From a humble toothpaste to toiletries to staples to medicines to even electronic items, all of us use Fast Moving Consumer Goods (FMCG) every day. In India, after Agriculture, IT, Telecom and Healthcare, the FMCG sector provides the highest employment to more than three million people in the country. Major players in the FMCG sector are ITC, Hindustan Unilever, Amul, Coco-Cola India, half of them being in the household and personal care.
The FMCG companies try to out-do one another in production, supply chain, sales, marketing and retail strategies in bringing to us on the store shelf, fast-moving inexpensive products that may often have a low shelf life. The profit earned from such products is very low and therefore, to have a sustainable business, the aim of any FMG company is to supply high volumes that cater to a huge consumer base, thereby earning a high turnover, albeit low margins.
At every stage of this journey from manufacturing to delivery to marketing and sales, an FMCG company faces several challenges in decision making and the only resource that can be relied upon is data. From day one of inception, the company starts collecting data in the form of POS data, customer demographic data, market research data, promotional campaign data, finance data, Twitter data, Facebook data, weather data and the list goes on. This data is often noisy, unstructured, scattered and available in different formats. How does a company make use of this data to create action items that not only cater to the present situation but also plan for future? The solution is Data Science and Machine Learning.
With the help of advanced machine learning algorithms, the FMCG company can merge all the data and bring structure to it, and therefore find better answers to various problems that plague their business.
With the help of Supply Chain Analytics, a supply chain manager can set up and maintain the most cost-effective supply chain that would result in OTIF (On Time In Full) deliveries.
A sales manager can forecast his sales more accurately with the help of Time Series concepts and recommend effective improvements in operations.
FMCG companies invest a large proportion of their funds in advertising. With the help of machine learning and predictive analytics, the brand manager of an FMCG company can efficiently utilize his marketing spend in the right mix of advertising and promotional channels, that would result in a high return on investment.
A retailer can choose the right shelf space expenditure with the help of statistical concepts like ANOVA. With the help of store clustering, an FMCG company can strategize their efforts on a regional basis to capture customers and deal with regulatory compliances. With the help of RFM (Recent Frequent Monetary) analysis and category scorecard analysis, the FMCG company can adopt a consistent sales strategy to acquire and retain loyal customers, who consistently buy their products instead of a one-time purchase.
With the advent of social media, text mining can help FMCG companies understand the taste & purchase behavior of consumers and thus create better and timely marketing campaigns.
With so many hard-to-predict factors affecting the extremely competitive FMCG industry, Data Science and Machine Learning is undoubtedly the way to do business in future.
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This article has been contributed by our faculty, Jaishree Ravi. Jaishree is an advanced data science professional having two decades plus functional experience with a Ph.D. in Operations, and background in Mathematical Statistics and Operations Research.