Learn about the various sources of customer data, and why they are important.
- [Instructor] Predictive customer analytics…needs data that carries signals…about the customers' intentions and behavior.…It is important to identify the right data sources…that provide these signals and use them for model building.…The customer-business relationship…consists of four entities,…the customer himself,…the product or service the customer buys or uses,…the channels of communication, like email, web chat,…and finally, the customer-facing agents…representing the company, like sales or support personnel.…
There are two types of data…that are required for customer analytics.…The first is the attributes of the entities,…and the second are the events in which…the entities participate.…Entities can be customers, products, channels, or agents.…Attributes of the customer include demographics, income,…age, gender, location, et cetera.…Attributes of the product include type,…price, quality, et cetera.…Attributes of the channel include type,…frequency of use, and response times.…
And attributes of the agents include handle times,…
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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.
- 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 34m 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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