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Mastering Price Elasticity Modeling: Best Practices for 2024

Enhanced by AI and machine learning, price elasticity modeling enables businesses to optimize pricing strategies by understanding how customer demand reacts to price changes. Unlike traditional methods that often overlook the complexities of consumer behavior, these advanced models provide data-driven insights to set prices that maximize revenue.

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Smart Pricing and Revenue Growth Management for Auto Service, Repair and Tire Retailers

Join us for an insightful session as we explore smart pricing and analytics strategies tailored for tire & repair shops. Learn how to optimize pricing and discounting to drive car count, sales, and enhance profitability. This webinar is especially geared toward multi-location, regional, or super-regional/national retailers, as well as private equity operators in the space, looking to drive profitable growth.

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How to Build an Effective B2B Price Elasticity Model (in under 90 minutes)

Understanding the business impact of price changes and promotional investments is crucial for effective revenue management. In this webinar, we'll guide you through building a B2B Price Elasticity Model in under 90 minutes, using both Linear Regression and Machine Learning methods. Learn how to increase price realization, optimize inventory, and drive net sales and gross profits. Attendees will receive a comprehensive blueprint and R code to implement in their organizations.

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Case Studies Armin Kakas Case Studies Armin Kakas

A Data-Informed Approach to Promotion and Marketing Spend Optimization in Thrift Retailing

A leading West Coast thrift retailer with $350M in revenue needed to optimize its promotional and marketing spending to support growth. Revology Analytics implemented comprehensive analytical models and a custom dashboard to guide strategic couponing and promotional bundling. This approach identified $80M in growth opportunities and reallocated marketing budgets for enhanced effectiveness, positioning the retailer for sustained growth.

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Building a Dynamic Pricing Capability (In Under 90 Days)

Many mid-market retailers and wholesalers' pricing, finance, and merchandising executives have inherited outdated pricing solutions. These legacy pricing processes don't quickly scale and heavily rely on expensive human, mundane tasks. There are no intelligent dynamic pricing capabilities that automatically set prices based on sales patterns, corporate objectives, and changing marketplace behavior. It often results in missed profit opportunities and liquidity problems.

Fortunately, you can build simple yet effective dynamic pricing solutions in 3-to 4 months that achieve up to 80% of its incremental Gross Profit $ potential. You can design and implement using practical methods and accessible technologies that empower your teams to take complete control without expensive 3rd party support.

Read more below about how you can achieve this and unlock an extra 1-3% in GP% in year 1.

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Give the Gift of Revenue Growth Analytics Skills

Kickstart the New Year with a game-changing gift for your team!

Discover our specialized corporate training programs in Advanced Revenue Growth Analytics, designed to fill the practical skill gaps in Revenue Growth Analytics (RGA) that many companies face.

It's the perfect opportunity to empower your commercial and analytics/data science teams with hands-on, real-world skills that immediately impact your bottom line.

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Taking Charge of Your Revenue Growth Analytics

Revology Analytics had the opportunity to join 25 leading Revenue Growth Management executives in CPG and Manufacturing in St. Louis earlier this week. We discussed the state of Revenue Growth Analytics in the CPG industry and the significant opportunity to in-source and own many of the advanced analytics capabilities foundational to a successful RGM and Pricing strategy.

In many ways, the state of Revenue Growth Management and its associated analytics capabilities have not changed meaningfully in the last ten years for Consumer Goods companies. There's still a heavy reliance on data and information providers like Nielsen and IRI to carry out foundational Pricing & Promotional Analytics efforts, including Price Elasticity and Marketing Mix Modeling, or a heavy reliance on 3rd party software for price analytics, scenario analyses, and optimization efforts.

However, the world is changing....fast.

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Case Studies Armin Kakas Case Studies Armin Kakas

Optimizing Medical Device Gross Profits with Dynamic B2B Margin Analytics Platform - Case Study

This case study explores how a prominent med-tech company revolutionized its margin management by developing an in-house Dynamic B2B Margin Analytics Platform. Faced with challenges such as limited visibility into pricing and margin drivers and inconsistent discounting practices, the company recognized the need for advanced pricing analytics capabilities. Partnering with Revology Analytics, they embarked on a strategic journey to enhance their pricing analytical acumen, aiming to improve net price realization and address revenue leakages. The case details the transformative impact of this initiative, demonstrating the effectiveness of co-creating an in-sourced solution with internal stakeholders, using an existing tech stack like Tableau for powerful visualization and scenario analysis capabilities.

