Data Analysis for Food Brand Development

💪 David v Goliath.... ... How to compete using a Smart Data Strategy... The biggest brands in the category can often easily outspend competitors when it comes to investment in data & insight, and this can give them a clear competitive edge. Smaller businesses are unlikely to be able to match their spend, but they can spend *smarter* to compete more effectively. Here’s how: 🚀  1. Start with High-Impact Data ↳ Market Overview Reports: Affordable sources like Mintel or Euromonitor provide a snapshot of market size, trends & competitor positioning. This helps identify category trends & establish the right areas or Shoppers to target without the ongoing cost of continuous data feeds. ↳ Focus on Key Business Questions: Pinpoint where insight will make the biggest impact e.g. - Detailed understanding of Retailer category performance ahead of a range review to help secure new distribution. - Identifying target consumers & optimal outreach strategies to boost penetration. 🔍  2. Leverage Selective EPOS & Loyalty Data ↳ Market-Level EPOS Data: This can be invaluable for insight into category dynamics & benchmarking KPIs vs competitors whilst avoiding high costs of retailer-specific feeds. ↳ Loyalty Card Data: Although this will only cover one retailer (so no total market read) it can give you very granular insights on sales performance as well as WHO is buying your brand. 🎯 3. Focus on Actionable Insights ↳ Prioritize Impactful Data: Concentrate on insights that can directly drive product development, pricing & promotions. Avoid ‘nice-to-have’ data that doesn’t materially impact your business. ↳ Make the most of the data you need DO have: Manage scope to only buy the data you *need* & make sure each source is *fully* mined. Investing time in analysis instead of buying new data can yield deeper understanding & more opportunities to optimise your brand performance. 📈 4. Scale Data Investments with Business Growth ↳ Mix One-Off & Continuous Feeds: Start with one-off data sources, then add targeted continuous data feeds as you scale. Regularly review usage & actionability & stop reports which don't add value. 🧠 5. Outsmart, Don’t Outspend --> Be Agile ↳ Develop a *Learning* culture : Smaller businesses can move around the Build/Measure/Learn loop much faster than bigger brands - Insight is the rocket fuel you need to power this. Key Takeaway: Strategic Data Use Although small & medium sized businesses will inevitably have less data, if they use what they can afford to answer the right questions & act quickly to execute then they can find a competitive edge of their own. What are your thoughts & experiences - let us know in the comments. Want to find out more? This week's #CategoryWins newsletter digs into this subject in much more detail : See link in comments or my bio ♻️ & if you enjoyed this post, please like & share it with your network. #CategoryManagement #FMCG #CPG #DataStrategy #CompeteSmarter

