Revology Analytics

There's still time to join our webinar next Wednesday. Whether you're a Pricing/RGM/Analytics professional in the Auto Service / Tire Retail or an adjacent industry, you'll find this webinar valuable. All registrants receive our 25-page whitepaper on how to drive profitable growth in the Automotive Retail industry.

How can your business implement Dynamic Pricing capabilities in just 𝟵𝟬 𝗱𝗮𝘆𝘀? Most mid-market retailers and distributors are still reliant on manual processes to implement, review, & update prices on their products. It's not unusual to see $0.5-2B companies on the upper end of mid-cap enterprises still use a patchwork of ad-hoc Excel reports and analyses to govern their pricing implementation. Dynamic, automated pricing solutions can offer a revolutionary approach to these businesses, enabling them to react swiftly to market changes & competitive pressures. Implementing a foundational version of dynamic pricing can be done efficiently within 90 days, significantly boosting profitability & operational efficiency. It can most often be built and implemented using popular tech stacks available to all companies (think Microsoft Azure or Google Cloud infrastructure). Implementing dynamic pricing that automates price setting based on real-time transactional & market data can be broken down into an actionable plan: 1. Invest in robust competitor price data scraping capabilities. 2. Engage key functional teams to understand each product category’s goals. Am I leveraging Product Group XYZ to drive market share? Am I maximizing Gross Profit $? Is it a loss leader intended to drive foot traffic to my stores? 3. Build CPI Elasticity models for products or product clusters to understand price sensitivity. Competitive Price Index (CPI) measures your price position relative to competitors. For example, a CPI of 105 means you are 5% more expensive than competitors. CPI elasticity measures changes in demand relative to changes in your competitive price position. 4. Group products into CPI Elasticity Groups to create the foundation for your optimal CPI index. 5. Based on sales revenue, velocity, CPI elasticity, product reviews/sentiments, and other metrics like cart abandonment rates and affinity analyses (basket attach rates), categorize products into critical categories such as anchor items, value-perception items, assortment-perception items, and background items. 6. Develop a Dynamic Price Optimization Matrix (DPOM) with baseline CPIs & adjustment factors like category goals, seasonality, pricing sentiment, and competitive density. 7. Collaborate with stakeholders to build an automated price execution process with manual reviews for critical items and complete automation for others. 8. Develop and pilot a Minimum Viable Analytics Solution (MVAS), incorporating stakeholder feedback and conducting A/B tests in select markets. From my experience, the key to successful, first-stage dynamic pricing lies in simplicity and stakeholder alignment / heavy collaboration in the development process. Ensure your models and methods are straightforward and easily understood (and accepted) by key internal stakeholders. Once deployed, we recommend updating your CPI elasticity models and other critical analyses (i.e., product clustering, etc.) 1x a quarter.

Sales & Marketing teams that leverage AI will continue to thrive. While #generativeAI is still in its hype phase, AI-enhanced tools have definitely benefitted the commercial domain. A good portion of Sales & Marketing decisions are data-driven and are ripe for automation. Large enterprises and smaller startups fully take advantage of it, but there's a huge opportunity in the mid-market space to reduce manual efforts and leverage AI/ML to "do the dirty work" at scale. Check out our latest infographic that summarizes the opportunity: #revenue_growth_analytics

