Predictive Customer Analytics
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.
0. Introduction
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001 Welcome
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002 Expectations and course organization |
003 Use the exercise files
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1. Customer Analytics Overview
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004 The importance of customer analytics
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005 The customer life cycle
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006 Apply analytics to the customer life cycle
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007 Sources of customer data
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008 The customer analytics process
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009 Use case - Online computer store
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2. Will you become My Customer
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010 The customer acquisition process
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011 Find high propensity prospects
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012 Recommend the best channels for contact
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013 Offer chat based on visitor propensity
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014 Use case - Determine customer propensity
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3. What else are you interested in
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015 Upselling and cross-selling
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016 Find items bought together
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017 Create customer group preferences
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018 User-item affinity and recommendations
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019 Use case - Recommend items
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4. How much is you future business worth
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020 Generate customer loyalty
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021 Create customer value classes
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022 Discover response patterns
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023 Predict customer lifetime value (CLV)
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024 Use case - Predict CLV
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5. Are you happy with me
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025 Improve customer satisfaction
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026 Predict intent of contact
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027 Find unsatisfied customers
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028 Group problem types
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029 Use case - Group problem types
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6. Will you leave me
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030 Prevent customer attrition
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031 Predict customers who might leave
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032 Find incentives
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033 Discover customer attrition patterns
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034 Use case - Customer patterns
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7. Best Practices
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035 Devise customer analytics processes
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036 Choose the right data
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037 Design data processing pipelines
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038 Implement continuous improvement
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Conclusion
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Next steps
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