Revenue Optimization Methods

If I could only optimize 4 things to increase sales, here's exactly where I'd start. Most brands optimize their homepage first. That's completely backwards. Instead, start "close to the money" and work backwards from purchase. Here's the priority order that actually moves revenue, quickly: 1. Post-purchase upsells (biggest bang for buck) Do you offer post-purchase upsells or cross-sells? If not, you're leaving like ~10% revenue on the table. Why this works: → Customer already has payment info entered → They're in "buying mode" after successful purchase → Impulse resistance is lowest right after buying → Implementation takes literally minutes What to offer: → Complementary products at a discount → More of what they just bought → "Complete the experience" add-ons → Extended warranties or care products Near-instant AOV increase with minimal effort. 2. Checkout optimization (where 70% drop off) If you are on Plus, are you using Shopify's checkout extensions? Must-have checkout blocks: → Cross-sells and upsells during checkout flow → Social proof (ratings/reviews/testimonials) → Trust signals (security badges, guarantee reminders) → Shipping incentives clearly displayed You've done the hard work getting them here. Don't let a poor checkout experience kill the sale. 3. Smart cart experience Ditch the dedicated /cart page. Use a slide-out/JSON cart instead. Why slide-out carts convert better: → No page load interruption → Maintain shopping momentum → Perfect space for additional offers → Keeps them on product pages longer Smart cart essentials: → Incentive progress bar ("Spend $25 more for free shipping") → In-cart upsells and cross-sells → Trust signals and guarantees repeated → Easy quantity adjustments We’ve seen these carts lead to a 20-40% improvement in cart-to-checkout conversion. 4. Cart abandonment recovery Even with perfect optimization, 30% will still abandon. Capture them. Recovery tactics: → Exit-intent popups → Abandoned cart email/SMS/direct mail sequences Most brands think: "Let's get more traffic to the homepage first." Smart operators think: "Let's maximize revenue from people already buying." Why this approach works: → Quickest implementation and results → Highest ROI optimizations first → Builds momentum and confidence → Generates revenue to fund further optimization The crazy part? We haven't even touched: → Product pages → Homepage → Collection pages → Navigation

Alongside world-class teams I've built 4 revenue engines from the ground-up now, and rebuilt a dozen. After 15-years of building reliable, efficient, and consistent revenue engines, these are the master keys. 🗝 Establish a Unified Revenue Operations Framework 🗝 Data-Driven Decision Making 🗝 Scalable Technology Stack 🗝 Continuous Improvement Culture 🗝 Customer-Centric Focus Everything starts with planning. Once your plan is established you need to design your data model and think through what the architecture needs to be in order to deliver on plan, drive reporting, etc... That takes you from the People and Process-levels into the Platform machinery where technology lives. You use all of that to build and maintain a continuous cadence of improvement... and then benefit from that ever-improving GTM efficiency to ensure the client experience is first rate. Here's a 12-step process to building out the revenue engine. p.s. it assumes "your house is in order" aka you know your ICP, have buyer personas down, understand the pain points and how your solution addresses them, etc... 1) Alignment Break down silos between sales, marketing, and customer success teams. Ensure everyone is working towards the same goals with shared metrics and definitions. 2) Process Optimization Map out your entire customer journey and identify bottlenecks or inefficiencies. Standardize processes and implement technology to automate repetitive tasks. 3) Centralized Data Invest in a CRM and other tools that collect and centralize data from across all customer touchpoints. Most orgs now have CDP systems and are using marketing automation tooling to maximize engagement surface area. 4) Robust Reporting Create dashboards and reports that give you real-time visibility into key performance indicators (KPIs) like pipeline velocity, conversion rates, customer acquisition cost (CAC), and customer lifetime value (CLTV). 5) Predictive Analytics Utilize advanced analytics to forecast revenue, identify trends, and make data-backed decisions to optimize your strategies. 6) Integrated Tooling Choose tools that seamlessly integrate with each other to avoid manual data entry and streamline workflows. 7) Automation Implement automation wherever possible to reduce errors, free up resources, and accelerate processes like lead nurturing, quote generation, and contract management. 8) Regular Reviews Conduct frequent reviews of your processes, data, and technology to identify areas for improvement. 9) Experimentation Test new strategies, technology and tactics to find what works best for your organization. 10) Learning Encourage a culture of learning and development for your team to stay ahead of industry trends and best practices. 11) Voice of the Customer Gather and analyze feedback from customers to understand their needs and pain points. 12) Personalization Tailor your marketing, sales, and customer service interactions to individual customer preferences and behaviors.

