Big Mart Sales Analysis 2016-2019
Completion Date:
Project Overview
Summary:
The dataset Analysis at Big Mart was collected for sales from 2016 to 2019 sales data for 1559 products across 10 stores in different cities. The aim is to build a predictive model and predict the sales of each product at a particular outlet. Using this model, Big Mart will try to understand the properties of products and outlets which play a key role in increasing sales.
Role:
Problem Statement
The Franchise Sector is one of most entrenched part of the United States business landscape. Across the country, there is a wide variety of franchised businesses with over 300 different categories of products and services offered. Although this business yields high revenues, it needs a lot of Capital to get started and also the to know the right products to sell. Franchise businesses accounts for about $1.3 trillion of the total revenues of the United States.
Executive Summary
Data Dictionary
Key Takeaways
Next Steps
Suggestions:
1. Listen on what customers wants and customers reviews/ratings
Surveys, store walks and input from your staff helps you feel the pulse of the business. It is good to check on the customers reviews on all media platforms like google, Facebook, this is one of the ways to know if the customers are satisfied with the services offered or how they can be improved.
2.Build a successful business track record
A good example is in the Month of October 2017, the data Shows Sales per Month decreased by half comparing to the previous month. It is advisable to research what could have caused the increment so that it doesn\'t occur again in future.
3. Offer excellent Customer Service
A happy customer will always come back, this is one of the strategies that keeps a business booming. Probability of the happy customer referring another customer is 90% which can tremendously boost a company\'s business sales.
4. Good keeping of business records
Knowing the business ways of operation and checking on the books often will help the owner to determine the best decision to make in order to yield revenues. For Example, in June 2017, the sales Year over Year fell to -25%, and then the following year same month it was high as 119%, this was a big turn over in terms of revenue. This shows very good business etiquette were kept in place.
Deployment
