Promotional Campaign Analysis

We Boosted an 8-Figure Beauty Brand's Email Revenue by 68.5%. Here’s a complete breakdown of how we did it: 1. Background • Client: An 8-figure beauty brand • Industry: Beauty and Personal Care • Challenge: The brand needed a comprehensive overhaul of their automated flows and customer journey. Their marketing efforts were not fully optimized, and they lacked a cohesive strategy that included email, SMS, and push notifications. 2. What We Did • Funnel Analysis and Optimization: Strategy: Analyzed the customer journey and identified major drop-off points. Implementation: Developed unique abandonment flows to address potential customer objections at each step. • Plugging Messaging Gaps: Strategy: Addressed major gaps in the existing flows. Implementation: Implemented new abandoned flows based on custom on-site behavior metrics. • Campaign Strategy: Strategy: Focused on staying top of mind for customers. Implementation: Implemented a campaign strategy aimed at maintaining a consistent cadence of strong content to maintain brand presence, crucial in this client’s industry sector. 3. Results • Overall Revenue Increase: Email Revenue: 68.5% increase from the previous year. • Revenue Breakdown: Campaigns Revenue: A 32% increase. Flows Revenue: 197% increase. • Channel-Specific Increases: Email Revenue: Increased by 63%. SMS Revenue: Increased by 115%. Push Revenue: Increased by 244%. 4. How We Did It • Analyzed the Customer Journey: We conducted a thorough analysis of the customer journey to identify major drop-off points and opportunities for engagement. • Plugged Major Messaging Gaps: Addressed gaps in the existing messaging flows and implemented new abandonment flows based on custom on-site behavior metrics. • Implemented a Comprehensive Campaign Strategy: Our strategy focused on keeping the brand top of mind for customers, which is particularly important in the beauty industry. This included a mix of email, SMS, and push notifications to ensure consistent engagement. By implementing these strategic changes, our client saw a significant increase in revenue across all channels. The improved approach made their marketing efforts more effective, driving higher engagement and conversions. If you’re looking to optimize your email campaigns and drive more conversions, reach out to us at Dispatch. We’re here to help you achieve your marketing goals with tailored, high-impact email strategies.

Claude code analyzed 4,055 cold email script variants across 1,160 campaigns that we launched in 2025 at RevGrowth. Here’s what it found: [Claude Code output] Here are 7 common traits shared across the top-performing campaigns: 1. Question-first openers that reference the prospect's world. Every top campaign opens with a short question about the prospect's current situation — not about the sender's product. "Have any flooring projects coming up?" "Projects ramping up?" "Gearing up for new builds?" It immediately signals relevance and gets the prospect thinking about their own needs before you pitch anything. 2. A tangible value asset as the centerpiece. The best campaigns don't sell — they offer something the prospect can say yes to without commitment. A playbook built for a similar company. A lunch & learn covering specs and ROI math. A free breakdown of cost savings. The email exists to deliver or offer that asset, not to book a meeting. 3. Soft permission-based CTAs. The closing ask is always low-friction and framed as the prospect granting permission: "Mind if I share it?" "Interested in a breakdown?" "Want me to send it over?" "Open to a lunch & learn?" These outperformed direct meeting requests, "would it be crazy" framings, and "are you the right person" questions by a wide margin. 4. Specific, quantified value props. Top campaigns include hard numbers: "cuts costs by 80% per square foot," "$4/sq ft savings," "saves weeks versus mitigation." They don't say "more cost-efficient" — they say exactly how much. The prospect can immediately calculate whether it's worth their time. 5. Hyper-personalization tokens beyond just {FIRST_NAME}. The highest-performing campaigns used {PROJECT}, {LOCATION}, and {COMPANY} to reference the prospect's actual work. One campaign referencing specific construction projects and locations hit 14.5% reply rates — while the generic version of the same script from the same workspace averaged 1-3%. 6. Recognizable brand name-drops in case studies. When a top campaign referenced a case study, it named a specific well-known company (e.g., "a playbook we created for Hasbro"). This gives the asset instant credibility and makes the prospect curious. Generic "we helped companies like yours" never appeared in the top performers. 7. Conversational, slightly casual tone. Top campaigns read like a short message from a real person — not a marketing email. They use dashes instead of colons, contractions, and sentence fragments. "Thought to share" not "I wanted to take a moment to introduce." The warmth is subtle but consistent across every winner. As you can see, we practice what we preach — and it’s reflected in our campaign data.

