Thanks to the demise of 3rd party digital IDs, our old friend Marketing Mix Modeling (MMM) is here to stay! Learn about MMM, how it's done, and how it can be highly impactful to you and your company.

In a previous article, I wrote about the three main structural challenges that data scientists and their organizations face when maximizing their career satisfaction and business impact (data ROI).

This edition of Revology Analytics Insider will dive deeper into the first impediment ("mismatch between data scientist aspirations and corporate reality"). We'll decompose why it exists and make concrete recommendations to the data science community and company leaders on how to best address it.

Companies are in the business of making money, and most often they care about maximizing their Revenues, Gross profit or Operating income. One of the biggest challenges companies face is the ability to correctly and systematically diagnose and isolate the individual drivers of key business performance changes and build fast, surgical actions to increase profitability. This week I talk about the benefits of doing a proper price-cost-volume-mix analysis for your business, and get you started on building a production level application with analysis examples and explanations in Excel and R.

Data science, AI and ML have been overhyped for at least the last decade, resulting in often unrealistic and misaligned expectations between data scientists and employers. Over the next couple of weeks, I shed light on the three major challenges data scientists typically encounter in their companies and provide concrete suggestions on how to tackle them for both personal and organizational success. Would love to hear from you about your experiences!

Most of us are familiar with the term customer price sensitivity as an important concept especially for sales, marketing and revenue management teams. It helps us understand how price changes affect demand, profitability or market share of our products or services. This week, I will describe the most popular analytical methods that help you measure your product or service price elasticities, including a few simple and proven machine learning based approaches that your analytics or data science teams can easily do.

Last week I wrote about the key tenets for building analytics teams for real, measurable impact in your organization. This week, I’ll focus on one of the four fundamental #datamonetization strategies that companies should employ: capitalizing on their data assets to deploy #operational improvement initiatives that drive cost savings, revenue increases or both. Operational #optimization initiatives are usually a good place for companies to start their #analytics journey, assuming some foundational data capabilities are already in place: reliable internal data, decent #datagovernance and tech stack, a good understanding of customer behavioral profiles and foundational #datascience capabilities.

Read about key analytics use cases across three industries that optimize operational processes to drive real performance. If you have your own analytics use case stories from the trenches (successes or lessons learned), or just want to chat analytics, machine learning or revenue management, drop me a note.

Most analytics transformation efforts do not deliver a positive ROI for the enterprise even after several years. CEOs and their boards know they need to execute an AI-led differentiation strategy to either future-proof themselves, address an existential risk in the marketplace, or simply to make some operational improvements to their business. Yet, according to most estimates, 75-90% of digital transformations fail and less than 1 in 5 companies have fully extracted value out of their analytics journey. Most organizations fail on the last-mile delivery – in other words, front-line employees and decision-makers are not using the analytics tools and processes as intended, or not using at all.
Over the last decade of leading analytics teams, I have succeeded and failed many times. Below are five lessons learned from these experiences. I hope it can serve as a helpful 10K foot roadmap for nascent or aspiring analytics leaders or seasoned business executives who want to build a sustainable practice that adds real, quantifiable value for their company and its customers.