Data Science Case Study: Optimizing Product Placement in Retail (Part 1)
In a previous post, I wrote about an approach that I take to creating value with my data science project. To quickly recap and summarize what I said in that post, the goal of Data Science is to empowering better decision making. Doing this requires that we have the empathy to ensure to the right questions and that we use the right information.
When juxtaposed against the Value Proposition Canvas, data science projects can be seen as products that meet the needs of our customers (namely decision making), deal with the challenges associated to making those decisions and maximize the benefits to be gained from making the right decisions.
The Data
For today’s post, the dataset I’m going to use comes from Analytics Vidhya’s ‘Big Mart Sales III’ dataset which is available through one of their practice competitions. You can take a look using the link below.
Data Description (Taken from the Competition Site)
The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined.
The data contained in the dataset is as follows:
- Item_Identifier: Unique product ID
- Item_Weight Weight of product
- Item_Fat_Content: Whether the product is low fat or not
- Item_Visibility: The % of total display area of all products in a store allocated to the particular product
- Item_Type: The category to which the product belongs
- Item_MRP: Maximum Retail Price (list price) of the product
- Outlet_Identifier: Unique store ID
- Outlet_Establishment_Year: The year in which store was established
- Outlet_Size: The size of the store in terms of ground area covered
- Outlet_Location_Type: The type of city in which the store is located
- Outlet_Type: Whether the outlet is just a grocery store or some sort of supermarket
- Item_Outlet_Sales: Sales of the product in the particular store.
Problem Definition
As stated previously, in this project, we will try to find the best product placement options for maximising the Item_Outlet_Sales variable. We will do so by creating a model to forecast that value for certain items, then suggest possible ways of improving that product’s placement.
Using what we know to create our customer profile we get:
- Job: Optimizing product placement
- Pains: Ignorance of the factors that influence sales
- Gains: Insight into customer preferences.
Formulating an Approach
To create the right data product, this is what we’ll do:
- Build a Model for creating sales forecasts
- Understand what affects sales
- Based on what affects sales, provide suggestions for increasing originals forecasts