When I analyze a campaign, the first thing I look for isn’t the execution. I care about the craft, but I care more about the emotional tension the brand is leaning into. Every strong campaign starts with a feeling. Once you understand that feeling, the whole strategy becomes clear. Here's the process I use... Identify the tension: → What pressure is the brand responding to → What emotion is sitting underneath the idea → Why this moment and why now Find the story: → What is the core message → Who is the message speaking to → What part of their world is being reframed Study the craft: → How the spot opens → The pacing and the beats → The role of sound and silence → What the brand wants you to feel at the end Understand the brand move: → Are they defending something → Are they expanding the audience → Are they shifting positioning → Are they building or repairing trust Watch the ripple: → How people react → What gets shared → What creates conversation → Whether the campaign earns attention Once you learn to see these pieces, campaign breakdowns get simple. You stop reacting to the execution and start understanding the intent.

Trade Promotions in FMCG: Are We Investing or Just Spending? After years of leading sales teams and driving growth in the FMCG food & beverage industry, I’ve seen one truth play out repeatedly: Trade promotions can make or break your bottom line. With trade spend often consuming 20–30% of revenue, the key question is: Are your promotions delivering ROI — or just draining margin? Here’s what I’ve learned from real-world execution across markets: 🔹 1. Every promotion must have a purpose Don’t run a promo just because it’s “that time of year.” Define clear objectives — volume lift, market share, or channel penetration — and align the mechanics accordingly. 🔹 2. Measure incremental sales, not just sell-in Selling to the distributor is only step one. True ROI comes when product moves off the shelf — and stays in the consumer’s basket. 🔹 3. Avoid promotion fatigue When discounts become the norm, they stop driving behavior. I always encourage a mix of value-adding mechanics (e.g., bundle offers, cross-category tie-ups) instead of just cutting price. 🔹 4. Localize your strategy One-size-fits-all doesn’t work. Trade dynamics vary by region. I’ve seen the same promo flop in one region and fly in another. Regional customization and geo-targeted execution boost both relevance and ROI. 🔹 5. Post-promo analysis is non-negotiable (Review, Learn, Adjust) Every campaign should close with a ROI analysis — what worked, what didn’t, and what we’d do differently. That’s how we build smarter calendars, not just busier ones. 🎯 Final Thoughts Promotions aren’t just tactics, they are a powerful lever — but only if we treat them as investments with expected returns, not just line items on a calendar. And like any smart investment, they deserve planning, tracking, and accountability. This mindset has helped me drive stronger topline growth while protecting margin — the sweet spot in FMCG.

Promotions help move inventory in the short run. But what happens to shoppers when they are repeatedly exposed to promotions?   Most promotion are assessed through their short-term lift on sales. The Alibaba experiment showed that even a 24-hour promotion can slightly increase price sensitivity afterwards.   However, what happens when consumers are exposed to deals again and again?   A classic FMCG analyzed over 8-years of household-panel data in the US to provide some answers.*   In the long run, consumers become:   📉 More price sensitive Even a small increase in promotions makes shoppers judge regular prices more harshly. Non-loyal shoppers (the vast majority) become especially price-driven. Price sensitivity increases by 2.5% and 9% for every 1% rise in price promotions or display promotions.   🔄 Less loyal Price promotions also grow the non-loyal segment. Fewer shoppers stick with a preferred brand and switching becomes the norm.   💸 More deal-seeking Promotion sensitivity rises over time and promotions become the expected moment to buy, not the exception.   And what about advertising?   📣 Advertising still reduces price sensitivity Advertising lowers long-run price sensitivity by about 0.3% for non-loyal shoppers. But its stabilizing effect is harder to achieve in a category where repeated discounts have pushed many shoppers into a deal-driven mindset.   ⚠️The takeaway: Promotions are not neutral tools. They leave long-run behavioral footprints that erode resilience, weaken pricing power, and shift the category toward more deal-driven competition.   Short-term lift is easy to see. Long-term erosion is not. But it matters far more for profitable growth.   👉 A more detailed post on this topic (part 2 of FMCG promotions) now on marketingscience.today * Source: Mela et al 1997

