How to Use Data to Improve Seasonal Sales Performance

Last week, I spoke with a VP of Merchandising from a large brand. They shared their struggle with increasing average transaction value 💰 Their strategy: Daily sales report analysis to craft promotions and bundles. The goal? Boost transaction value. The result: Smaller purchases, no significant progress 😮 My advice: Dive deeper. Beyond conventional tactics, explore seasonal patterns, basket analysis. Differentiate. Here's the reality 👇 - 2023 has shifted consumer spending habits - Overused discounts are losing impact - Traditional strategies are outdated - Promotions matter, but they're not the complete answer It's time for a new playbook in fashion retail 📕 1. Get Smart with Stock: Look at what sells best in combinations with other articles. Ensure your stores carry these gems in sizes that combine well with other assortment. This way, you are not just selling single items, you are increasing overall transaction value. 2. Match Winners with Demand: Focus on the combos and sizes people love the most. Plan these assortment mixes according to the stores’ demand. Your goal is to have these ready and waiting in the stores that need them. 3. Fill the Gaps Creatively: If some stores are missing combos, get creative. Introduce combinations that not only fill the space but also make you more money where there's a real want for them. We're talking high-margin, demand-driven combinations. Pro Tip When customers can't find what they're looking for within their budget, let's turn that moment into an opportunity. Offer them an exclusive, one-time discount on a higher-priced combo. This strategy not only moves our overstocked, high-margin items but also keeps our profit margin healthy. It's a win-win: customers feel valued with a deal just for them, and we increase the sale's value. Manual methods can't keep pace with today's demands, but the right technology can transform challenges into opportunities. Leverage data, automation, and customer insights for a real change.

Because with a bad forecast everything else will fail... This infographic contains 7 steps to create and improve a forecast: ✅ Step 1 - Start with Historical Data Collection & Cleaning 👉 gather and clean past sales data (ideally 3 years) 👉 remove outliers, fill in gaps, and ensure data accuracy before analysis ✅ Step 2 - Segment Your Demand 👉 break down your demand into segments to create more granular forecasts 👉 examples: volume, value, product categories, customer types, regions ✅ Step 3 - Generate a Baseline Statistical Forecast 👉 as starting point, generate a baseline forecast using statistical methods like time series analysis ✅ Step 4 - Apply Seasonality and Trend Adjustments 👉 use historical seasonal patterns and emerging trends to fine-tune your forecast for upcoming periods ✅ Step 5 - Collaborate & Fine-tune in S&OP Meetings 👉 collaborate with sales, marketing, finance, and operations to align on one consensus forecast ✅ Step 6 - Adjust for Market Intelligence 👉 incorporate insights from sales teams, marketing campaigns, external research, and product launches to adjust your baseline forecast ✅ Step 7 - Incorporate Forecasts into S&OE (Sales & Operations Execution) 👉 drive actionability in the short term based on this aligned forecast, helping the team respond quickly to deviations 💥 Bonus Step: Build a Continuous Feedback Loop 👉 track forecast accuracy by comparing actual sales to forecasted figures, and regularly update your model based on this feedback Any other steps to consider? #supplychain #salesandoperationsplanning #integratedbusinessplanning #procurement

🚀 AI: Beyond Personalization to Boost Revenue 🚀 Whatever your LLM of choice is, when you are asking it for a quick answer to a question, or you’re employing predictive AI tools that make your efforts at work more intelligent. One thing is certain, AI has become an essential tool in our lives.  Heck, even my toothbrush uses AI to recognize my brushing style and guide me on how to improve. AI is all about the convenience of instant information. In our professional lives, the impact of AI can be much more significant. Particularly within industries like retail, where it allows businesses to predict customer demands, streamline operations, and enhance customer fulfillment. One of the most impactful AI use cases I had the opportunity to work on was AI-driven inventory optimization. Predictive analytics, historical data, and even weather data allow businesses to predict demand more accurately, ensuring products are in the right place at the right time. For instance, by factoring in regional weather patterns, sales history, seasonal trends, and considering lead times. We can ensure that seasonal products like winter coats or swimsuits arrive just when they’re needed most. This approach reduced overstock by ~20% and minimized stockouts by ~30%, directly driving revenue and improving product availability for customers. AI-powered applications and platforms today are not just about improving efficiency, they are a strategic lever for driving revenue growth, optimizing operations, and ultimately enhancing customer satisfaction. AI can help you find the right product, and make sure it’s at your store, but it’s still up to you to decide if you’re ready for winter ❄, or still holding onto summer 🌞! #AI #DigitalTransformation #RetailTech #InventoryOptimization #SupplyChainInnovation

The holidays are like the Super Bowl for retailers. And the competition is fierce. Changing customer expectations and shifts in data collection have made it harder than ever for marketers to succeed in a crowded retail space. That’s why I collaborated with Jason Downie, U.S. CEO of Making Science, on these data-driven strategies retailers can use to maximize their earnings this season (and future-proof their marketing). Check out our tips, or catch the link to the full Total Retail article in the comments below.  1️⃣ Put first-party data first With third-party cookies on the decline, prioritizing first-party data is more important than ever. Loyalty programs and mobile apps are great tools for building comprehensive customer profiles (with consent!) and enabling deeper personalization. 2️⃣ Leverage #GenerativeAI Retailers like Walmart are finding tangible ways to leverage generative #AI to improve customer and employee experiences. For example, their “adaptive retail” program harnesses data and large language models to improve their product catalog. This improves customers’ ability to find what they’re looking for. 3️⃣ Predict inventory and pricing needs AI-powered predictive analytics can help forecast demand, align inventory and delivery, and fine-tune pricing strategies in real time. This approach avoids stockouts and rushed pricing changes while improving margins. 4️⃣ Adjust campaigns in real time Holiday campaigns need to adapt quickly. Real-time data helps refine ad spend and messaging on the fly, ensuring campaigns align with customer trends. A centralized #DataCloud paired with AI makes this easier than ever. 5️⃣ Unify cross-channel data Siloed data hurts customer experience. By integrating data across in-store, online, and mobile channels, retailers can deliver a seamless journey and consistent messaging that keeps customers coming back. The future of #retail is data-driven and AI-powered. Those who embrace it now will be better positioned to meet rising expectations and build lasting loyalty—not just this season but all year long.