Discuss how ensuring that customers are happy plays a vital role in the success of a business.
- [Instructor] So far, in the story,…Roger has purchased a laptop.…Now, imagine that he's trying to set it up…in his home office.…Upon doing so, he finds that his laptop…keeps loosing connection with his wireless keyboard.…He thinks he messed up the setup…and he tries to find ways to fix it.…He goes to the customer website…to see if there are any self-help videos…that will help him identify the cause.…He can not find any.…He then calls the company's 1-800 number.…It goes to a self-service prompt…that asks him for all his information.…
He's then put on a queue for the next 15 minutes.…Eventually, he gets to an agent who asks him for…all the information all over again.…As you can probably imagine, Roger is getting irritated.…After listening to his problem…the agent provides him a couple of instructions…over the phone, but Roger is unable to understand…and follow them.…Roger requests for in-house help…but the agent declines it, saying,…he does not have that covered under his warranty.…Frustrated, Roger is thinking of never buying…
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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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