Over the last few months, I analysed 𝟯𝟭 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝗿 𝗙𝗠𝗖𝗚 𝗯𝗿𝗮𝗻𝗱𝘀 across food, beverages, personal care and dairy- with a simple question: 𝘄𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗱𝗿𝗶𝘃𝗲𝘀 𝘀𝗰𝗮𝗹𝗲 𝗶𝗻 𝗜𝗻𝗱𝗶𝗮𝗻 𝗙𝗠𝗖𝗚 𝘁𝗼𝗱𝗮𝘆? One insight stood out: 𝗗𝟮𝗖 𝗱𝗶𝗱𝗻’𝘁 𝗳𝗮𝗶𝗹. 𝗪𝗲𝗮𝗸 𝘂𝗻𝗶𝘁 𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝗰𝘀 𝗱𝗶𝗱. Between 2019–2024, CAC rose ~𝟮.𝟯𝘅, CPMs inflated 𝟮𝟬–𝟮𝟱% 𝗮𝗻𝗻𝘂𝗮𝗹𝗹𝘆, payback stretched from 𝟰𝟱–𝟲𝟬 𝗱𝗮𝘆𝘀 𝘁𝗼 𝟲–𝟵 𝗺𝗼𝗻𝘁𝗵𝘀, and 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗺𝗮𝗿𝗴𝗶𝗻𝘀 𝗳𝗲𝗹𝗹 𝗯𝗲𝗹𝗼𝘄 𝟮𝟬% for brands dependent on discount-led trial. That’s when almost every brand moved from 𝗗𝟮𝗖 → 𝗺𝗮𝗿𝗸𝗲𝘁𝗽𝗹𝗮𝗰𝗲𝘀 → 𝗾-𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲 → 𝗴𝗲𝗻𝗲𝗿𝗮𝗹 𝘁𝗿𝗮𝗱𝗲 → 𝗺𝗼𝗱𝗲𝗿𝗻 𝘁𝗿𝗮𝗱𝗲- not by strategy, but by 𝗣&𝗟 𝗰𝗼𝗺𝗽𝘂𝗹𝘀𝗶𝗼𝗻. What the data showed  1. 𝗖𝗮𝘁𝗲𝗴𝗼𝗿𝘆 𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝗰𝘀 𝗱𝗲𝗳𝗶𝗻𝗲 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀. Beauty runs at 65–72% gross margin, snacks at 45–52%, beverages ~40% and dairy 25–32%. Two brands can hit INR 500Cr. revenue with completely different EBITDA- because margin is built into category physics, not marketing.  2.𝗖𝗵𝗮𝗻𝗻𝗲𝗹 𝗺𝗶𝘅 𝘀𝗵𝗮𝗽𝗲𝘀 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗰𝗮𝘀𝗵. D2C gives margin but burns CAC, marketplaces add scale but leak margin, Q-Com drives trial but dilutes contribution. GT/MT scale but strain working capital. 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗶𝘀 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴, 𝗻𝗼𝘁 𝗚𝗧𝗠.  3. The strongest brands in the dataset were not the loudest- they were the ones built on 𝘃𝗲𝗹𝗼𝗰𝗶𝘁𝘆 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲. Distribution doesn’t win; 𝘀𝗲𝗹𝗹-𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗱𝗼𝗲𝘀. If 𝗦𝗞𝗨 𝗿𝗲𝗼𝗿𝗱𝗲𝗿 > 𝟮𝟭 𝗱𝗮𝘆𝘀, cash is stuck.  4. Portfolio design compounds growth. Hero SKUs drive trial, 𝗵𝗮𝗯𝗶𝘁 𝗦𝗞𝗨𝘀 𝗱𝗿𝗶𝘃𝗲 𝘀𝗰𝗮𝗹𝗲. A 200ml beverage pack compounds faster than a premium 1L SKU because 𝗳𝗿𝗲𝗾𝘂𝗲𝗻𝗰𝘆 𝘀𝗰𝗮𝗹𝗲𝘀 𝗰𝗮𝘀𝗵 𝗳𝗹𝗼𝘄. 𝗙𝗿𝗲𝗾𝘂𝗲𝗻𝗰𝘆 > 𝗰𝗮𝘁𝗮𝗹𝗼𝗴 𝘄𝗶𝗱𝘁𝗵.  5. 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗶𝘀 𝗮𝗻 𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝗰 𝘀𝗶𝗴𝗻𝗮𝗹. M3 repeat < 35% = weak PMF, M6 repeat < 45% = broken unit economics, true repeat > 70% (ex-coupons) = defensible brand.  6. 𝗖𝗮𝘀𝗵 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗶𝘀 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆. If 𝗖𝗖𝗖 > 𝟰𝟱 𝗱𝗮𝘆𝘀, GT expansion becomes working capital debt. The next winners will scale customer-funded, not capital-funded. 𝙏𝙇;𝘿𝙍: The best outcomes in FMCG will come from brands with margin quality, velocity, frequency and cash discipline- not just distribution and paid acquisition. 𝗙𝗿𝗼𝗺 𝗮 𝗴𝗿𝗼𝘄𝘁𝗵 𝗶𝗻𝘃𝗲𝘀𝘁𝗼𝗿 𝗹𝗲𝗻𝘀, valuation now follows economic quality: CM>30%, M6 repeat>45%, payback<90 days, WC turns >8x and EBITDA visibility in 18–24 months. 𝟮𝟬𝟮𝟲 𝘄𝗶𝗹𝗹 𝗻𝗼𝘁 𝗿𝗲𝘄𝗮𝗿𝗱 𝗵𝘆𝗽𝗲. 𝗜𝘁 𝘄𝗶𝗹𝗹 𝗿𝗲𝘄𝗮𝗿𝗱 𝗼𝗽𝗲𝗿𝗮𝘁𝗼𝗿𝘀. #FMCG #D2C #ConsumerBrands #GrowthStrategy #UnitEconomics #CPG #BrandBuilding #PrivateEquity #VentureCapital #ConsumerEconomics #BusinessModel #DistributionStrategy #StartupInsights #Scaling

