Design a predictive analytics solution to identify and recommend the best channels to use to contact your high propensity prospects.
- [Instructor] Once you have a list…of prospects to reach out…you will need to decide how best to communicate with them.…There are multiple channels available,…like phone calls, emails, mobile,…or targeted ads on internet or social media.…But different people react differently to different media.…Roger likes to pay attention…to the marketing emails he gets.…He always clicks them and checks them out.…Jessica, on the other hand, filters such emails,…that go to her junk folder.…She, however, are shown a propensity…to respond at the pop-up during her browsing…based on her recent searches.…
The goal of this use case is to recommend the best channel…for contacting each prospective customer.…With so many different mediums available,…it is important to target customers…in such a way that will receive the most attention…and the highest return on investment.…What data would you use?…Prospect data is always going to be there.…You should also use data about past successful events,…events in the past that you reached out…to a specific prospect in a specific channel…
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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