4 benefits of utilizing Data Analytics for an FMCG company

Data analytics can be a game-changer for FMCG companies. In our previous post, we explored this idea and examined the current state of the FMCG industry with its struggles of adapting to the lightning-quick era we live in and how tapping into data analytics can prove to be a more efficient way of operating as well as a solution to new challenges faced by businesses today. 

Now we will delve into the 4 major benefits obtained by FMCG companies who use data analytics in their day-to-day operations. 

Clearer understanding of customers

Instead of making use of guesswork and sorting through individual purchase data, FMCG companies can derive various insights from data regarding the product or service by tracking consumer data, such as how consumers find the product, the amount of time they spend on the website, the time of day when it is most purchased and other products that consumers show interest in. The user can monitor transactional data to ascertain what products are in demand and what stays in the inventory for too long, whether in an actual retail store or online. Thus, companies can have a more accurate understanding of the customers’ behavior and needs.  

Further, it can also assist the business in marketing and promotional activities that it undertakes as the user is now able to link promotional campaigns to purchase behavior. It helps not only with a more general customer base but also helps to target specific customers in an optimal way. 

Cross-platform marketing is now easier and simpler than ever as businesses have an efficient way to assess consumer behavior using technologies like AI which can drive conversion from an interested party to a purchaser. It also helps in the creation of a well-rounded brand that inspires loyalty in its customers by focusing on customer satisfaction through the offering of better deals and customer personalization. 

Optimization of supply chain processes

While customer and consumer behavior and analysis are often the more sought-after playground for data analytics due to the top line benefits, application in operational areas like supply chain result in more direct and tangible benefits through cost savings and operational efficiency. Executives now can rely on algorithms and data to make crucial decisions. 

With geoanalytics, it is possible to merge delivery networks which improves service accuracy and reduces time taken between movements from station to station. This is a more modernized and well-organized solution to the problems faced by businesses due to inefficiencies in the delivery process. 

Accenture’s research on the impact of big data analytics on the supply chain has revealed that incorporating big data analytics into operations leads to a 4.25x improvement in delivery time and 46% of companies reported experiencing a 2.6x improvement in supply chain efficiency in figures of 10% or more.

 

Warehouse management is another process that data analytics can streamline as it is able to facilitate the required warehouse processes such as identification of inventory levels, mismatches in delivery and incomes and machine performance, and more.  

Improved product design and accurate pricing

Companies can develop products and customized designs for consumers by trawling through a wealth of data and making use of technologies such as intelligence tools and predictive analytics. Data analytics has completely transformed the landscape for product innovation as it can tell the user what’s going on with their products – from development, to launch, to customer satisfaction. 

It also aids in making more precise predictions regarding sales figures, demand, and return on investment. Distribution strategies can be created as shopping and buying behavior across various channels are analyzed and consequently, factors like places of distribution and distribution techniques can be determined with more ease. 

Matching consumer data to market price points assists in the formulation of data-driven pricing strategies that are flexible and intelligent, consequently helping in achieving product and sales objectives. It also creates opportunities for dynamic pricing strategies that can respond quickly to marketplace developments. 

Reduction in costs and risks

Streamlining processes within the business through data analytics also makes it easier for FMCG companies to become cost-efficient as well as identify areas of the business that utilize excessive financial resources. The user can trace outcomes of decisions to financial objectives. For example, with predictive analytics, operational costs can be reduced by helping in the purchase of raw materials and hedging against market fluctuations. 

Since fraud is a big concern for these companies due to the fast-moving structure of the products and reliance on third-party actors like vendors, suppliers, and transporters, etc., data analytics can also act to secure the physical, intellectual, and financial assets of the company.  

Such an instance can be seen in how with the help of analytics, retailers can identify instances where customers might be returning products to participate in return fraud. With data that can inform the retailer about what normal consumer behavior resembles regarding how many products a customer buys in a year, it can then identify and blacklist people participating in return fraud much more easily.  

Businesses can use data analytics applications to parse, process, and visualize their audit logs to determine the course and origins of a cyber-attack. This type of security can cut down on instances of misuse whether internally or externally and serve as a good base to build organizational security practices. 

With the use of various statistical paths, as well as big data methodologies, businesses can create predictive fraud propensity models, which then help cut down on fraudulent practices. 

Conclusion

The leaders of the FMCG industry have taken notice of the potential of data analytics in business operations and have been putting it to use for helpful insight and informed decision-making.
FMCG giants like Unilever employ data analytics to compute the customers’ location of purchase and their routine whether say it was purchased during their daily commute to work or their weekends off while shopping with their families.
Using aggregate consumer data with the tools of data modeling and simulation, Procter and Gamble has strengthened their product lines and promoted new products within those lines. This has led to a reduction in costs in traditional prototyping.
These are a few examples of how big FMCG companies have been making use of data analytics. However, the overall industry still has not completely embraced the possibilities offered by data analytics.

Smaller businesses that have fallen behind on adoption need to get on the train of data analytics as soon as possible. This comes with its own difficulties and complexities, which is where Tesser Insights can step in to provide an analytical ecosystem for businesses that can level the playing field, allowing them to hold their own against industry giants. Whichever way you look at it, data analytics is clearly the future for FMCG companies, and it is best not to be left behind as more and more companies see their potential.