Learn about the process of upselling and cross-selling to existing customers, and how that's important to a business.
- [Narrator] Imagine this.…Roger just bought a laptop online.…But let's think.…Is a laptop the only item on a shopping list?…He might need charging stations, monitors,…and warranties.…He might also be shopping for his middle school kid…and need other laptops and electronics.…What's my point?…Well, knowledge of the customer's desires and situations…and upselling cross-selling additional items…is easy money for any business.…
If you can predict Roger's shopping list,…then you can sell him a lot more.…More sales means more profit.…You might have heard the sales lingo of upsells…and cross-sells.…Upsell means selling additional items…that complement the items purchased.…So if Roger purchases a laptop,…an upsell would be cables, a bag, or a warranty.…Cross-sell means selling items…in other independent categories…the customer might be interested in.…A person buying a high-end laptop…is most probably a technical person,…and he might be interested…in technology-related books or other items…like mobile phones.…
Now, you might be wondering,…
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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