Lynda - Predictive Customer Analytics
Lynda - Predictive Customer Analytics
Size: 191 MB | Duration: 1h 38m | Video: AVC (.mp4) 1280x720 15&30fps | Audio: AAC 48KHz 2ch
Genre: eLearning | Level: Intermediate | Language: English
Size: 191 MB | Duration: 1h 38m | Video: AVC (.mp4) 1280x720 15&30fps | Audio: AAC 48KHz 2ch
Genre: eLearning | Level: Intermediate | Language: English
Use big data to tell your customer's story, with predictive analytics. In this course, you can learn about the customer life cycle and how predictive analytics can help improve every step of the customer journey. Start off by learning about the various phases in a customer's life cycle. Explore the data generated inside and outside your business, and ways the data can be collected and aggregated within your organization. Then review three use cases for predictive analytics in each phase of the customer's life cycle, including acquisition, upsell, service, and retention. For each phase, you also build one predictive analytics solution in Python. In the final videos, author Kumaran Ponnambalam introduces best practices for creating a customer analytics process from the ground up.
* Understanding the customer life cycle
* Acquiring customer data
* Applying big data concepts to your customer relationships
* Finding high propensity prospects
* Upselling by identifying related products and interests
* Generating customer loyalty by discovering response patterns
* Predicting customer lifetime value (CLV)
* Identifying dissatisfied customers
* Uncovering attrition patterns
* Applying predictive analytics in multiple use cases
* Designing data processing pipelines
* Implementing continuous improvement
* Acquiring customer data
* Applying big data concepts to your customer relationships
* Finding high propensity prospects
* Upselling by identifying related products and interests
* Generating customer loyalty by discovering response patterns
* Predicting customer lifetime value (CLV)
* Identifying dissatisfied customers
* Uncovering attrition patterns
* Applying predictive analytics in multiple use cases
* Designing data processing pipelines
* Implementing continuous improvement
Lynda - Predictive Customer Analytics
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