Decision-makers typically make inaccurate demand forecasting due to their inability to analyze data from various external data sources, all of which impact a product’s demand and buyer’s behaviour.
Some key data sources can include:
According to the P&G report: “Whenever a shopper shifts their buying pattern due to an OOS, it adjusts the demand history away from the sales history and no one can see the true demand history.”
Demand, essentially is sales combined with lost sales. However, lost sales are exactly that: lost. Sometimes shoppers do inform the store about the products they didn’t find so they can be informed when it does come back in stock. However, this doesn’t happen as often as the stores would like, which means, it becomes practically impossible to measure demand accurately.
However, if stores can manage their POS data efficiently, they can better gauge how quickly certain items are moving. This requires drawing careful comparisons and assessments between historical data and real-time data, in order to predict the impact of lost sales from unexpected out of stock situations. These figures should then be used for better demand forecasting.
Learn more: POS Data Analysis: How Can Retailers and Consumer Goods Companies Make the Most of it
Weather has an immense impact on the consumer psychology, habits, preference for products and overall behavior. Obviously, products that are required for specific weathers only will have varying demand throughout the year. For example, raincoats, umbrellas, gumboots, etc., will only have high demand during monsoons. If there is a sudden outbreak of a diseases, the demand for certain health related products such as face masks and sanitization products would go drastically up.
These are situations that companies should extra data for, in order to ensure that they’re able to have enough stock to meet consumer demand. Assessing historical data in combination with future weather predictions, as well as past weather patterns, stores will be better able to forecast the levels of demand and supply.
Learn more: How Weather Impacts Retail Sales & How Big Data Analytics Can Help
When big events happen, such a music concert or a sports tournament, there’s obviously going to be a massive influx of consumers and subsequent increase in demand for certain types of products – these could be beverages, novelty items, etc.
This may be an obvious one but data on past sales provides invaluable inputs on demand and sales for different products at different times and locations, throughout the year.