful DS teams.

ABOUT THE COMPANY
Myntra 1 is one of India’s leading destinations for fashion, beautyand lifestyle and a pioneer in m-commerce play. An integral part ofthe Flipkart Group, Myntra brings together technology and fashion
to create the best experience in the fashion and lifestyle space inIndia. The company has partnered with over 7000+ leading fashionand lifestyle brands such as H&M, Levis, U.S. Polo Assn., Tommy Hilfiger, Louis Philippe, Jack & Jones, MANGO, Forever 21, Urbanic,Marks & Spencer, W, Biba, Nike, Puma, Crocs, M.A.C, and Fossil
and many more, to offer a wide range in latest branded fashionand lifestyle wear. Myntra services over 27,000 pin codes across thecountry.
1 https://www.myntra.com/