Customer Lifetime Value [Retail & Consumer Goods]
2.8 هزار بار بازدید -
4 سال پیش
-
In this webinar, BlueGranite shows
In this webinar, BlueGranite shows you how easy it is to leverage Databricks' accelerator notebooks on Microsoft Azure, to uncover and analyze your Customer Lifetime Value metrics. Check out our other videos here: https://bit.ly/3liRquT
Research has shown that increasing customer retention by 5% can lead to increased profits of 25% to 95%. Understanding and analyzing your brand’s Customer Lifetime Value is the key to unlocking insights that can help you increase your customer loyalty and profitability.
Given a customer’s order history (which includes the frequency and value of prior purchases), the goals of this analysis will be to predict:
How many future purchases can we expect this customer to make within a given timeframe?
What is the probability that the customer is still alive within a future timeframe?
What is the conditional, expected average lifetime value in monetary terms of the customer?
The models used to derive these predictions are the BG/NBD model and the Gamma-Gamma submodel.
By understanding a customers’ future value and likelihood of returning, we can use this information to guide other analytics and business decisions.
Session Agenda
- How Customer Lifetime Value can improve profitability
- Demonstration of the Databricks accelerator notebook for CLV
- How to leverage machine learning opportunities such as recommender systems and cognitive services
BlueGranite: https://www.bluegranite.com/
Blog: https://www.bluegranite.com/blog
Live and Recorded Events: https://www.bluegranite.com/events
Don't forget to SUBSCRIBE and hit the bell icon for notifications about new Modern BI, Data Platform, and AI content from the BlueGranite channel: https://bit.ly/2TXTZGs
#Retail #Databricks #Azure
Research has shown that increasing customer retention by 5% can lead to increased profits of 25% to 95%. Understanding and analyzing your brand’s Customer Lifetime Value is the key to unlocking insights that can help you increase your customer loyalty and profitability.
Given a customer’s order history (which includes the frequency and value of prior purchases), the goals of this analysis will be to predict:
How many future purchases can we expect this customer to make within a given timeframe?
What is the probability that the customer is still alive within a future timeframe?
What is the conditional, expected average lifetime value in monetary terms of the customer?
The models used to derive these predictions are the BG/NBD model and the Gamma-Gamma submodel.
By understanding a customers’ future value and likelihood of returning, we can use this information to guide other analytics and business decisions.
Session Agenda
- How Customer Lifetime Value can improve profitability
- Demonstration of the Databricks accelerator notebook for CLV
- How to leverage machine learning opportunities such as recommender systems and cognitive services
BlueGranite: https://www.bluegranite.com/
Blog: https://www.bluegranite.com/blog
Live and Recorded Events: https://www.bluegranite.com/events
Don't forget to SUBSCRIBE and hit the bell icon for notifications about new Modern BI, Data Platform, and AI content from the BlueGranite channel: https://bit.ly/2TXTZGs
#Retail #Databricks #Azure
4 سال پیش
در تاریخ 1399/06/12 منتشر شده
است.
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