Design a predictive analytics solution to identify unsatisfied customers based on feedback from customers with similar experiences.
- [Instructor] How does a business find out…if their customers are happy or unhappy…with their products or service?…Typically they do so with a survey…and compute an overall customer satisfaction score.…Surveys work great when customers answer them…but survey results are skewed in two primary ways.…First, unsatisfied customers typically take the surveys.…Think about it, since Roger is upset about the service…and product he's probably more willing…to spend time on a survey and vent,…while Jessica might not be that forthcoming.…
Second, only 10% of the customers respond to a survey.…So how does the business identify those silent,…unsatisfied customers who might be thinking of…or have already moved on to other options?…The goal of this use case is to build a model…that would predict a customer satisfaction score…for all its customers,…regardless of whether they filled out a survey.…The data used for building this model is from customers…who actually answered the survey themselves.…
The data included customer demographics…
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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