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Here’s the chronology of events: Month 1: Cold Start on a winter night Our first task AUTOMATING MACHINE LEARNING MONITORING: Imagine if what viral polite grandma was thinking when she was typing in her search query was actually true: that there is a human operator on the other side of the screen accepting queries on his/her whim, who then manually searches through websites and returns a list of relevant results.

It would be in such a pre-automated world where you could also imagine data scientists manually monitoring a long list of machine learning models for a long list of clients.

Feature Engineering: At Retention Science, we want to capture all sorts of variability in customer behavior in order to model behavior such as calculating purchase probability, predicting customer lifetime values, and optimizing which discounts are most appropriate for which customers.

For instance, with user data, we’re able to derive their average order value, location, age, which items users browsed recently, and so forth.

The data science team at Retention Science uses Welcome Purchase Probability to help sort out the good customers from the bad so marketers can spend less effort courting bad customers and more time engaging the good ones.Part 1 of the Retention metrics explained: Welcome Purchase Probability post explained why we use WPP and how it works.

Data Science Demo - Customer Churn Analysis

This introduction to Data Science provides a demonstration of analyzing customer data to predict churn using the R programming language. MetaScale walks ...

Using Survival Analysis to understand customer retention - Lorna Brightmore

PyData London 2018 In this talk, I'll show how we use techniques in Survival Analysis and Machine Learning to predict the time a customer (and their dog) will ...

Actionable Insights With Predictive Analytics For Marketers

Traditional marketing analytics or scoreboards are essential for evaluating the success or failure of past marketing activities. But today's marketers can leverage ...

In-Depth Customer Churn, New Customers & Lost Customers Examples In Power BI using DAX

Here we take a look at some customer churning / lost customers & new customers examples with DAX. This is not easy and you need to understand a lot before ...

Customer loyalty programmes... why bother! : Lance Walker at TEDxTeAro

Lance Walker is the CEO of Loyalty NZ, the company that runs New Zealand's largest and most successful coalition loyalty programme, Fly Buys. Lance will be ...

Amplify Customer Insights with Predictive Analytics

In this 60-minute webinar, we will explore: Customer Lifetime Value (CLV) – The solution helps you to understand the pay-offs of new customer acquisition ...

Growing Customer Lifetime Value with Best in Class Data Management Practices

Improve your customer retention through effective data management. Sometimes sorting through customer data to identify the most valuable way to engage your ...

Seven Steps to Customer Success at Scale - Customer Success Summit 2015

From Customer Success Summit 2015. Customer success has become one of the hottest capabilities technology companies find themselves investing in today.

Calculating Customer Lifetime Value 2015

Arkady Kleyner shares some of the fundamental aspects of calculating Customer Lifetime Value.

How to Increase Customer Retention by 10% with Predictive Customer Success

Learn how customer success teams at top B2B companies are increasing customer retention rates (and recurring revenue) by using predictive analytics to spot ...