While there is considerable hype surrounding 𝗔𝗜 for most business functions and industries, it is a powerful reality transforming commercial teams' operations. Businesses can streamline processes, enhance productivity, and significantly boost performance by leveraging 𝗔𝗜 𝘁𝗼𝗼𝗹𝘀. AI tools are designed to optimize various aspects of the sales and marketing process, from lead generation and customer engagement to forecasting and performance tracking. These tools can analyze customer interactions, predict future behaviors, and personalize marketing efforts, ensuring that the right message reaches the right audience at the right time. * 𝗦𝗮𝗹𝗲𝘀 𝗔𝗜 𝗧𝗼𝗼𝗹𝘀 help with lead scoring, sales forecasting, and various aspects of customer relationship management (CRM) platform inputs. These tools can predict future behaviors by analyzing customer interactions, transactional history, or lookalike modeling, allowing sales teams to focus on high-potential leads. * 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗔𝗜 𝗧𝗼𝗼𝗹𝘀: These tools assist in campaign optimization, customer segmentation, and customer journey analysis & optimization. They use data to personalize marketing efforts, ensuring targeted messaging and practical engagement. * 𝗔𝗜 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: AI applications can automate repetitive tasks such as data entry, scheduling, and email outreach, freeing up time for more strategic activities. Implementing AI tools effectively requires a strategic approach. Here's a step-by-step guide: 1. 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗣𝗮𝗶𝗻 𝗣𝗼𝗶𝗻𝘁𝘀: Determine where your team struggles the most, whether it's lead generation, customer retention, or administrative tasks. This will guide your choice of tools. 2. 𝗦𝗲𝘁 𝗖𝗹𝗲𝗮𝗿 𝗚𝗼𝗮𝗹𝘀: Define what you want to achieve with AI, such as increasing sales by 20% or reducing manual tasks by 50%. 3. 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: Choose tools that integrate seamlessly with your existing systems to avoid unnecessary complications. 4. 𝗦𝘁𝗮𝗿𝘁 𝗦𝗺𝗮𝗹𝗹: Begin with one or two tools and expand gradually. This allows your team to learn and adapt without being overwhelmed. 5. 𝗜𝗻𝘃𝗼𝗹𝘃𝗲 𝘁𝗵𝗲 𝗧𝗲𝗮𝗺: Get input from the end-users of these tools. Their buy-in is crucial for successful implementation. From my experience, successful AI integration hinges on starting small and scaling up as your team becomes more comfortable with the technology. It's also vital to involve your team early to ensure they understand the benefits and feel confident using the new tools. If you are building a bespoke solution, involve your team throughout the development process, from concept alignment to testing/deployment. For instance, one of our clients saw a 30% increase in sales efficiency using predictive lead scoring/lookalike modeling effort (collaboratively built), allowing their sales team to focus on the most promising leads. hashtag #𝗿𝗲𝘃𝗲𝗻𝘂𝗲_𝗴𝗿𝗼𝘄𝘁𝗵_𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀

Most mid-market businesses 𝐬𝐭𝐫𝐮𝐠𝐠𝐥𝐞 𝐭𝐨 𝐭𝐮𝐫𝐧 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐢𝐧𝐭𝐨 𝐫𝐞𝐚𝐥 𝐩𝐫𝐨𝐟𝐢𝐭𝐬. This challenge stems from a narrow view of Revenue Growth Management as only tactical pricing, which overlooks broader aspects of sales and marketing enablement and overall productivity. A holistic revenue growth strategy starts with understanding that sales and marketing efficiency are crucial to your profitable growth. For B2B companies, it's more important than optimal price setting alone. Instead of fixating solely on pricing, consider how comprehensive revenue growth analytics can enhance productivity and strategic decision-making across various dimensions: • 𝐒𝐚𝐥𝐞𝐬 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲: Identify which sales strategies yield the highest return, optimize sales territories, and refine lead-scoring models to focus on high-potential prospects. • 𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐄𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞𝐧𝐞𝐬𝐬: Use data to tailor marketing campaigns, measure campaign ROI, and refine customer segmentation for more personalized engagement. • 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬: Gain deeper insights into customer behaviors and preferences to improve retention strategies and increase customer lifetime value. To move beyond just pricing tactics, consider integrating a broader spectrum of metrics: • 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐀𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧 𝐂𝐨𝐬𝐭 (𝐂𝐀𝐂): Total cost to acquire a new customer. • 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐋𝐢𝐟𝐞𝐭𝐢𝐦𝐞 𝐕𝐚𝐥𝐮𝐞 (𝐂𝐋𝐕): Total revenue from a customer over their relationship with your business. • 𝐂𝐡𝐮𝐫𝐧 𝐑𝐚𝐭𝐞: Percentage of customers who stop doing business with you over a given period. • 𝐍𝐞𝐭 𝐑𝐞𝐯𝐞𝐧𝐮𝐞 𝐑𝐞𝐭𝐞𝐧𝐭𝐢𝐨𝐧 (𝐍𝐑𝐑): Percentage of recurring revenue retained, including expansions and contractions. • 𝐀𝐯𝐞𝐫𝐚𝐠𝐞 𝐑𝐞𝐯𝐞𝐧𝐮𝐞 𝐏𝐞𝐫 𝐔𝐬𝐞𝐫 (𝐀𝐑𝐏𝐔): Revenue generated per user (or account). • 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧 𝐑𝐚𝐭𝐞: Percentage of potential customers who take a desired action. • 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐀𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧 𝐏𝐚𝐲𝐛𝐚𝐜𝐤 𝐏𝐞𝐫𝐢𝐨𝐝: Time taken to earn back the cost of acquiring a new customer. • 𝐑𝐞𝐯𝐞𝐧𝐮𝐞 & 𝐏𝐫𝐨𝐟𝐢𝐭 𝐆𝐫𝐨𝐰𝐭𝐡 𝐑𝐚𝐭𝐞: The rate at which revenue or profit increases. • 𝐍𝐞𝐭 𝐏𝐫𝐨𝐦𝐨𝐭𝐞𝐫 𝐒𝐜𝐨𝐫𝐞 (𝐍𝐏𝐒): Measure of customer loyalty and satisfaction. • 𝐆𝐫𝐨𝐬𝐬 𝐌𝐚𝐫𝐠𝐢𝐧 𝐑𝐞𝐭𝐮𝐫𝐧 𝐨𝐧 𝐈𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭 (𝐆𝐌𝐑𝐎𝐈): Profitability of inventory investment. 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 To integrate comprehensive revenue growth analytics into your operations, consider these steps: • 𝐁𝐮𝐢𝐥𝐝 𝐚 𝐑𝐨𝐛𝐮𝐬𝐭 𝐂𝐥𝐨𝐮𝐝 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞: Use platforms like AWS, GCP, or Microsoft Azure for scalable data storage and real-time processing. • 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬: Implement automation tools to reduce manual tasks and focus on strategic activities. Find out more in the comment below. hashtag #𝐫𝐞𝐯𝐞𝐧𝐮𝐞_𝐠𝐫𝐨𝐰𝐭𝐡_𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬

