Revology Analytics

RFM (Recency-Frequency-Monetary) Analysis is a critical Revenue Growth Analytics technique that sets the foundations for answering the above questions.

RFM Analysis has traditionally been employed in the Marketing domain, although it applies to any functional domain that touches the customer (Pricing, Supply Chain, A/R, Customer Service, etc.).

It's a simple analytical technique but highly effective at driving customer insights that lead to improved customer retention, increased profits, and greater customer satisfaction through smarter and more surgical sales and marketing campaigns.

Read my brief article below:

RFM Analysis as an Important Revenue Growth Analytics Capability — Revology Analytics Revenue Growth Analytics (RGA) is a foundational enabler for organizations looking to transform their Revenue Growth Management strategies. RGA goes beyond traditional pricing techniques and provides insights into areas such as customer mix management, customer retention and cross-sell opportunities

Having completed over 30 Coursera courses and certifications and read over 50 books on stats/ML/analytics throughout my career, I learned more about the field than I ever could in a graduate program (save a Ph.D.).

Here's my main advice for aspiring analysts, data scientists, or working professionals who want to up their game with advanced analytics and machine learning in a real, pragmatic way that lets them retain key learnings and add value to their companies:

When you complete a Coursera, Udemy, Datacamp, etc., pause for a few weeks and apply the learnings with real data.

You just learned how to predict customer churn using Random Forest?

That's awesome!

You should spend some nights & weekends building a customer churn model for your business unit using real transactional and CRM data.

Doing the alternating coursework to real-world project method has several advantages:

1. Working through real, impactful projects after completing courses is a great way to consolidate your newly-acquired knowledge and test your understanding.

2. Taking on multiple projects provides you with a deeper understanding of how all the skills, concepts, and algorithms fit together, giving you invaluable experience when it comes to building & presenting an analytics solution.

3. Projects also benefit your long-term knowledge retention, enabling greater experimentation and increased confidence when working with different datasets to solve challenging business problems.

4. Doing the above also shows prospective employers that you have initiative and a dedication to learning more about the field, conveying that you understand the subject matter fully and have taken steps towards mastering it independently.

For those wanting to augment your , science, or chops, the top 25 books I've read over the past 15 years encompassing Analytics, Machine Learning, and Pricing are the following...

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+ If you're starting with stats:

1.

"An Introduction to Statistical Methods and Data Analysis" (by Ott and Longnecker)
2. "Discovering Statistics Using R" (by Field and Miles)

+ When you want to go deeper with stats and march towards ML:
3. "An Introduction to Statistical Learning"
4.

"The Elements of Statistical Learning" (by Hastie and Tibshirani)

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+If you are a business practitioner or analyst who wants to learn the basics of Data Science / Machine Learning:

5. "R for Data Science" (by Grolemund and Wickham)
6.

"Practical Data Science with R" (by Zumel and Mount)
7. "Data Science for Business" (by Provost and Fawcett)
8. "Applied Predictive Modeling" (by Kuhn and Johnson)
9. "Text Mining with R" (by Silge and Robinson)

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+If you are a business analyst who wants to learn more about NLP and Deep Learning:

10. "Practical Text Mining" (by Miner and Elder)
11. "Text Analytics with Python" (by Sarkar)
12. "Natural Language Processing in Action" (by Lane, Howard, et al.)
13.

"Deep Learning" (by Goodfellow and Bengio)
14. "GANs in Action" (by Langr and Bok)

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+If you are a data scientist, analyst, finance, or pricing practitioner who wants to learn more about the science and art of Pricing:

15.

"Confessions of the Pricing Man" (by Simon)
16. "Power Pricing" (by Dolan and Simon)
17. "Segmentation, Revenue Management, and Pricing Analytics" (by Bodea and Ferguson)
18. "Pricing Done Right" (by Smith)
19. "Impact Pricing" (by Stiving)
20.

"The 1% Windfall" (by R. Mohammed)
21. "Smart Pricing" (by Raju and Zhang)
22. "Pricing and Profitability Management" (by Meehan, Simonetto, et al.)
23. "Pricing and Revenue Optimization" (by Phillips)
24. "Promotion Dynamics" (by Neslin and van Heerde)

25. "Value First, Then Price" (by Hinterhuber and Snelgrove)