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Armin

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Armin

CRM system hygiene a top data priority

Fixing our CRM data hygiene should be a top leadership priority to drive sales productivity and revenue growth. However, for many B2B environments particularly in Manufacturing and Wholesale, there’s often a big disconnect between strategy and execution. Companies spend a disproportionate time and investment on market research studies to understand their buyer archetypes and personas, only to stop at great Power Points, executive updates and cross-functional pontifications.

Meanwhile, the CRM systems are plagued by outdated and missing data and no value- or needs-based segmentation information, which lays the foundation for automated lead scoring or prescriptive capabilities like upsell, cross-sell or churn mitigation.

Now is the time to act and start treating our holistic CRM data with the attention and priority it deserves!

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Armin

How US Mobility has Eroded and Accelerated Retail Sales from 2020-22

The Apple Mobility index, published from January 13, 2020 to April 13, 2022, tracked the relative volume of map direction requests for various cities, regions and countries as people were driving, walking or taking public transportation to places.
As the US went into a lockdown in early 2020, key retail segments suffered tremendously, but with many recovering and accelerating their performance thanks to stimulus checks and the pent-up need for consumers to be out & about again.

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Armin

Marketing Mix Modeling is Here To Stay

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.

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Armin

An executive’s advice for data scientists (and leadership) – Part II.

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.

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Armin

A Guide to Rate-Mix Modeling to Accelerate Margin Performance

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.

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Armin

An executive’s career advice for data scientists – Part I.

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!

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Armin

The Science (and Art) of Estimating Price Elasticities

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.

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Armin

Monetize your Data with Operational Optimization

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.

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Armin

Building analytics teams for real impact

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.

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Have a Revenue Growth Analytics pain point, a question, or a content suggestion?

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