The consumer goods industry has witnessed decades of glorious growth based on age-old, proven strategies aimed at providing innovative product solutions to the ever-growing range of consumer needs, rapidly expanding into emerging markets, scaling brands and operations to meet mass appeal, and leveraging this scale at every node of the value chain to generate profits. In recent times, the same companies have struggled to ensure sustainable growth with their large-scale mass branding approaches and increasingly diverse consumer profiles to satisfy. While technology has played a crucial role in the growth of the consumer goods sector to date, the future landscape promises disruptive 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 of internet penetration and the digitalisation of consumer and shopper habits, FMCG operators will need to adapt and innovate much more quickly to remain relevant and competitive. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay To stay competitive, companies are adopting new ways of analyzing data about their consumers and retailers through 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) analytics towards predictive and prescriptive analytics for business insights. This information allows companies to be more relevant and react more quickly to changes in consumer behaviors and needs. Without a doubt, companies with the right predictive business insights have a competitive advantage, making it critical 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 unlocking this insights is acquiring the right set of data in the most organic way possible. Companies must equip themselves with relevant tools to collect comprehensive and relevant data sets and provide fast and efficient analytics to guide business decision making. One of the most useful types of data is in-store data as it can quickly provide insights into consumer behavior and their purchasing journey to drive sales growth. This data can be used to influence sales and channel strategies, as well as joint business plans with reseller partners. Here are just a few ways in-store data analytics can provide a better understanding of consumers and translate into better sales strategies: In-store shopping journey and areas where consumers spend the most time, data related to shelves to help refresh shelves based on the shopper, which shelves or merchandising devices are most effective in increasing awareness, which types of products sell best in each store, changes in in-store consumer behavior in based on seasonality. Additionally, analyzing visitor volume (traffic) and purchase frequency over time at each distribution point can help stores optimize staffing, inventory and promotion plans. Companies can also choose to analyze data outside of the store, such as each store's neighborhood demographics, socioeconomic background, and life stage data to.