Predictive Analytics Explained Simple: A Study On The Impact Of Predictive Analytics On The Fast Moving Consumer Goods (FMCG) Industry

Predictive Analytics

Predictive analytics can be integrated to enhance the digital experience of a wide range of stakeholders. It enables the company to classify the likelihood of a future outcome based on historical data, self-learning algorithms and machine leaning techniques. Below are some use cases of predictive. 

Figure 1 Use Cases of Predictive Analytics 

Behavioral analytics: is the ability to predict online buying behavior based on what a consumer has bought in the past to predict what they are likely to buy next. For example, Unilever (Ustore) will be able to identify and move the most likely products of next purchase to the closest distributor to ensure fast delivery within the day enhancing the consumer experience. Behavioral analytics can be based on individual, region, country or global, etc. 

Inventory and supply chain: Predictive analytics will identify which products will move faster than the other giving an idea on what to store in more quantities and what to store less to avoid clutter and optimize warehouse space. Going further, it will enable a Just in time (JIT) approach to predict real time demand and make sure all delivery points and intermediaries gets the products on time. 

• Distributors are suppliers don’t have to be rushed.  
• Can reduce warehouses to save substantial costs  
• Give insights with ERP automation for suppliers and intermediaries to prepare in advance enhancing stakeholder experience Senior Management: 

Knowing these market insights will help predict the future market trends based on current and historical market data so the top management can take informed strategic decisions which will reduce risk and increase rate of strategic success to better identify and respond to consumer behavior.