::: Please find my Kaggle notebook here: https://www.kaggle.com/blirinddanjolli/cltv-prediction-analysis to run codes together with dataset.
Investments are company’s commitment for better future. Each company has it’s own calculations to determine the amount of investment, direction and time of exposure. Also a lot work should be done to determine which way to promote/advert our next steps. But as economics is here to say, for sharing limited resources to limitless demands; every company also has limited resources to reach certain part of their customers.
But further of reaching them, we need to understand whether they will continue to buy from us. For example who will be our company’s top 10 most valuable customer 2 quarters later; or which ones aren’t buying anymore from our company so we can specify our adverts for them.
Let’s start.
We have one year dataset of a retail gift shop. This analysis will start as if dataset has no null and no minus values. So we have kind of a “half-baked” dataset.
Retail gift shop dataset’s variables are as below:
Invoice — Unique code for bill/invoice
StockCode — Unique code for product
Description — Product name
Quantity — Number of product sold in certain bill
InvoiceDate — Bill date and time
UnitPrice — Price of product
Customer ID — Unique customer ID for each customer
Country — Name of the country where sale occurs
BUSINESS PROBLEM: An e-commerce company wants to predict customers future purchases and determine business investment plan according to predicted data.
DATASET STORY: There is Online Retail II, 2010-2011 sheet file as dataset. Products sold are mostly souvenirs and most of the customers are corporates.
Thank you for your reading!
This work has been done with the support of www.veribilimiokulu.com.