8CIOReview | | JUNE 2023IN MY OPINIONBy Sunando Das, CMI Director, Predictive Marketing and Retail Analytics, UnileverCOVID has driven fundamental shifts in consumer behaviours resulting in more polarised consumer segments driven by different drivers and responsiveness to marketing/sales levers. Given the changing dynamics, the marketing questions are where the future growth will come from, how to sweat the investments more, how to predict the future consumer behaviours to drive sales. Machine Learning (ML) has been instrumental in addressing these challenges, driving business impact, and bringing certainty to uncertainty which I have spoken about earlier. The business impact of these applications will be enhanced with evolutions in ML. This article outlines these eight likely ML evolutions in the next 12-18 months. Beyond Prediction1. From Predicting the Future of a Known Past to that of an Unknown Past: Whilst the uncertainties imposed by COVID in predicting the future consumer demand has been addressed by leveraging ML, what remains unresolved is the prediction by consumer segments where sales data does not exist. Solving this challenge will help to move from predicting the consumer demand to shaping the future consumer demand. Evolutions in neural network and game theory applications are helping address this challenge.2. From Prediction to Shaping Future Sales: Consumer Lifetime Value of categories has reduced significantly in FMCG (Fast Moving Consumer Goods) since the onset of COVID. There are categories with significantly increased consumption in 2020 but the rate of increase will decelerate in 2021. The focus will be on predicting and identifying consumers who are likely to change their future consumption patterns to shape their behaviour rather than looking at past or present behaviour as proxies of future behaviour. This is where evolutions in ML models will be critical. 3. Transferability of Findings: Deployment of any ML application across the business requires significant budget and resources. Hence, ML capabilities to learn from a defined set of markets, categories to extrapolate to the rest of the business will gain relevance. This capability has been in existence and has been applied for several applications over the years ­ what will evolve is the scale across all ML applications.Convergence: skill sets, business applications 4. The Convergence of Data Science and Econometrics: There has been resurgence in the budget optimisation applications where econometrics plays a huge role. However, econometrics has challenges that are overcome by using a combination of ML models to drive higher precision, consistency, and granularity. Getting skilled econometricians to apply ML models without losing the past learning will gain more prominence in the coming months as budget optimisation FROM PREDICTION TO FORESEEING AND SHAPING THE FUTURE: THE REQUIRED EVOLUTIONS IN MACHINE LEARNING Sunando Das
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