Top 5 Analytics Use Cases in FMCG Industry
The global market for fast-moving consumer goods (FMCG) has been growing at a rapid pace for a significantly long period. However, despite positive growth prospects of the industry, its trends continuously change based on dynamic consumer behavior.
Depending on the changing consumer needs and demands, there is a sudden shift in strategic decisions, FMCG companies have to get used to.
In the current scenario, technologies such as - machine learning and data science, are gaining tremendous importance in the FMCG industry and its operations, organizations are aiming to reduce their vulnerability to continually changing consumer trends. FMCG analytics is changing the way data is operated in organizations - the focus from ‘product’ is shifting rapidly towards ‘consumers’.
Why is there a need for Data Analytics in the FMCG industry?
According to Subrata Dey, Global CIO at Godrej Consumer Products Limited (GCPL), in today’s disruptive and competitive environment, every business has the challenge to grow their top line. With Godrej being no exception, the company is trying to ramp up its top line by leveraging data analytics in the FMCG industry.
Presently, FMCG organizations have an opportunity to revamp their marketing and operations. Having data analytics techniques in place, FMCG companies can move beyond simple reactive operations and take proactive decisions.
Numerous factors such as - (marketing, inventory, seasonal changes, returns, out-of-stock, raw material availability, localized pricing, and so on) drive the FMCG industry. In these unstable times, the FMCG industry can depend on data analytics to identify trends, gaps, and opportunities in customer behavior and supply chains.
Let’s take a look at the following use cases which are currently in use in various leading FMCG companies to have a better understanding of the value of data analytics in the FMCG industry.
#1 Inventory Optimization: Numerous organizations are struggling to find the right balance between on-shelf availability and inventory levels. The “rising bar” of customer expectations and business objectives as well as the increasing complexity of FMCG supply chains is driving organizations to ask more complex questions about their inventory management. FMCG Analytics can reveal insights about crucial performance drivers such as service levels, inventory and asset utilization.
Inventory optimization Analytics outcomes embrace:
# Escalate on-shelf availability.
# Enhance efficiency and workforce effectiveness
# Rebalance inventory between raw materials VS work in progress, VS finished product.
#2 Forecast Optimization: Organizations need to forecast sales to trickle-down effect across departments. The process of generating a forecast needs combining FMCG analytics with business and product knowledge, as well as a continuous focus on improving results to keep up as the business evolves. With leading analytical capacities in place, companies can approach each problem from the different angles required—from the product perspective, customer perspective, retail structure and complexity, and supply chain interdependencies.
Analytics-driven outcomes include:
# Build knowledge of product group behaviors based on historical tendencies.
# Understand the effect of product forecast
# Understand the forecasting accuracy leading to reductions in excess inventory, better manpower utilization, lower expedite costs, and reduced stock-outs.
#3 Supply chain Analytics: In FMCG, the supply chain is one of the crucial parts of the business. One way that FMCGs are using big data in supply chain analytics is to optimize delivery networks. Organizations across the sector have been using analytics to merge multiple delivery networks to create a faster, more streamlined process. This not only helps to enhance service accuracy, but it also removes the tedious wait times between stations. Furthermore, having big data analytics in FMCG industry can create a more efficient supply chain through leading on the management of warehouses. Thanks to advances in technology, analysis of warehouse facilities and processes can be carried out in real-time. This includes identifying delivery mismatches, inventory levels, and income deliveries.
# Minimize Stockouts
# Effective vendor management and collaboration
# Agile demand fulfillment
# Reverse logistics diagnostics and improvements
#4 Price and promotion analytics: With such large investments in trade promotion processes, FMCG companies find it challenging to make informed decisions that trigger appropriate actions and equip them to win in both emerging and developed markets. In such scenarios, FMCG Analytics can help manufacturers become more sophisticated in managing pricing across the value chain. This would include shelf-based pricing, price to distributor and price to the retailer as well as optimization of promotional spend-a massive expenditure for CPG companies.
Analytics-led outcomes include:
#Balance the sales and marketing investment mix to increase sales.
#Enable control and visibility on trade spend investment.
#Improve sales and demand forecast accuracy
#5 Leveraging analytics to increase Customer Retention and Loyalty: In an increasingly hyper-competitive market, the ability to retain customers and gain their loyalty can determine whether a business succeeds or fails. Organizations are turning to data analytics to retain the valuable customers they already have by avoiding weak points. Companies can analyze customer behavior and experience, taking advantage of key opportunities to improve influence buying behavior, customer experience, and increase customer retention.
#Timely promotions and offers to retain or upsell customers
# Engagement and gamification
So, to maintain a competitive edge in a fast-growing marketplace, it is becoming increasingly necessary for FMCG companies to look for proactive methods of harnessing new and extensive data sources in unique ways. Analytics can help the FMCG companies achieve a deeper understanding of their customer data and can offer insights to transform a market laggard into a leader.
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