𝗠𝗼𝘀𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗷𝘂𝗺𝗽 𝘀𝘁𝗿𝗮𝗶𝗴𝗵𝘁 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 — 𝗯𝘂𝘁 𝗳𝗼𝗿𝗴𝗲𝘁 𝗼𝗻𝗲 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝘀𝘁𝗲𝗽: 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴. When you're doing any kind of analysis, the 𝗿𝗲𝗮𝗹 𝗴𝗮𝗺𝗲 isn’t just about SQL queries, dashboards, or charts. It’s about: → How you define the problem → How you model the business context → How you connect numbers to decisions Here’s what I’ve learned from experience 👇 Before starting any analysis, I ask: → What are we trying to improve? → What are the main drivers? → What KPIs define success? → What’s in our control, and what’s not? This helps you build a 𝗺𝗲𝗻𝘁𝗮𝗹 𝗺𝗼𝗱𝗲𝗹 of how the business works — and 𝗹𝗲𝗮𝗱 the analysis instead of just crunching numbers. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: We’re analyzing why promo campaigns aren’t driving sales. Most people will just compare revenue before and after the promo. But if you think in terms of business modeling, you’ll ask: → Was the promo timed with payday behavior? → Did it cannibalize full-price sales? → Were discounts deep enough for price-sensitive users? → Did it even reach the right customer segment? That’s where the real value of analysis lies — in thinking like a business owner, not just a report generator. I’ve seen analysts with average tech skills become key decision-makers — because they knew how to structure their thinking. So the next time you're doing an analysis, pause and ask: "What business levers am I actually analyzing?" That’s how you go from reporting → to strategy. #BusinessModeling #DataAnalytics #KPIThinking #DecisionMaking #AnalyticsForBusiness #StructuredThinking #SQL #PromoAnalysis #DataLeadership #OpsDrivenAnalytics

𝗬𝗼𝘂𝗿 𝗹𝗮𝘀𝘁 𝗽𝗿𝗼𝗺𝗼𝘁𝗶𝗼𝗻 𝗱𝗶𝗱𝗻'𝘁 𝗳𝗮𝗶𝗹 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗱𝗶𝘀𝗰𝗼𝘂𝗻𝘁. It failed because of seven variables you didn’t control. Here’s what I heard in most promo planning meetings: “Let’s do 30% off.” “Competitor did it, so we should too.” “We’ve always done a Black Friday promo.” No hypothesis. No readiness checklist. No real-time tracking. Just discount + hope. Then when it underperforms,  they blame  the market,  timing, or  customer. But here’s the truth: You’re not running promotions. You’re running experiments. And like any experiment — if you don’t control the variables, you learn nothing. Most retail promos fail before launch: → 80% of failures happen pre-launch → Inventory not allocated right → Signage arrives late → Staff can’t explain the offer → Marketing & Operations not aligned And the biggest miss? No post-mortem. Teams jump to the next campaign  without analyzing what actually worked. To fix this, master the 𝗥𝗲𝘁𝗮𝗶𝗹 𝗣𝗿𝗼𝗺𝗼𝘁𝗶𝗼𝗻 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸: 1️⃣ Define Your Promotional Hypothesis 2️⃣ Question the Promotional Calendar 3️⃣ Pre-Promo Readiness Checklist 4️⃣ Align to Customer Cash Flow 5️⃣ Promo Pricing Architecture 6️⃣ Hero SKU Strategy 7️⃣ Track Real-Time Performance 8️⃣ Cross-Functional Alignment 9️⃣ Post-Promotion Analysis 🔟 ROI Beyond Revenue 𝗧𝗵𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁: Top retailers don’t run more promotions. They run smarter experiments with: → Clear hypotheses (what are we testing?) → Controlled variables (timing, pricing, hero SKUs) → Execution discipline (80% fail pre-launch) → Real-time adjustments (daily huddles save campaigns) → ROI beyond revenue (margin, loyalty, data, brand) Promotions aren’t revenue events. They’re multi-million dollar experiments. Run them like one. 💬 Which framework would have saved your last promotion? -- 📌 Save the playbook for your next promotion. ♻️ Repost to help a retail leader who needs it. ➕ Follow me for retail frameworks that work in the real world.

