Discuss the best practices for choosing the right data and maintaining the accuracy and integrity of your data.
- [Narrator] Data is the central piece…of predictive analytics; if you have the right data…and ask the right questions, your models…and predictions will be accurate.…If you use the wrong data, your business is going to…declare predictive analytics as justified.…Plan for enough data for building models.…The right amount of data will provide…consistent and stable models that…continue to provide accurate predictions.…Too small data asset results in inaccuracy.…Too much data will require more resources…and a longer processing times.…
Look for data from different sources…to capture different prospectives of customer behavior,…customer demographics, browsing data,…context in the data, social media data are examples…of data assets that apply to customer analytics.…Check sources of data for authenticity and accuracy.…Clean data in a methodical and repeatable way.…You should be able to automate it.…Evaluate data for outliers and eliminate unwanted data.…Plan for data delivery at required speed,…especially during real time predictions.…
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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