Stop guessing your growth path. Map it instead with the Lean Canvas model. Last year a client was losing cash after a bad investment. Their Board wanted a clear plan, but management's ideas were scattered. Pressure rose as their cash runway shrank. I used a blank Lean Canvas and met with management. Box by box, we turned fuzzy thoughts into clear statements. In a few hours, the team could see the whole business on one page. A week later, decisions sped up, waste was cut, and revenue began increasing. The Board praised the new focus because just one sheet had replaced weeks of endless slides. 1. Start with the Problem box because pain fuels purchase: ⇀ List the top three headaches your market hates. ⇀ Ask customers for blunt complaints. ⇀ Rank pains by urgency and frequency.  ⇀ If the pain is weak, the plan is weak. 2. Name the Customer Segments who wake up with that pain: ⇀ Avoid lumping everyone together - be precise. ⇀ Describe one real person, not a demographic blur. ⇀ Note where they already search for help. ⇀ Specific faces drive focused solutions. 3. Your Unique Value Proposition attracts attention: ⇀ Write it like a headline your customer would repeat. ⇀ Highlight the biggest outcome, not features. ⇀ Short, clear value wins the click. ⇀ Keep it under ten words. 4. Now sketch your Solution: ⇀ Draft three bare-bones features solving each top pain. ⇀ Mockup screens or sketches quickly. ⇀ Show them to five prospects tomorrow. ⇀ Speed beats perfection in early design. 5. Channels tell you how messages travel to wallets: ⇀ Pick the two cheapest tests before buying ads. ⇀ Leverage existing communities and email lists. ⇀ Measure response time and cost per lead. ⇀ Cheap learning outruns expensive guessing. 6. Revenue Streams prove the idea can feed itself: ⇀ State exactly who pays, how much, and how often. ⇀ Compare price to the pain’s current cost. ⇀ Pilot a single pricing tier first. ⇀ Real cash beats hypothetical guesses. 7. Analyse Cost Structure for sustainability: ⇀ List the three largest costs and make them variable. ⇀ Negotiate monthly, not annual, contracts. ⇀ Lean costs preserve runway for learning. ⇀ Automate before hiring. 8. Key Metrics keep founders honest on progress: ⇀ Choose one north-star metric and two support numbers. ⇀ Link each metric to habit or revenue. ⇀ Track weekly in one simple dashboard. ⇀ What gets graphed gets fixed faster. 9. Finally, name your Unfair Advantage: ⇀ This is the asset rivals can’t match. ⇀ Lean on unique data, patents, or proven community. ⇀ Document founder expertise that speed cannot buy. ⇀ Without moats, margins leak. 10. Don't forget to summarise your high-level concept and identify early adopters too. Review our lean canvas model weekly to stay on track with your strategy. What's your favourite strategic model? ------- ♻️ Repost to help others in your network. Follow Jonathan Maharaj FCPA for more insights on accounting, finance and leadership.

I've helped dozens of acquirers, including Adyen, Checkout.com, and others optimize their authorization rates. 99% of them fall into 3 big traps... Mistakes that keep them bleeding revenue through unnecessary declines. Here's how to quickly fix them (so you can start maximizing approval rates today): Before data scientists got involved in payments, optimization wasn't really a thing. Engineers just found the fastest way to get their work done, often creating these systemic issues that persist today. So here are a few traps to avoid and how to fix them: TRAP 1: Generic Response Code Abuse Most teams send 80%+ of declines as "05: Do Not Honor" or "51: Not Sufficient Funds." This renders your data useless. You can't identify trends, optimize strategies, or help merchants understand why transactions fail. Strategic Fix: Treat response codes as your optimization roadmap. The more granular your codes, the more likely you are to find patterns that can drive intelligent retry logic and merchant coaching. TRAP 2: Blanket Decline Strategies Teams block entire countries, merchant categories, or transaction types "just to be safe." This kills legitimate transactions and frustrates customers who then switch to competitors. Strategic Fix: Risk is contextual, not categorical. Build dynamic risk models that consider transaction velocity, device fingerprinting, and behavioral patterns rather than static rules. TRAP 3: Static Authorization Hold Periods Most acquirers hold authorizations for 7+ days, blocking customer spending power unnecessarily. Strategic Fix: Authorization holds are working capital management. Analyze settlement timing by merchant segment to optimize cash flow without increasing risk. Other ways to increase authorization rates and revenue include: Account Updater: Automatically updates expired card details with merchants, preventing recurring payment failures Stand-In Processing: When issuers are offline, optimized STIP parameters can approve low-risk transactions instead of blanket declines Real-time alerts: Building alerts to notify when BINs are underperforming, so you can take appropriate actions such as Dynamic 3DS or Payment Flagging. The result? Acquirers who focus on implementing these fixes see 15-25% fewer unnecessary declines within as little as 60 days. Authorization optimization isn't just about approving more transactions; it's about intelligently managing risk while maximizing revenue per transaction attempt. P.S. During this summer, I have turned my newsletter into a Payments 4.0 Summer School, every week I will go deep, explaining the current trends and opportunities, providing the best frameworks and strategies. Subscribe here https://lnkd.in/etQJ2Tb5 to get it.