Revenue Growth Management (RGM) is often mistakenly equated solely with pricing strategies. However, true RGM encompasses a broader scope, leveraging comprehensive commercial analytics to drive business growth across multiple dimensions. Beyond pricing optimization, RGM integrates advanced metrics and analytics efforts such as Customer Acquisition Cost, Lifetime Value, Churn Rate, Marketing ROI, and Net Revenue Retention to provide a holistic view of revenue and profit opportunities. By analyzing these diverse data points, companies can unlock insights that lead to improved customer segmentation, targeted marketing campaigns, optimized product and customer mix, and enhanced operational efficiencies. Companies that expand their RGM focus beyond pricing are better positioned to identify untapped revenue streams, boost profitability, and achieve sustainable growth in an increasingly competitive and insights-driven marketplace.

Whether you're a commercial executive or private equity operator, if you are responsible for profitable growth at an Auto Service, Repair & Tire Retailer, join Revology Analytics for a live webinar on July 31. Join us for an insightful session as we explore smart pricing and revenue growth 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 that want to transform how they price & promote their products & services to drive greater Net Sales and Profits. Link in the comments. #revenue_growth_analytics

Check out the latest podcast by The Alexander Group, a leading Revenue Growth Strategy firm, where 𝐊𝐞𝐯𝐚𝐧 𝐒𝐚𝐯𝐚𝐠𝐞 from AG's Marketing practice and 𝐀𝐫𝐦𝐢𝐧 𝐊𝐚𝐤𝐚𝐬, 𝐅𝐨𝐮𝐧𝐝𝐞𝐫 & 𝐌𝐚𝐧𝐚𝐠𝐢𝐧𝐠 𝐏𝐚𝐫𝐭𝐧𝐞𝐫 at Revology Analytics, discuss the transformative power of AI in Marketing. In this episode, we discuss 𝐡𝐨𝐰 𝐀𝐈 𝐭𝐨𝐨𝐥𝐬 𝐥𝐢𝐤𝐞 𝐥𝐚𝐫𝐠𝐞 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐦𝐨𝐝𝐞𝐥𝐬 𝐚𝐧𝐝 𝐦𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐠𝐫𝐚𝐩𝐡𝐬 𝐚𝐫𝐞 𝐫𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐢𝐳𝐢𝐧𝐠 marketing analytics including customer journey analysis, along with surgical 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧, making it more seamless than ever. Learn which AI solutions you should focus on to automate key aspects of your marketing pipeline development. 𝐊𝐞𝐲 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬: * The rise of AI in the sales process * Strategies for implementing AI in your marketing functions * Practical applications and real-world success stories. Discover how to leverage the power of AI to optimize your marketing strategies, improve marketing ROI, and drive profitable revenue growth. This conversation is filled with expert advice and actionable tips. 𝐅𝐨𝐫 𝐦𝐨𝐫𝐞 𝐝𝐞𝐭𝐚𝐢𝐥𝐬, 𝐜𝐡𝐞𝐜𝐤 𝐭𝐡𝐢𝐬 𝐩𝐨𝐬𝐭: https://lnkd.in/ehyBjC9Q

