Forecasting strategy with geospatial data

Predicting the future is difficult, even for seasoned experts. While we can’t be certain how things will pan out, we still use as much data as possible to anticipate future trends.
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In the early days of commerce, business owners relied on experience and intuition to gauge demand. While this was fine for small enterprises, as the markets became more complex and businesses grew, it became increasingly imprecise and antiquated. 

Inevitably, intuition-based forecasting was replaced by a data-driven approach. Businesses started to rely on historical sales data, using raw numbers to create meaningful insights about future demand. This approach made demand forecasting more standardized, reliable, and capable of minimizing (though never eliminating) uncertainty.

Historical data is central to demand forecasting, which uses methods such as moving averages, exponential smoothing, and linear regression models to identify trends and estimate future demands. However, in rapidly changing market conditions, historical data can quickly lose its predictive value due to its rigid nature.  

Demand sensing represents the next evolutionary step in demand forecasting. It improves on traditional forecasting by focusing on short-term accuracy and real-time data. Be it the changing of the seasons, natural disasters, or consumer buying behavior, demand sensing has a broader range of short-term signals to help make predictions more quickly and accurately. For instance, using social media trends and recent sales patterns to ensure certain items are in stock in anticipation of rising demand. 

For the FMCG and retail industries, demand sensing has become crucial in helping to tackle supply chain guesswork and marketing efforts. To make confident forecast decisions, geospatial data and insights play a prominent role in these sectors. 

Optimizing the supply chain with geospatial data insights

A crucial part of supply chain management is ensuring the right products are available at the right time and place. Access to accurate geospatial data insights helps sense customer demand to make this possible. By analyzing point-of-sale (POS) foot traffic, customer demographics, preferences, and competition, businesses can create a more responsive and efficient supply chain.

A powerful use of this insight is identifying and analyzing customer clusters. These are groups of customers in specific geographic areas who share similar purchasing habits. Mapping the clusters allows businesses to position distribution centers and warehouses closer to high-demand areas. This reduces delivery times, cuts transportation costs, and ensures products are stocked based on localized demand patterns.

For example, these insights can be used to assess where certain seasonal product types are most in demand. Retail and FMCG companies can optimize their inventory levels at nearby warehouses in anticipation of this demand. 

Geospatial data insights help localize marketing campaigns

As the practice of personalized marketing has proven to be an effective tool for driving conversions, understanding the neighborhoods where target audiences frequently visit offers a valuable advantage in creating localized campaigns.

By analyzing customer origins and movement patterns, businesses can better anticipate behaviors and design marketing campaigns that resonate with specific audiences. Echo’s Catchment Area analysis enables a precise study of mobility trends, allowing tailored campaigns to be strategically launched in areas specific to consumer behavior.

But how is this connected to demand sensing and forecasting strategies? In a landscape where customer behavior can change for several reasons, the ability to adapt is critical. Real-time mobility data provides a powerful short-term signal of changing movement patterns, helping businesses respond more effectively to evolving consumer demands and identifying new audiences. 

For example, gym chains like Basic-Fit can target fitness lovers in Toulouse, France, by concentrating their marketing efforts on denser areas to maximize conversion and determining how many employees to staff on different days:

Customer-centric insights for forecasting strategies

At its center, optimizing supply chains and crafting localized marketing campaigns are two sides of the same coin. They hinge on the understanding of customer behavior, which is made possible through advanced geospatial data insights. Businesses can preempt customer needs with great precision by examining POS foot traffic, demographic patterns, and customer preferences. It’s a granular approach that enables them to not only adjust inventory across a range of locations but also refine marketing campaigns that reflect local preferences. In doing so, businesses are placing the customer at the heart of their operations. 

A customer-centric approach fosters agile adaptability, which is increasingly important in the complex and changing FMCG and retail marketplaces. Demand forecasting has come a long way since the days of intuitive decision-making—demand sensing allows businesses to respond to shifting consumer behaviors efficiently. The way we see it, geospatial data is the crystal ball for anticipating the future.