Learn about the various phases and types of customer analytics, what data they use, and what results are generated.
- [Instructor] Okay, so now you know what…Predictive Customer Analytics is and how it is used.…Let's briefly touch upon the process…of Predictive Customer Analytics.…Predictive Customer Analytics…is a continuous improvement model.…It needs a well laid-out process within the business…to work and deliver.…The process starts with identifying use cases…where predictive customer analytics can help the company.…Once the use cases are identified,…architects need to identify the data sources…that can be utilized for this purpose…then data pipelines need to be built…to acquire, process, integrate and store the data.…
Then data scientists need to get to work…to mine the data and build models.…Models need to be tested for accuracy…before they can be deployed.…The performance of the models needs to be monitored…and the models need to be fine-tuned.…As time goes on, additional data elements…can be added in for better prediction work.…I feel obligated to mention that, for any of this to work,…there needs to be management buy-in.…
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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