📲 I spent over 5 years in SMS marketing for eCommerce. After analyzing/working on dozens of programs... Here's how I'd approach it from the ground up if I were starting from zero👇 Phase 1: Build the shell 🐢 Set up core behavioral automation & list growth strategies to increase revenue. If you don't get these right, you'll never scale it. - Two-tap opt-in onsite + at checkout to capture subscribers - Welcome Series + set up day 1 two-way messaging expectations w/ subscribers - Abandoned Cart - Browse Abandonment - Post-Purchase ( Educational onboarding and reorder reminders) - Transactional Updates (97% of consumers want these & they are VERY low hanging fruit to drive engagement + revenue. I've seen brands drive $50k/mo from these messages) Phase 2: Add more fuel 🚀 Start leveling up your strategy - Start sending SMS campaigns as your retargeting list grows. Campaigns are great... but remember unsubscribe rates go up a lot and segmentation is your best friend🙂 - Begin behavioral segmentation inside of your automations to personalize messaging/increase relevancy. - Integrate your Customer Service platform to start handling replies (fast if you can)... These are often buying questions that can lead to more sales and happy customers - Test more list growth strategies like adding SMS opt-ins to your order tracking pages, interactive email, package inserts, and test paid ads/influencer funnels that go to SMS funnels Phase 3: Optimization & Conversational 🧪💬 Focus on 2% improvements... - A/B test EVERY automated flow with at least 2 variations per message. Test copy, timing, mms vs sms, personalization, etc... - A/B test your sign-up units. Whether it's behavioral or the look/feel of the sign-up unit... don't settle for one. Let the data tell you what is the top converting path. - A/B test your campaigns. Figure out the best send times for your respective segments, what voice works best for copy, mms vs sms, top converting offers, etc... Don't just test revenue here (think unsubscribe rates, click-through rates, etc...) - Implement 2-way conversational automation across key flows. Look for key moments to engage in more conversations with your customers to build relationships, collect more zero-party data, and deliver personalized content. My favorites are the welcome series and post-purchase... but I've seen cool examples even in places like the abandoned cart to handle objections (we built a whole business off of this at Tone). Bonus: If your vendor does human-powered texting like Concierge or Sales, this might be the time to consider it (only after everything else is in place!). 👉 Great SMS programs are not built overnight. The best programs are built in phases... with constant evaluation, testing, and optimization. Those 2% improvements over time compound and set you up for long-term success and growth. Hmm maybe we should name a podcast about that? 🤔 #smsmarketing #ecommerce

