+1 916 605 7200          moreinfo@retailpro.com        
 
   +1 916 605 7200              moreinfo@retailpro.com            

Predictive analytics: Looking at the past to shape future sales

Understanding customer behavior and shopping patterns is difficult enough during “normal times.”

So, when a shockwave hits the system – like a global pandemic or natural disaster – it stresses the supply chain and puts planning on its ear.

Accounting for seasonality in demand

Image: JESHOOTS.com

Predictive analytics can help retailers prepare for all types of seasonal happenings, including not only holidays, but also hurricanes and wildfires.

Natural disasters are often seasonal: For example, wildfire season is August-November and hurricane season is slightly longer, starting in June.

While it is impossible to predict the final landfall point of a hurricane or the path of a wildfire, goods can be procured in a way that optimizes costs while considering all path probabilities.

Making accurate predictions regarding the types and amounts of products demanded by consumers is not trivial: Ineffective forecasting efforts result in shortages of in-demand products as well as overages of unwanted products that ultimately must be salvaged.

Focusing predictive analytics on concrete business objectives

Image: shattha pilabut

It seems paradoxical that predictive analytics uses historical information to determine future shopper actions.

Such retail data might include transactions, sales results, customer complaints, and marketing information.

Retailers use predictive analytics with a business goal in mind. 

By harnessing large, heterogeneous data sets into models, they can glean clear, actionable intelligence that helps them achieve their goals, such as more sales, less inventory, and faster deliveries.

Having the right data is key to predictive analytics success. That information may include:

  • Point-of-sale data
  • Consumer-related information (e.g., loyalty programs)
  • Store layout
  • Online navigation traffic flow
  • Consumer demography
  • External factors, such as weather

Retailers can prepare for seasonal shopping by crunching last year’s sales data, combining it with those other pieces of information, and creating a game plan that can meet any storm – or holiday – head on.

The key to retail growth in today’s marketplace is unlocking the benefits of predictive analytics to gain a deep understanding of the customer base to maximize sales, improve inventory churn and increase customer satisfaction.





130

Countries

9000

Customers

54000

Stores

159000

Points of Sale

130

Countries

9000

Customers

54000

Stores

159000

Points of Sale

130

Countries

9000

Customers

54000

Stores

159000

Points of Sale