Top 5 Data Analytics Use Cases In FMCG Industry

Introduction

The global market for fast-moving consumer goods (FMCG) has been growing at a rapid pace for a significantly long period. However, despite the 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 toward consumers.

How advanced technologies are addressing some of the biggest complexities in the FMCG sector

The FMCG industry is not immune to the challenges associated with adopting and implementing AI and analytics. From data quality and availability to talent shortage, lack of standardization, cultural barriers, integration issues, and ethical considerations, the FMCG industry faces several hurdles that must be overcome to achieve success.

Data silos, inconsistent formats, and security concerns can impact the quality of data, limiting its availability for analytical purposes. A shortage of skilled data professionals and resistance to change can pose significant obstacles to successful AI and analytics initiatives. Additionally, the diverse range of FMCG products makes data standardization challenging, hindering the effectiveness of AI and analytics.

Furthermore, integrating AI and analytics with existing systems can be complex, and ethical considerations such as data privacy and bias must be addressed. Overcoming these challenges is crucial to leveraging the full potential of AI and analytics to enhance decision-making and drive growth in the FMCG industry. Here are some of the biggest analytical and AI-related challenges in the FMCG industry:

Data quality and availability: FMCG companies often have large volumes of data, but data quality and availability can be a significant challenge. Data silos, inconsistent data formats, and data security concerns can all impact the quality of data and limit its availability for analytical purposes.

Lack of standardization: The FMCG industry has a diverse range of products, and data standardization can be a significant challenge. Without standardization, it is challenging to compare data across products, regions, and business units, limiting the effectiveness of AI and analytics.

Integration with existing systems: FMCG companies have a complex IT infrastructure, and integrating AI and analytics with existing systems can be challenging. Ensuring that AI and analytics solutions work seamlessly with existing systems is critical to the success of these initiatives.

Therefore, addressing these challenges is critical to the success of AI and analytics initiatives in the FMCG industry.

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 its 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 5 Analytics Use Cases in FMCG Industry

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.

Forecast Optimization

Organizations need to forecast sales to trickle-down effects across departments. The process of generating a forecast needs to combine FMCG data 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, 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 expedited costs, and reduced stock-outs.

Supply Chain Analytics

In FMCG, the supply chain is one of the crucial parts of the business. One way that FMCGs are used 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 also removes the tedious wait times between stations. Furthermore, having big data analytics in the FMCG industrycan create a more efficient supply chain by leading 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

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 the 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

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.

• Product Recommendations
• Timely promotions and offers to retain or upsell customers
• Engagement and gamification

Conclusion

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 FMCG companies achieve a deeper understanding of their customer data and can offer insights to transform a market laggard into a leader.