The consumer goods industry has seen decades of glorious growth based on proven age-old strategies of providing solutions of innovative products to the ever-expanding range of consumer needs, rapid expansion into emerging markets, developing brands and operations to meet mainstream appeal and leveraging this scale at every link of the value chain to generate profits. In recent years, these same companies have struggled to generate sustainable growth with their large-scale mass marketing approaches and increasingly diverse consumer profiles to satisfy. Although technology has played a crucial role in the growth of the consumer goods sector so far, the future landscape promises earth-shaking changes with the explosive growth of e-commerce shoppers in markets like China and India and the inevitable adoption of the Internet of Things. With the rapid growth in internet penetration and the digitalization of consumer and buyer habits, FMCG players will need to adapt and innovate much faster to stay relevant and competitive. Say no to plagiarism. Get a tailor-made essay on “Why violent video games should not be banned”? Get the original essay To stay competitive, businesses are adopting new ways to analyze data about their consumers and retailers with the growing range of applications of big data analytics in marketing. , finance, sales and supply chain. Business discussions are rapidly moving away from diagnostic (root cause) analysis and toward predictive and prescriptive analytics aimed at generating business insights. This information allows businesses to be more relevant and respond more quickly to changing consumer behaviors and needs. There is no doubt that businesses with the right predictive business insights have a competitive advantage, making it imperative to have the right data analytics systems and processes in place to succeed. The right consumer insights can be very powerful and the key to gaining these insights is acquiring the right set of data in the most organic way possible. Businesses need to equip themselves with relevant tools to collect comprehensive and relevant data sets and provide rapid and effective analytics to guide business decision-making. One of the most useful types of data is in-store data because it can quickly provide insights. on consumer behavior and their purchasing journey to help drive sales growth. This data can be used to influence sales and distribution strategies as well as joint business plans with retail partners. Below are some ways in-store data analysis can enable better consumer understanding and result in better sales strategies: The in-store shopping journey and the areas where consumers spend the most time time, shelf data to drive shelf refreshing based on shoppers, which shelves or merchandising devices are most effective in driving awareness, what types of products sell best in each store, variations in customer behavior consumers in store based on seasonality. In addition, analyze the volume of.