Revology Analytics

AI/ML is not the ultimate solution for every data-related problem. We must first set up foundational descriptive and diagnostic analytics capabilities and more straightforward ML approaches before applying more advanced techniques.

It's essential to understand the business problems and work closely with functional partners to solve them in a way that aligns well with the company's analytical readiness and operating rhythm.

Beyond the Hype: Practical Revenue Growth Analytics Use Cases that Drive Impact — Revology Analytics AI/ML is not the ultimate solution for every data-related problem. We must first set up foundational descriptive and diagnostic analytics capabilities and more straightforward ML approaches before applying more advanced techniques. It's essential to understand the business problems and work closely

The future of analytics belongs to this new breed of business analytics professionals, who bring together advanced analytics and functional business expertise (pricing, sales & marketing, finance, supply chain) to uncover hidden value for companies.

The Business Analyst of Tomorrow — Revology Analytics Data science is an ever-evolving field, and its roles are also changing. As businesses increasingly rely on data to inform their decisions, there is a growing need for people with both the technical skills and domain/industry expertise to drive measurable value.

It often amuses me that in the era of ChatGPT (and despite the countless books I have in my library on AI/ML), I've never used any Deep Learning model for any of my client engagements or prior advanced analytics leadership roles.

If you need to use Machine Learning, keep it simple! — Revology Analytics It often amuses me that in the era of ChatGPT (and despite the countless books I have in my library on AI/ML), I've never used any Deep Learning model for any of my client engagements or prior advanced analytics leadership roles. For 99.95% of data problems in traditional (non-tech) companies, De

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

This short video will show you a simple Gross Profit Growth Deep Dive built on customer transactional data (using Tableau). Be sure to watch it on full screen.

RA Quick Insights Video Series: Driving rapid margin actions with transactional data analysis (Part III. - Gross Profit Growth Deep Dive) — Revology Analytics Companies are in the business of making money, and most often, they care about maximizing their Revenues, Gross Profit, and Operating Income. One of the biggest challenges Finance and Revenue Management teams face is the ability to systematically diagnose the drivers of fundamental business performa

Discount Curve Analysis (DCA) is an essential but often underused method that summarizes the % of Units (or Cumulative % of Units) sold at each 1% Price Discount (from 0% to 100%).

Check out my video tutorial that explains this important Revenue Analytics technique.

RA Quick Insights Video Series: Driving rapid margin actions with transactional data analysis (Part II. - Discount Curve Analysis) — Revology Analytics This week, I wanted to show you another simple yet effective revenue analytics technique to steer sales behavior in the right direction and drive Gross Profits.

Check out my podcast episode with Mark Stiving of Impact Pricing, where we talk about Revenue Growth Analytics (with a heavier focus on Pricing/Margin Analytics).

Mark is a brilliant pricing strategist and a highly sought-after Value-Based Sales & Pricing Transformation expert.

A former salesperson turned Ph.D., he has a unique, unicorn-like perspective that combines the most impactful elements from the world of Sales, Academia, and Revenue Management.

I've been an early follower of Impact Pricing and have read (most of) Mark's Pricing books early in my career. I encourage those interested in learning about impactful, pragmatic ways to grow Sales & Profits to check out the Impact Pricing podcasts.

Pricing Data Analytics: The Hows and Whys with Armin Kakas Armin Kakas is an expert in analytics, having lots of education in statistics, machine learning and A.I. He has an MBA, and he was a former VP of

It often amuses me that in the era of ChatGPT (and despite the countless books I have in my library on AI/ML), I've never used any Deep Learning model for any of my client engagements or prior advanced analytics leadership roles.

For 99.95% of data problems in traditional (non-tech) companies, Deep Learning (and any of its derivations like LSTMs, GANs, CNNs, etc.) are overkill at best and a complete waste of time (or not applicable) at worst.

My original article below:

If you need to use Machine Learning, keep it simple! — Revology Analytics It often amuses me that in the era of ChatGPT (and despite the countless books I have in my library on AI/ML), I've never used any Deep Learning model for any of my client engagements or prior advanced analytics leadership roles. For 99.95% of data problems in traditional (non-tech) companies, De

Today’s Data Scientist is Tomorrow’s (well-paid) Business Analyst!

Side note: let’s not call it Data Science anymore…

Today’s Data Scientist is Tomorrow’s (well-paid) Business Analyst — Revology Analytics Data science is an ever-evolving field, and its roles are also changing. As businesses increasingly rely on data to inform their decisions, there is a growing need for people with both the technical skills and domain/industry expertise to drive measurable value.

If you're interested in Machine Learning and data science, don't limit yourself to reading articles, or watching videos & tutorials. Instead, take the initiative to start building your skills today.

Become proficient in SQL, Python, or R to help you get started then spend the nights & weekends working with real data.

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)