Fast-Moving Consumer Goods (FMCG) companies operate in a highly dynamic environment where speed and accuracy in decision-making are critical factors.
However, the lack of visibility into product distribution and final points of sale makes it difficult to optimize the supply chain, forecast demand, and design effective commercial strategies.
To address these challenges, leading companies are turning to advanced data analytics and data-driven Road-To-Market (RTM) strategies. An effective RTM strategy enables businesses to optimize distribution, better segment markets, and make well-informed strategic decisions.
In this article, we explore the key challenges in the FMCG sector, the crucial role of data analytics in RTM strategy, and how companies like Coca-Cola have improved operational efficiency through advanced data-driven strategies.
Fast-Moving Consumer Goods (FMCG) are products that sell quickly and at relatively low prices.
Examples of FMCG include food, beverages, personal care products, and household cleaning items. These product categories have high demand and a constant turnover on retail shelves.
The FMCG sector faces significant challenges due to the nature of its supply chain and distribution.
Unlike other industries where companies can easily track where their products end up, FMCG businesses often struggle with a lack of detailed information about the final point of sale, making strategic decision-making more complex.
Road-To-Market (RTM) is the strategy that defines how to efficiently and profitably deliver products to the end consumer. In the FMCG sector, characterized by highly fragmented distribution and volatile consumption patterns, a data-driven RTM strategy is essential for maintaining competitiveness.
A well-designed RTM strategy allows companies to:
FMCG companies face multiple challenges that impact operational efficiency and decision-making:
FMCG products are sold through a vast network of retailers, including supermarkets, convenience stores, pharmacies, and small independent shops.
This diversity makes distribution control difficult, as manufacturers rely on distributors and wholesalers to deliver their products.
Unlike companies that sell directly to consumers, many FMCG brands lack precise information about where their products are being sold and in what quantities.
The absence of data prevents them from accessing real sales insights, making it difficult to optimize their commercial, distribution, and marketing strategies.
Consumer trends shift rapidly, and FMCG companies must anticipate demand to avoid issues of overstocking or stockouts.
However, without accurate data on consumer purchasing behavior, demand forecasting becomes a significant challenge.
Fast-Moving Consumer Goods companies cannot rely solely on their internal data to understand market demand and optimize their supply chain. While internal information is valuable, it is limited and does not provide a complete view of consumer behavior, market fluctuations, or external factors affecting supply and demand.
To make more precise and strategic decisions, leading FMCG companies are leveraging advanced data analytics that integrate external information.
Incorporating third-party data—such as consumer trends, competitive intelligence, economic forecasts, or weather data—enables them to anticipate demand, optimize logistics, and strengthen their market positioning.
The FMCG sector has been investing in data strategies and advanced analytics for years to optimize operations, manage inventory, and gain deeper consumer insights.
However, many companies still rely heavily on internal data and traditional approaches, limiting their ability to adapt to market changes in real time.
Integrating external data presents a major opportunity for the sector.
By incorporating market trends, real-time demand insights, weather patterns, supply chain disruptions, and competitive intelligence, FMCG companies can significantly improve the accuracy of their forecasts, optimize distribution, and maximize profitability.
Some companies deploy field teams to retail stores to collect data on sales and consumer preferences.
While this method remains useful, it is costly and time-consuming.
Enhancing internal data with external information allows companies to gain a more comprehensive view of the market.
A great example of how a data-driven RTM strategy can transform operational efficiency is Coca-Cola.
Coca-Cola sells its products through distributors who purchase large quantities (e.g., 10,000 units), but the company lacks precise visibility into where these products end up or how much is sold at each point of sale.
This issue is particularly critical in highly fragmented sectors such as hospitality (bars, restaurants, etc.), where controlling distribution becomes even more complex.
To address this challenge, Coca-Cola has implemented data-driven strategies:
Thanks to this data-driven approach, Coca-Cola has gained greater visibility into its distribution network, optimized logistics, and maximized sales at final consumption points.
Conclusion
The FMCG industry faces numerous challenges due to fragmented distribution and limited visibility at points of sale. However, leveraging external data, digitalization, and advanced analytics enables companies to overcome these difficulties and make more informed decisions.
By integrating geospatial data, demographic insights, and industry statistics into their strategies, FMCG companies can optimize distribution, strengthen relationships with distributors, and maximize profitability. In a data-driven world, businesses that effectively harness this information will gain a crucial competitive advantage in the market.
Before you go...