Review the best practices for devising and executing customer analytics processes within a business.
- [Instructor] Predictive customer analytics…does not work in a vacuum.…It needs data, people, and processes to come together…to create continuous value.…Here are some of the tips to devise a successful process.…Create a project for every unique problem…or opportunity identified.…Each project must have a goal, sponsor, and owners.…Build the right team for the project.…You need a diverse set of experts…ranging from marketing and sales to engineers.…
Data scientists are also needed to create insights.…Conduct periodic status updates.…The process needs constant improvisation…and everyone needs to pitch in and contribute to it.…Plan for multiple iterations.…Each iteration needs data preparation,…analysis, prediction, experimentation, and review.…Review progress and results with stakeholders.…Incorporate simulations and experiments.…Simulation helps you understand how external factors…might influence customer decision making.…
Experimentation like A/B testing…will help you validate if predictions…are going to hold good in the field.…
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
Skill Level Intermediate
Business Analytics: Prescriptive Analyticswith Alan Simon2h 40m Intermediate
Data Science Foundations: Python Scientific Stackwith Miki Tebeka3h 37m Intermediate
1. Customer Analytics Overview
2. Will You Become My Customer?
3. What Else Are You Interested In?
4. How Much Is Your Future Business Worth?
5. Are You Happy With Me?
6. Will You Leave Me?
7. Best Practices
Choose the right data1m 19s
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