For over a decade, I've worked alongside mid-market CPG brands ($50MM - $1B revenue), and the story is often the same: smart people, great products, but struggling to maintain profitable growth in the face of relentless pressure. Trade promotions that don't deliver and subsidize baseline sales, competitor price wars, and the constant battle for margin across the value chain. It's exhausting, and frankly, it's often unnecessary. This isn't about "tough market conditions." It's about having the right system for Pricing and Revenue Growth Management Analytics and processes. It's about moving from reactive firefighting to a proactive, insights-driven strategy built on a foundation of integrated/harmonized data and some essential predictive analytics/scenario analyses (no fancy AI). 𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹𝗶𝘁𝘆 𝗜 𝘀𝗲𝗲 𝗺𝗼𝘀𝘁 𝗼𝗳𝘁𝗲𝗻: • 𝗣𝗿𝗼𝗺𝗼 𝗥𝗢𝗜? 𝗔 𝗕𝗹𝗮𝗰𝗸 𝗕𝗼𝘅. Many brands are flying blind, repeating promotions without knowing if they generate incremental profit. Retail buyers are often in the dark as well. We're talking about potentially wasting 10-20% of gross revenue on ineffective trade promotions. • 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗼𝗿-𝗗𝗿𝗶𝘃𝗲𝗻 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 𝗣𝗮𝗻𝗶𝗰. Reacting to every competitor's move leads to a race to the bottom. You need the proper Pricing RGM intelligence and scenario planning, not knee-jerk reactions. • 𝗧𝗵𝗲 𝗣𝗿𝗼𝗳𝗶𝘁 𝗣𝗼𝗼𝗹 𝗠𝘆𝘀𝘁𝗲𝗿𝘆. Who's benefiting from your promotions? Are you subsidizing your distributors or retailers? The lack of transparency here is a significant margin leak. It doesn't have to be this way. Here's how to take back control: 1. 𝗧𝘂𝗿𝗻 𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝗹 𝗮𝗻𝗱 𝗲𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗗𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗔𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 𝗮𝗻𝗱 𝗽𝗿𝗼𝗺𝗼 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀. Stop guessing. Implement a driver-based revenue and margin analysis to isolate the true impact of price, volume, mix, and competitive actions. Promo ROI capabilities enable you to reallocate spend to profitable promotions and strategically adjust pricing or product mix. 2. 𝗣𝗿𝗲𝗱𝗶𝗰𝘁, 𝗗𝗼𝗻'𝘁 𝗥𝗲𝗮𝗰𝘁. Near real-time price intelligence and scenario modeling are weapons against price wars. Model pricing impacts and make proactive decisions to protect your brand and bottom line. 3. 𝗠𝗮𝗽 𝘁𝗵𝗲 𝗣𝗿𝗼𝗳𝗶𝘁 𝗣𝗼𝗼𝗹 𝗟𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲. It reveals exactly where value is being captured—by you, your distributors, or the retailers. It also helps with renegotiating trade terms. 4. 𝗣𝗿𝗶𝗰𝗲 𝗳𝗼𝗿 𝗩𝗮𝗹𝘂𝗲, 𝗡𝗼𝘁 𝗝𝘂𝘀𝘁 𝗩𝗼𝗹𝘂𝗺𝗲. Price-value mapping aligns your pricing with customer perception and willingness to pay. It's about reinforcing brand equity while maintaining profitability. Stop leaving your pricing to chance. I've created a 𝗖𝗣𝗚 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 & 𝗥𝗚𝗠 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗛𝘂𝗯 specifically for mid-market CPG brands. It's packed with practical guides, tools, and frameworks you can use immediately to address the above pain points. The link to access is in the comments.

