Книга Fighting Churn with Data

Формат
Мова книги
Видавництво
Рік видання

Churn is the bane of any subscription business, such as content subscriptions, software as a service, and even ad-supported freemium apps. You can improve customer retention through product changes and targeted engagement campaigns based on data-driven interventions.

 

This hands-on guide is packed with techniques for converting raw data into measurable metrics, testing hypotheses, and presenting findings that are easily understandable to non-technical decision makers.

 

Don’t let your hard-won customers vanish from subscription services, taking their money with them. In Fighting Churn with Data you’ll learn powerful data-driven techniques to maximize customer retention and minimize actions that cause them to stop engaging or unsubscribe altogether.

 

•   Identifying processes suited to machine learning

•   Using machine learning to automate back office processes

•   Seven everyday business process projects

•   Using open source and cloud-based tools

•   Case studies for machine learning decision making

 

For readers with basic data analysis skills, including Python and SQL.

Код товару
20645112
Характеристики
Тип обкладинки
М'яка
Мова
Англійська
Доставка та оплата
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Опис книги

Churn is the bane of any subscription business, such as content subscriptions, software as a service, and even ad-supported freemium apps. You can improve customer retention through product changes and targeted engagement campaigns based on data-driven interventions.

 

This hands-on guide is packed with techniques for converting raw data into measurable metrics, testing hypotheses, and presenting findings that are easily understandable to non-technical decision makers.

 

Don’t let your hard-won customers vanish from subscription services, taking their money with them. In Fighting Churn with Data you’ll learn powerful data-driven techniques to maximize customer retention and minimize actions that cause them to stop engaging or unsubscribe altogether.

 

•   Identifying processes suited to machine learning

•   Using machine learning to automate back office processes

•   Seven everyday business process projects

•   Using open source and cloud-based tools

•   Case studies for machine learning decision making

 

For readers with basic data analysis skills, including Python and SQL.

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