Review the best practices for designing data processing pipelines that ensure timeliness and integrity.
- [Narrator] A very important aspect…of successful customer analytics…is the data pipeline and processing infrastructure…within your business.…This infrastructure ensures that data is available…for analytics in a timely manner…and guarantees its accuracy.…The following are some of the recommended best practices…for building data processing pipelines.…Provide mechanisms for republishing and reprocessing data…to catch up for gaps and interruptions.…Work with data provider organizations…to make sure that any gaps in data availability,…security, and format are taken care of.…
Separate real time and historical data feeds if required.…Real time data is expected to be fast,…but can suffer from data loss.…Historical data, however, is batch-oriented,…and accuracy of data is far more important.…While it is a good design goal to build the same pipeline…for both real time and historical,…do not try to over-design to make this happen.…It only makes problems worse.…Some parts of the pipeline can be different…for real time and historical data.…
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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