Predictive Analytics

Retailers use data to predict back-to-school demand

By October 12, 2016 No Comments

There will be students who need clothes and supplies when they head back to school in 2016. The questions retailers must answer are how many and where will they go to shop. This allows stores to prepare inventories and market to audiences most likely to be swayed by promotional content.

Business intelligence software should help retailers forecast demand – provided the companies use the right information. Industry studies, store records and external factors are all data sets stores should use in predictive modeling systems.

Begin with basic information
Stores can begin predicting demand with general industry findings. A Mintel survey of 1,830 parents found the majority of participants plan to spend more on back-to-school shopping in 2016 than they did in 2015, according to Chain Store Age. Two-thirds planned to buy clothing, but only 8 percent said they would buy new supplies or electronic devices.

"Stores should mix and match public information sources."

By parsing through the data available to the public, stores can see how consumers plan to spend in the coming seasons, the effects of the current economy on audiences and other factors that may limit or motivate sales. As each business is unique, stores should mix and match public information sources to focus on findings that directly affect their particular products.

Examine the store's history
One of the best sources of insight into how your store will perform in the future is what led to success in the past. Data Science Central said the first step is to make all relevant internal information visible. This means practicing data collection and storing information in a system that makes the details easy to compare to other datasets.

It's possible a store has data silos or other inefficient systems that makes it hard to view past trends or compare inventory movements to different seasons. It may be best to invest in a custom performance business solution in summer to prepare for the fall rush. Even if a business doesn't have the data necessary for predictive modeling now, it can improve processes for next year.

Look at outside factors
Once a business has studied its own data and market trends, it needs to look at all other data sets that may influence demand. For example, The Weather Channel said 2016 will be a very hot summer, so shoppers in certain states will avoid long shopping trips.

This data could indicate that even if general demand increases in 2016, brick-and-mortar stores may not see the effects as more families turn to e-commerce solutions. It's important to have visibility of all information – such as community finances, traffic patterns and local school expectations – to create accurate predictions. 

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