You can use Actable AI's no-code Data Science platform to predict the number of products a customer will purchase form a store. Knowing the order size will allow the store management to calculate its revenue and profits in advance enabling them to improve their planning and decision making. Furthermore, knowing how many items will be sold for any given period will allow the supermarket to optimize its stocks. You can use similar analytics to actually understand which of your products sells well and why in advance to allow you to make sure the in demand products will always be in stock.
In this specific example, we use our predictive analytics (classification) to estimate the quantity of products sold from historical sales data. Furthermore, you can use our causal inference, de-biasing and counterfactual analysis to better understand the underlying drivers of why some product might sell better vs others or if your marketing is actually increasing your sales or now.
The dataset consist of three month sales data (17 columns and 1000 rows) for a supermarket with 3 branches. Below are the 17 variables and their descriptions.
Invoice id: Computer generated sales slip invoice identification number
Branch: Branch of supercenter (3 branches are available identified by A, B and C).
City: Location of supercenters
Customer type: Type of customers, recorded by Members for customers using member card and Normal for without member card.
Gender: Gender type of customer
Product line: General item categorization groups - Electronic accessories, Fashion accessories, Food and beverages, Health and beauty, Home and lifestyle, Sports and travel
Unit price: Price of each product in $
Quantity: Number of products purchased by customer
Tax: 5% tax fee for customer buying
Total: Total price including tax
Date: Date of purchase (Record available from January 2019 to March 2019)
Time: Purchase time (10am to 9pm)
Payment: Payment used by customer for purchase (3 methods are available – Cash, Credit card and Ewallet)
COGS: Cost of goods sold
Gross margin percentage: Gross margin percentage
Gross income: Gross income
Rating: Customer stratification rating on their overall shopping experience (On a scale of 1 to 10)