The number of 𝟗-𝐟𝐢𝐠𝐮𝐫𝐞 𝐦𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐞𝐫𝐬 (i.e., the upper end of mid-market) managing their business on random Excel sheets is astonishing. Leveraging 𝐚𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐭𝐨 𝐝𝐫𝐢𝐯𝐞 𝐬𝐚𝐥𝐞𝐬 & 𝐦𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 has been considered table stakes for about a decade, yet here we are in the age of 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈, with large companies routinely relying on gut feelings, outdated methods, and a Frankenstein process of Excel workbooks. Nowadays, manufacturers need to speed up their game by making genuine connections with customers and using data-driven insights to make decisions. They must embrace advanced insights capabilities to keep up with the competition, turning data into actionable strategies that drive real growth. Developing robust insights capabilities can transform your sales and marketing efforts. It's not just about collecting data but about analyzing and applying it to uncover hidden opportunities, streamline operations, and connect with customers more meaningfully. 𝐇𝐞𝐫𝐞'𝐬 𝐡𝐨𝐰 𝐦𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐞𝐫𝐬 𝐜𝐚𝐧 𝐛𝐮𝐢𝐥𝐝 𝐭𝐡𝐞𝐬𝐞 𝐜𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬: Data is the fuel that powers insights. Without good data, you're flying blind. Accurate and consistent data allows manufacturers to make confident decisions. By tracking customer and market behavior, manufacturers can spot trends, adjust production, address profit pool issues, tweak marketing messages, and develop products that better meet customer needs. 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 𝐟𝐨𝐫 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚 𝐒𝐨𝐥𝐢𝐝 𝐃𝐚𝐭𝐚 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐒𝐭𝐚𝐫𝐭 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐛𝐚𝐬𝐢𝐜𝐬: Identify the necessary internal and external data and its sources. 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐞 𝐜𝐥𝐞𝐚𝐧 𝐝𝐚𝐭𝐚: Ensure accuracy and consistency across systems. 𝐈𝐧𝐯𝐞𝐬𝐭 𝐢𝐧 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐭𝐨𝐨𝐥𝐬: Choose Machine Learning and business intelligence platforms like Azure and Microsoft Power BI that fit your needs and budget and align with your existing tech stack. Build domain-specific intelligence tools collaboratively and iteratively with internal stakeholders / end-users. 𝐓𝐫𝐚𝐢𝐧 𝐭𝐡𝐞 𝐭𝐞𝐚𝐦: Ensure everyone understands how to access and interpret insights. 𝐂𝐨𝐧𝐝𝐮𝐜𝐭 𝐝𝐚𝐭𝐚 𝐚𝐮𝐝𝐢𝐭𝐬: Regularly maintain accuracy in data collection and analysis. 𝐂𝐫𝐞𝐚𝐭𝐞 𝐚 𝐝𝐚𝐭𝐚-𝐝𝐫𝐢𝐯𝐞𝐧 𝐜𝐮𝐥𝐭𝐮𝐫𝐞: Encourage decision-makers to support their ideas with data. 𝐌𝐚𝐤𝐞 𝐢𝐭 𝐚𝐜𝐜𝐞𝐬𝐬𝐢𝐛𝐥𝐞: Ensure data is easily accessible and understood throughout the organization. From our experience, the key to successful insights-driven strategies lies in integrating advanced analytics with human expertise. While data provides valuable insights, the expert touch is essential for interpreting and applying these insights effectively. Strong collaboration between analytics and commercial teams ensures adoption and maximizes the impact of data strategies.