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Preparations for a Post-Pandemic Retail Environment Preparations for a Post-Pandemic Retail Environment
Covid-19 has challenged us to redesign multiple aspects of our life and this has inevitably led to wide-ranging impact across business... Preparations for a Post-Pandemic Retail Environment

Covid-19 has challenged us to redesign multiple aspects of our life and this has inevitably led to wide-ranging impact across business in multiple different sectors. The retail industry has especially been disrupted as people seek convenience from the safety and comforts of their homes. As the volume of demand rises and newer entrants tax an already peaked supply chain, customers have little choice but to move away from brands they’re loyal to. Multiple sources claim e-commerce to have accelerated as businesses have packed 5-10 years of innovation in a few months. eMarketer predicts that this growth in e-commerce will slow down, and so developing loyalty and habit with newly acquired customers or staying relevant to existing customers is going to be key for e-commerce companies.

While the changes that companies have had to undergo in the recent past may seem extraordinary given the timeframe, a post-pandemic world does not mean a return to previous normalcy. E-commerce is now being asked to fill in for a lot more for the customer than it previously has. While E-commerce has previously been seen as advantaged for pricing competition, and wider assortments, it’s now also expected to provide better customer engagement and discovery experiences. Physical channels have previously enjoyed a moat here, with the success of e-commerce mostly coming from areas where these experiences weren’t crucial to the purchase experience. In having to adapt to Covid-19, customers have formed new habit patterns to satiate their needs and desires, and the winners in the next generation of e-commerce are going to where customers no longer feel any urge to go back to their previous forms of shopping.

AI can play a central role in providing the innovation and experimentation that fuels this next wave of experiences. Businesses that were running like well-oiled machines and carefully optimized to generate revenue in a competitive retail market are totally thrown off due to unpredictability and changing times. As we seek to build out newer experiences, the adoption of good design patterns can go a long way to help not just scale final solutions but also provide for rapid innovation and experimentation in this ever-evolving competitive landscape.

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Good architecture patterns help us create invariants in a changing landscape, including retail. As the field of AI has matured, we are beginning to see some widely adopted architectural patterns. However, these patterns still need to work well inside of your particular ecosystem and be well suited to the challenges that are specific to you, from a system as well as a business standpoint. In order to create architecture patterns that stand the test of time through shifting business priorities, they need to carefully consider multiple aspects of the ML lifecycle from the inception of ideas to production deployment. The constraints that provide ease of testing new ideas for models are quite different from those that constitute a robust and frictionless production deployment of ML solutions; the model that a scientist carefully fine tunes needs to be deployed and monitored with the same care, while operating under many different constraints in a production environment. Likewise, the ability to measure online and offline performance consistently and effectively, and to be able to account for and mitigate biases are crucial.

While the concerns to be mindful of in an ML solution are varied, and lack of attention to even one aspect could lead to an under-optimized solution, accounting for all concerns of a healthy ML ecosystem independently for each product can be cost-prohibitive. Feature stores like Feast or HSFS can help bridge the gap between feature parity challenges between training and production in a real-time context. Likewise, hyperparameter search algorithms baked into the compute infrastructure like Katib can help with applying the latest innovation in hyperparameter tuning in a model agnostic fashion. This translates to better performance algorithms at lower costs in a model agnostic fashion. Building AI solutions with good architectural patterns that stand the test of time, and allow for different business optimizations to cope with the changing competitive landscape is thus imperative to helping our businesses keep up with the pace of change.

Editor’s note: Nishan is a speaker for ODSC East 2021. Check out his talk to learn more about the future of retail, “Architectural Patterns in Machine Learning to Generate Sustainable Business Value,” there!

As the Vice President of Algorithms for Overstock.com, Inc, Nishan Subedi is responsible for leading algorithmic products and research across Overstock. Partnering with different business units across customer, marketing, sourcing, and operations functions as well as other technology arms, Nishan strives to apply data science-driven innovative practices to business problems. We are hiring!

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