Note to the reader: This article assumes some familiarity with data science and analytics concepts.

The finance team of a major Australian retailer wanted to know the answer to the question of “How much does the weather affects the weekly sales?” — A ubiquitous problem which many financial analysts and managers are struggling to solve in the company. However, nobody in the business has a scientific method of solving it.

The applications of accurately quantifying the effects of weather on sales are many folds, such as:

1.) Improved customer service by optimising pricing & discounting the right products during certain weather conditions.

2.) Estimating the demand for specific weather-sensitive products such as umbrellas or ice creams.

3.) Improvement in the supply chain by lowering working capital & reducing logistics costs by determining lousy weather days.

As pointed above the business value of solving this problem is tremendous, as this could help better understand the impact of certain weather events on the top and bottom line. Not just that, the business can better monitor and report weekly transactional habits of the customer, which are influenced by the weather. The ultimate aim of this project was to forecast the weather-dependent sales component for the coming week.

In this article, I do not wish to go into the nitty-gritty of mathematical model selection and implementation. However, a high-level understanding of the project management framework used to execute the project is outlined below. I will describe the cross-industry standard process for data mining (CRISP) data science framework for problem-solving and elaborate on how I went about solving this problem.

The CRISP data science framework consists of:

1.) Business Understanding

2.) Data Understanding

3.) Data Preparation

4.) Modelling

5.) Evaluation

6.) Deployment & Presentation

#machine-learning #retail #weather-analytics #forecasting #retail-technology