After 10+ years of analyzing marketing performance data, I've noticed a (very!) common optimization pitfall. Teams focus solely on Cost Per Acquisition (CPA) while missing the bigger revenue (ROAS) picture. 𝐖𝐡𝐲 𝐂𝐏𝐀 𝐈𝐬𝐧'𝐭 𝐄𝐧𝐨𝐮𝐠𝐡 👉 Different user segments show varying behaviors post-conversion (retention rates, seats per account, cancellation patterns, upselling potential) 👉 Low CPA campaigns might actually generate less revenue than higher CPA initiatives with better ARPU 👉 Subscription products have multiple revenue-generating actions beyond initial conversion 𝐓𝐡𝐞 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐰𝐢𝐭𝐡 𝐓𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐑𝐎𝐀𝐒 𝐓𝐫𝐚𝐜𝐤𝐢𝐧𝐠 👉 Multiple revenue events (renewals, plan changes, seat additions) can't be cleanly attributed to original campaigns 👉 Attribution windows often misassign later revenue events to organic or CRM campaigns 👉 Conversion events alone don't capture the full revenue story 𝐓𝐡𝐞 4-𝐒𝐭𝐞𝐩 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐀𝐜𝐜𝐮𝐫𝐚𝐭𝐞 𝐑𝐎𝐀𝐒 𝐌𝐞𝐚𝐬𝐮𝐫𝐞𝐦𝐞𝐧𝐭 1️⃣ 𝘚𝘵𝘰𝘳𝘦 𝘈𝘥 𝘗𝘭𝘢𝘵𝘧𝘰𝘳𝘮 𝘚𝘱𝘦𝘯𝘥 - Implement ETL tools (Fivetran, Funnel etc) to store spend data - Create unified view across platforms with daily campaign-level granularity 2️⃣ 𝘊𝘢𝘭𝘤𝘶𝘭𝘢𝘵𝘦 𝘙𝘦𝘷𝘦𝘯𝘶𝘦 𝘗𝘦𝘳 𝘜𝘴𝘦𝘳 - Aggregate all revenue events (subscriptions, renewals, upgrades) - Create comprehensive user lifetime value view - Store in same warehouse as ad spend data 3️⃣ 𝘛𝘳𝘢𝘤𝘬 𝘐𝘯𝘪𝘵𝘪𝘢𝘭 𝘊𝘰𝘯𝘷𝘦𝘳𝘴𝘪𝘰𝘯 - Ensure conversion events link to single touchpoint - Maintain consistent unique identifiers (user_id, campaign_id) - Connect conversion data to revenue tracking 4️⃣ 𝘑𝘰𝘪𝘯 𝘋𝘢𝘵𝘢 𝘚𝘦𝘵𝘴 𝘧𝘰𝘳 𝘈𝘯𝘢𝘭𝘺𝘴𝘪𝘴 - Combine spend, revenue, and conversion data - Create segmented views by market, strategy, audience, keyword - Enable granular ROAS calculation per campaign With this as basis, you can calculate granular ROAS and payback period for your individual campaigns, ads or keywords. Full detailed guide with implementation steps in comments.

This one metric separates thriving businesses from failures. Most entrepreneurs overlook it until it's too late. It’s not hard to create a great product or service. The real challenge is producing it for less than people are willing to pay. This is where businesses thrive or die. At Quest Nutrition, our mission was clear: make a protein bar with the flavor of a candy bar but the protein profile of a protein powder. It was crazy expensive at first. (We joked about having the most costly protein bars on planet Earth.) We knew to scale, we had to drive costs down. Here’s how we did it: Model It Out. Build a detailed business model. Know your costs at different volumes. Break down your costs for ingredients and employees, and align them with your revenue. Scale Smartly. Initial costs will be high. As you grow, buy ingredients in larger quantities to reduce costs. Validate Your Assumptions. If your product needs to be priced higher than what customers are willing to pay, you don’t have a business. Run thought experiments to test this before sinking years and money into it. Stay Objective. Don’t fall in love with your idea. Base your decisions on data. The worst time to realize you can’t be profitable is after launch. Now let’s apply this to hiring. Model It Out: Calculate the cost of hiring help at different levels of your business. Break down the costs of each hire, including salaries, benefits, and overheads. Align these costs with the revenue they are expected to generate. For each volume of business, determine how many employees you can afford and what their impact on revenue will be. Scale Smartly: Hire in phases. Initially, take on more roles yourself or hire part-time help. As your business grows and revenue increases, you can hire more full-time employees. Focus on efficiency before increasing headcount. Validate Your Assumptions: Ensure that hiring additional help will directly contribute to increased revenue or significantly reduce costs. If it doesn’t, rethink your strategy. Run the numbers and see if you can maintain your profit margins with the new hires. Stay Objective: Don’t hire based on gut feeling or desperation. Use data to make hiring decisions. Track the performance and ROI of each new hire. If they aren’t contributing to profitability, reassess their role or your hiring strategy. Key takeaways: → Model your costs meticulously and align them with expected revenue.  → Scale your hiring and production smartly, focusing on efficiency. → Always validate your assumptions with data and thought experiments. → Stay objective and use data to guide your hiring and business decisions.

One of my most rewarding projects involved a client who was struggling to increase sales despite having a strong product offering. After a thorough analysis of their pricing strategy, I identified a few key areas for improvement. Firstly, the client was offering too many discounts and promotions, which diluted the perceived value of their products. To address this, I recommended reducing the frequency of discounts and focusing on creating a more premium perception. Secondly, the pricing structure was overly complex, making it difficult for customers to compare products and make informed decisions. We simplified the pricing tiers and introduced a clear value proposition for each option. Finally, we adjusted the pricing based on market research and customer feedback. By understanding the competitive landscape and the customers' willingness to pay, we were able to optimize the prices to maximize revenue. These changes resulted in a significant increase in sales, with a 40% boost in revenue within the first quarter. The client was thrilled with the results and has continued to see positive growth. How have you been able to optimize your pricing strategy to increase sales? What factors do you consider when making pricing decisions?