𝗧𝗼𝗼 𝗺𝗮𝗻𝘆 𝗺𝗮𝗿𝗸𝗲𝘁𝗲𝗿𝘀 𝗮𝗿𝗲 𝗻𝗼𝘁 𝘁𝗮𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝘁𝗶𝗺𝗲 𝘁𝗼 𝗮𝗻𝗮𝗹𝘆𝘇𝗲 𝘁𝗵𝗲𝗶𝗿 𝗱𝗮𝘁𝗮. 𝗧𝗵𝗲𝗿𝗲 𝗶𝘀 𝗻𝗼 𝘃𝗮𝗹𝘂𝗲 𝗶𝗻 𝗵𝗮𝘃𝗶𝗻𝗴 𝗮𝗰𝗰𝗲𝘀𝘀 𝘁𝗼 𝗱𝗮𝘁𝗮 𝗶𝗳 𝘆𝗼𝘂 𝗮𝗿𝗲 𝗻𝗼𝘁 𝘂𝘀𝗶𝗻𝗴 𝗶𝘁. 𝗧𝗵𝗲 𝗯𝗲𝘀𝘁 𝗯𝗿𝗮𝗻𝗱 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗰𝗮𝗻 𝘁𝗲𝗹𝗹 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝘀𝘁𝗼𝗿𝗶𝗲𝘀 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀. Conduct a deep-dive business review of your brand at least once a year. Otherwise, you are negligent of the brand, where you are investing all your resources. Dig into the five specific sections—marketplace, consumers, channels, competitors, and the brand—to establish your brand’s key issues, which you answer in the brand plan. Analyze your consumer target to understand better the consumer’s underlying beliefs, buying habits, growth trends, and critical insights. Use the brand funnel and leaky bucket analyses to uncover how they shop and make purchase decisions. Try to understand what they think when they buy or reject your brand at every stage of the consumer’s purchase journey. Uncover consumer perceptions through tracking data, the voice of the consumer, and market research. 𝗧𝗼 𝗸𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝘆𝗼𝘂𝗿 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗳 𝘁𝗵𝗲 𝗰𝗼𝗻𝘀𝘂𝗺𝗲𝗿𝘀, 𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝟭𝟬 𝗽𝗿𝗼𝗯𝗶𝗻𝗴 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀: 1. Who are your possible target consumer segments? Are they growing? How do you measure them? 2. Who are the consumers most motivated by what you have to offer? 3. Who is your current target? How have you determined demographics, behavioral or psychographic, geographic, and usage occasion? Generational trends? 4. How is your brand performing against KEY segments? Share, sales, panel or funnel data, tracking scores? What about by channel or geography? 5. What drives consumer choice? What are the primary need states? How do these consumer needs line up to your brand assets? Where can you win with consumers? 6. Map out the path to purchase and use brand funnels to assess your brand’s performance in moving through each stage. Are consumers changing at stages? Are you failing at stages? 7. What are the emerging consumer trends? How does your brand match up to potentially exploit them? Where would your competitors win? 8. What are the consumer’s ideal brand experiences and unmet needs we can address? 9. What are the consumer’s emotional and functional need states? How does the brand perform against them? How are you doing in tracking studies to meet these benefits? 10. What is the consumer’s perceptions of your brand and your competitors? Voice of the consumer.

𝐓𝐡𝐞 𝐩𝐫𝐨𝐣𝐞𝐜𝐭 𝐭𝐡𝐚𝐭 𝐠𝐨𝐭 𝐦𝐞 𝐦𝐲 𝐟𝐢𝐫𝐬𝐭 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐚𝐧𝐝 𝐰𝐡𝐚𝐭 𝐈 𝐝𝐢𝐝 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐥𝐲. When I started my data journey, I made the same mistake most beginners make I kept learning new tools, but never applied them properly. I finished courses in Excel, SQL, and Power BI. But when someone asked, “Show me something you’ve built,” I had nothing solid to share. That’s when I decided to build one simple project but do it right. 🧠 𝐓𝐡𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐈 𝐂𝐡𝐨𝐬𝐞 A food delivery startup was facing delays and customer churn. I asked myself: What if I could identify the real reason behind it? ⚙️ What I Did • Collected mock order data (delivery time, ratings, region, customer type). • Used Excel for data cleaning & exploration. • Applied SQL to find trends — regions with max delays, peak times, top complaints. • Created an interactive Power BI dashboard showing average delivery time, churn %, and regional performance. 📊 𝐓𝐡𝐞 𝐎𝐮𝐭𝐜𝐨𝐦𝐞 When I shared it on LinkedIn with a short write-up “How I used SQL & Power BI to analyze delivery delays for a food startup,” it got noticed by a hiring manager. 𝐓𝐡𝐚𝐭 𝐩𝐫𝐨𝐣𝐞𝐜𝐭 𝐚𝐥𝐨𝐧𝐞 𝐠𝐨𝐭 𝐦𝐞 𝟑 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐜𝐚𝐥𝐥𝐬 𝐰𝐢𝐭𝐡𝐢𝐧 𝟐 𝐰𝐞𝐞𝐤𝐬. Not because it was “perfect,” but because it showed real-world thinking. 💡 Here’s what I learned: ✅ Don’t build fancy dashboards solve real business problems. ✅ Recruiters love projects that connect data → business impact. ✅ One well-documented project beats 10 half-finished ones. 📘 I’ve shared a list of free datasets + 10 real project ideas you can start with today inside my Telegram channel → 👉 https://lnkd.in/dfDUnDMW 👍 Like, 🔁 Repost & 🔔 Follow Heena Kouser for job-ready insights, interview prep & free learning tools. #DataAnalytics #DataScience #PowerBI #SQL #CareerGrowth #DataProjects #FresherJobs #BusinessAnalytics #LearningJourney #Upskilling