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Building Dynamic Pricing for Fortune 500 Specialty Retailer - Case Study

In this case study, we delve into the strategic implementation of dynamic pricing by a Fortune 500 specialty retailer, aiming to enhance market share and profitability in their brick-and-mortar (B&M) channel. Despite the success of dynamic pricing in their e-commerce channel, the retailer faced unique challenges in translating this strategy to their physical stores, constituting 90% of their sales. The initiative encountered skepticism and logistical hurdles in frequently changing price tags. In partnership with Revology Analytics, the retailer overcame these challenges through a practical, simple-to-understand dynamic pricing solution, resulting in increased gross margins, fast and cost-effective implementation, and successful stakeholder engagement in their dynamic pricing journey.

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Solving Unproductive Inventory Challenge with Dynamic Markdown Pricing - Case Study

Learn how a leading North American consumer durables distributor improved liquidity and profitability with Revology Analytics' Markdown Price Optimization solution. Discover how the solution increased Gross Profit by 5% annually, enhanced inventory liquidity by 30%, and saved significant labor hours for pricing and sales teams. Explore the benefits of advanced inventory management for sustained growth and financial stability.

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Driving Manufacturer Gross Profit through Promotion Effectiveness & Optimization - Case Study

In this case study, we explore how a $1.5B privately-held Consumer Packaged Goods manufacturer successfully navigated the challenges of a competitive market. Facing declining market share and profitability, the company struggled with ineffective promotional strategies, leading to eroded gross margins in the face of increased promotional spending. The company collaborated with Revology Analytics to develop the Promotion Effectiveness and Optimization platform using the client's existing tech stack. This included the development of a Pricing & Promotions data warehouse in Azure and Diagnostic and Predictive Analytics capabilities for Pricing & Promotions using Tableau and R. The case study offers valuable insights into overcoming obstacles in planning and profitability, showcasing the significant improvements in retail buyer engagement, gross profit, promotional ROI, and market share.

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The Merits of Aggregated Data for Demand and Price Elasticity Modeling

It's time to rethink traditional sales and price elasticity modeling methods.

Aggregated data at the retail chain level and modern machine learning models are vital in shaping an efficient, practical, and robust approach for predicting and explaining retail sales.

Why this shift? The traditional reliance on granular, disaggregated (i.e., store-product-day level) data and old-school models are costly, complex, and often misaligned with practical management strategies.

Turning to aggregated data and modern ML approaches reduces costs, enhances model accuracy and efficiency, and makes these valuable insights more accessible to smaller firms and faster for larger ones.

Moreover, in-sourcing these capabilities builds critical, sustainable expertise for Revenue Growth Management, freeing companies from reliance on 3rd parties. This shift signifies an effective and sustainable future for Revenue Analytics and redefines competitive positioning, enabling superior service and product offerings.

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Unveiling Your Revenue Growth Analytics Maturity: Unlock Your Full Profit Potential

Discover the power of Revenue Growth Analytics with our newly launched Maturity Scorecard, designed to assess your organization's capabilities in Margin Analytics & Optimization, Sales & Customer Growth Analytics, and Promotion Effectiveness & Optimization.

Take the 3-minute assessment and gain access to tailored recommendations for improvement, as well as an extensive library of free Revenue Growth Analytics resources, including Tableau dashboards and price elasticity models in R and Python. Don't miss out on this opportunity to elevate your Revenue Analytics capabilities and drive significant business impact.

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The Importance of Knowing Your Price Elasticities

Why is it essential to understand the business impact of Price changes and Promotional investments, and what are the analytical approaches to estimating Price elasticities?

With continued data proliferation and the democratization of popular Machine Learning methods among the data analyst community, knowing your Price and Promotional elasticities at the Customer-Product level is fundamental to Finance, Sales, and Revenue management.

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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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