Dynamic pricing is an effective tactic to increase conversion and revenue for subscription products. When I first tested dynamic pricing while leading subscriptions at TechCrunch, we were able to increase conversion rate by 22% while also increasing 1-year estimated LTV. Here's how we did it: 1️⃣ Identify what impacts conversion We investigated which variables were most strongly associated with conversion, and we found 10 variables (see the first image). We then used a machine-learning algorithm to score all users from 0-100 based on the criteria. 2️⃣ Create marketing segments We used the scores to create marketing segments based on the likelihood to subscribe score. We could have created 100 segments, but that’s overly complex for a first test so we simplified it into three groups to reduce scope (low, medium, and high). We referred to the score as the LTS score, or “likelihood to subscribe” score.  3️⃣ Develop hypothesis and run an experiment Our hypothesis was that segmenting with price differentiation would lead to a higher conversion rate and higher LTV than a static experience. We ran an experiment where users with a medium and high likelihood to subscribe score received a higher trial price point ($5 first month), and users with a low likelihood to subscribe score received a lower trial price point ($1 first month). See the second image for the test plan. 4️⃣ Analyze the data We looked at conversion volume, conversion rate, and gross revenue, and then modeled the estimated LTV for 1 year. Revenue and LTV numbers are intentionally removed from the image for LinkedIn sharing. Shown in image 3, the results were:  *Using dynamic pricing led to a 22% lift in conversion and higher revenue than a static paywall experience.  *Conversion rate for the medium and high score segment was 2.5x higher than the average of all other segments. The test was initially a success. It also created ideas for follow up tests and analysis. Some of the smartest subscription businesses take a similar approach. For example, The New York Times uses a machine learning algorithm to create a "dynamic meter." Every user gets a slightly different experience with the meter in order to optimize and balance engagement and revenue. Are you taking advantage of dynamic pricing to optimize revenue for your product?

Price elasticity is more than just an economic principle—it’s the foundation of any robust Pricing & Revenue Growth Management strategy. Understanding how consumers and customers respond to price changes is crucial for optimizing profits while balancing market share with EBITDA goals. Traditional pricing methods, such as cost-plus or competitor-based pricing, often fall short. They miss the intricate relationship between price and demand, leading to missed opportunities and diminished profitability. With the rise of AI and ML, price elasticity modeling has become a powerful tool for making more informed, insights-driven pricing decisions at scale. Modern techniques go beyond basic linear models, leveraging vast amounts of internal and external data to provide a nuanced understanding of customer behavior. This allows companies to dynamically adjust prices, tailor strategies for different customer segments, and respond swiftly to market changes. Price elasticity provides the strategic insight needed to optimize pricing, maximize revenue, and protect margins in a competitive landscape by quantifying how demand fluctuates with price adjustments. AI/ML-powered models set new standards for pricing strategies by integrating real-time data and predictive/prescriptive analytics, enabling businesses to fine-tune their pricing approaches in ways traditional methods never could. To integrate price elasticity modeling into your pricing strategy, consider the following steps: 1. Data Collection: Gather high-quality, relevant data, including historical sales figures, inventory data, customer demographics, product reviews, competitive pricing, and other miscellaneous things like weather data. 2. Advanced Analysis with AI/ML: Utilize AI and machine learning to build robust price elasticity models. Approaches like the Double Machine Learning method uncover intricate relationships between pricing and demand that traditional models miss. 3. Customer Segmentation and Strategy Alignment: Different segments of your market will respond uniquely to price changes. By segmenting your customers based on their price sensitivities, you can tailor your pricing strategies to each group, maximizing revenue and profits. 4. Continuous Optimization: Implement small, controlled price changes and monitor their impact using A/B testing and analysis. Use real-time data to refine your pricing strategy continually, ensuring it evolves with market conditions and customer preferences. From our experience guiding mid-market companies through the transition from traditional to modern pricing models, the shift to AI/ML-driven elasticity modeling often results in meaningful gains in accuracy and pricing precision. To learn more, see the helpful links in the comments section. These include free resources that offer Price Elasticity modeling examples in R/Python using linear, ElasticNet, Random Forest, and Double Machine Learning methods.