I recently built this interactive Power BI dashboard to analyze restaurant sales trends, customer demographics, and delivery performance. 💡 The Goal: To transform raw transaction data into a strategic roadmap, helping stakeholders identify operational bottlenecks and pinpoint exactly where revenue is coming from. 🔍 Key Insights: • Operational Alert: The analysis flagged a critical logistics issue—32.8% of orders are Cancelled and 32.8% are Delayed. Highlighting this allows management to fix the supply chain immediately. • Demographic Myth-Busting: Contrary to the belief that fast food is just for youth, the data revealed an equal customer split between Teenagers, Adults, and Seniors, suggesting a need for inclusive marketing campaigns. • Menu Optimization: Identified 'Pasta' and 'Sandwiches' as the highest revenue drivers ($3M+ each), actually outperforming Burgers and Pizza in total sales volume. 🛠️ Tools Used: Microsoft Power BI (Power Query for ETL, DAX for calculated measures) #DataAnalytics #PowerBI #DashboardDesign #DataVisualization #BusinessIntelligence #PortfolioProject #RestaurantAnalytics 

Market Knowledge & Market Insight in FMCG Fast Moving Consumer Goods (FMCG) markets are highly competitive, dynamic and sensitive to changes in consumer behavior. Brands sucess not only by knowing what is happening in the market but also by understanding why it is happening and how to act on it. Both Market Knowledge (facts and data) and Market Insight (deep understanding & actionable intelligence) are critical for success described as follows:- 📊 1. Market Knowledge: It refers to the collection of facts and information about the market environment. In FMCG, it includes knowing market size, growth rate, trends, competitors, consumer demographics and retail channels. Key Components of Market Knowledge: - Market Size & Growth: Total market value, volume and category growth. - Trends & Seasonality: What is trending (Eg. Healthy, sustainable packaging). - Competitor Landscape: Pricing, promotions, distribution channels. - Customer Segmentation: Demographics, geographies and purchasing patterns. 💡 2. Market Insight: It deals with interpreting the information to uncover hidden patterns, motivations and opportunities. It shows why consumers behave a certain way and how the brand should respond. Key Elements of Market Insight: - Understanding consumer motivations (health, convenience, affordability). - Detecting behavior shifts (Eg. Move from in-store to online purchases). - Identifying unmet needs (gaps in flavors, sizes or price points). - Predicting future trends before competitors. 🚀 3. Practical Applications: Turning knowledge & insight into action: - Product Development: Use insights to identify unmet consumer needs and trends*then launch new SKUs, flavors, sizes or product variants that fill market gaps. - Branding & Communication: Craft messages and campaigns that emotionally connect with consumers, highlight benefits they care about, and differentiate the brand from competitors. - Route to Market & Distribution: Adjust distribution strategies and sales channels to match consumer buying habits, whether in-store, online or through modern trade, ensuring maximum availability. - Pricing Strategy: Optimize pricing based on consumer perceived value, affordability and competitor benchmarks rather than only production cost, driving both profitability and market share. - Promotions & Campaign Planning: Design promotional offers and campaigns aligned with consumer behavior, seasonality and purchase motivations to maximize impact and ROI. - Customer Experience: Enhance the end to end consumer experience, from product discovery to purchase and post purchase engagement, strengthening loyalty and repeat purchases. In summary, Market Knowledge provides the foundation, while Market Insight gives the competitive edge.Brands that transform data into understanding & action achieve shelf space, customer loyalty & sustainable growth.

When it comes to innovation, the key isn’t just the idea, it’s in the data. Take Kraft Heinz with their Crystal Light Vodka Refreshers. The company saw that nearly 20% of their existing Crystal Light consumers were already using it as a mixer in alcoholic drinks. That’s a consumer signal they could act on. With this insight, Kraft launched a product that not only aligns with customer behavior but taps into an established demand. Then there’s PepsiCo with Cheetos Mac n’ Cheese. PepsiCo Mexico identified a growing demand for household cooking staples and a lack of innovation in the mac and cheese category. Once again, this wasn’t a shot in the dark; it was informed by consumer research. The result? 35M+ pesos in sales in the first two years. Understanding consumer preferences gave Pepsi the edge to succeed. Consumer insights data helps you understand your audience to make confident, data-backed decisions to launch innovations that expand your brand’s reach and drive volume growth. Consumer insights data helps you understand your audience to make confident, data-backed decisions to launch innovations that expand your brand’s reach and drive volume growth. How are you using consumer insights for your next innovation? #RetailMedia #ConsumerInsights #MarketingStrategy #ExperientialMarketing #Innovation #CPG