📊 How do you design a retailer-specific promotion optimization study? Promotions are one of the biggest investments brands make — yet too often, they’re designed on averages. The truth? Walmart shoppers don’t behave like Target shoppers. Costco and Amazon each play by their own rules. That’s why retailer-specific research is the only way to truly optimize promotions. Here’s how to approach it 👇 🔹 1. Capture Shopper & Retailer Context Every study starts with segmentation: – Where do you usually buy [category/product]? – How often do you shop this retailer? – What role does price play vs. loyalty or convenience? This lets you tag responses by retailer loyalty group from the start. 🔹 2. Build Retailer-Realistic Scenarios Design promotions in the format shoppers actually see in store or online: – Walmart → 2-for-$12 multipack – Target → Buy 2, Get 3rd 50% off – Costco → $3 instant rebate on club pack – Amazon → 20% off with Subscribe & Save Realistic execution ensures shopper responses reflect real behavior. 🔹 3. Layer in Elasticity & Mechanics Testing – Promotion Elasticity: Would you purchase at 10% off? 20% off? 30% off? – Mechanics Trade-Offs (via conjoint): Discount vs. multi-buy vs. loyalty points. – Stock-Up Multipliers: How many units would you buy? – Switching Dynamics: Would you move from a competitor or private label? 🔹 4. Analyze Retailer by Retailer The outputs are clear, segmented insights: – Best mechanic for Walmart vs. Target vs. Costco vs. Amazon – Optimal discount depth per retailer – Incremental lift vs. cannibalization risk – Shopper switching patterns unique to each channel 🔹 5. Add AI + Machine Learning This is where the study comes alive: – AI simulates virtual shelves in each retailer’s style – ML builds retailer-specific promotion elasticity curves – Predictive models run “what if” scenarios (e.g., What if Walmart runs 20% off while Target runs BOGO?) ✅ The result: A retailer-ready sell-in story for your commercial teams — showing exactly how promotions perform, by retailer, with clear ROI guidance. 💡 Takeaway: Promotions aren’t one-size-fits-all. The winners customize by retailer, by mechanic, and by shopper segment. And with AI, brands of all sizes can now access this level of insight. 👉 Have you ever run a promotion study by retailer? What differences surprised you most? #RGM #Promotions #Retail #Pricing #FMCG #CPG #AI #ShopperInsights

How we reduced MMM's In-sample MAPE by 4.6 percentage points through RBF technique. Recently we replaced an existing MMM vendor for a prominent client. One of the complaints the client had with the earlier vendor was that - they were not able to capture key events and promotional events accurately. The client had clear data on which weeks they ran the promotional events and which weeks they conducted the key events. However despite the data, the earlier vendor could not capture it comprehensively. We dug into their code and found that they had used the archaic 'dummy encoding' to capture the events. 📌 The problem with Dummy Variable (one hot) Encoding A simple dummy variable assumes that the entire promotional event impact happens on one isolated day or week. But anyone who has looked at real market behavior knows that this is not true. The influence starts building on the day / week of launch, peaks after some days and decays over the next several days. When we compress this entire behavioral arc into a single spike (like in Timeline 1), we end up with: ❌ Mis attributed channel contributions ❌ High in-sample MAPE ❌ Wrong budget recommendations This is one of the hidden reasons why MMM sometimes “feels off” during key promotional events. If you see a lot of daylight between your predicted and ground truth chart, that is not a healthy sign for your model. 📌 How Aryma Labs tackled this through Radial Basis Function (RBF) Encoding We didn't believe that the effect of the promotion or events lasts only at the exact time of launch. We believed that its effect slightly lingers. So how do we capture this? Applying RBF can be thought of as placing a 'water cup' or 'standard Normal Distribution' at the point of campaign or promotion launch. Instead of a crude one-day or one week spike, RBF encoding allows us to model any event as a smooth curve with a rise, a peak and a decline. 📌 The Result: After applying the In-sample MAPE reduced from 10.6% to 6.0%. A remarkable 4.6 percentage point reduction or approximately 43 % reduction !! Overall, RBF application leads to: ✅ Correct separation between true media impact vs seasonal lift ✅ More trustworthy contribution splits ✅ Better model fit and improvement in In-sample MAPE ✅ Better downstream budget decisions If you are still using Dummy variable encoding, you are underfitting reality. ▪️Any promotion or event is not a date. It is a distribution of activity. Model it like one and your MMM will immediately start telling a more truthful story. If MMM is not feasible you can use techniques like ITSA or Information theory based Incrementality tests. With the latter, you can also model Creative Fatigue !! Check out the links for Aryma Delta and Adstock ITSA in comments.