Forecasting Retail Sales with Customer Behavior Analytics
I was just forwarded a fantastic white paper that dives deep into the details of developing an analytics-driven retail sales forecasting model. The paper was written by Wipro, and goes into very granular detail on the retail sales forecasting analytics process, the areas to analyze, the time periods, and myriad other variables that should be factored into the sales forecasting model.
The paper quickly gets into the value locked within customer behavior data, building on the typical POS analysis and adding store details, customer lifetime transactions, credit history, weather, etc. This is an area in which Quantivo excels, and our product can quickly unearth the valuable patterns within this potentially large set of customer data.
While their paper does get mathmatical at some points, it’s a very worthwhile read and only serves to highlight the value of having an analytics tool like Quantivo that can quickly join various data sets and then quickly surface the valuable and actionable customer patterns within the data.
You can download their white paper here (registration required). You may also be interested in a related white paper that we’ve written, downloable here, that focuses on using SaaS analytics for just this type of analysis.
Let us know what you think of both papers.