Demand Planning In Retail

The latest Zonda Homebuyer Demand Index (ZHDI), a real-time indicator of new home market demand, is in for August.   As a reminder, the ZHDI is an extension of our Zonda Market Ranking (ZMR). The ZMR ranks markets based on actual sales, while the ZHDI looks at future demand based on how active new home shoppers are on NewHomeSource.com. August highlights include: - The national ZHDI remained at an average level, but was down 10% year-over-year. - Among the top 50 markets, 52% were underperforming, 24% were average, and 24% were overperforming. Last month, 42% were underperforming, 28% were average, and 30% were overperforming. - More markets slipped into underperformance in August, reflecting heightened economic uncertainty. Our research shows that while a softer job market initially reduces housing demand, the resulting drop in interest rates eventually offsets the slowdown and helps revive sales activity.  - New home demand in much of South Carolina continues to outperform nationally (National Outlook subscribers, look out for our upcoming SC custom research piece). - Similar to last month, the ZHDI suggested that near-term sales in Texas and Florida will remain sluggish. Growing supply has pushed some consumers to shop around, while others have chosen to step to the sidelines. Subscribers to our Homebuyer Outlook report can log in today to view the updated August numbers, which track listings, searches, and migration patterns across the country. Tim Sullivan Keith Hughes Bryan Glasshagel Cameron McIntosh Kyle Cheslock Sarah Bonnarens Sean Fergus Eva Beeth

◾ Our US Equity Sentiment Indicator registers +0.3 this week suggesting broad investor positioning in the US equity market remains neutral. This week marks the first positive reading since February 2025. However, of the indicator’s nine components, only passive fund flows and retail margin debt are a standard deviation or more above their 52-week averages. ◾ While positioning overall appears constrained, isolated pockets of froth have continued to percolate within the equity market in recent weeks. For example, baskets related to quantum computing, cryptocurrency, and drones have all surged by more than 50% in the past month. Many of these companies rank among the US stocks with the highest recent share trading volumes. ◾ ETF and mutual fund flows indicate widespread recent US household participation in the equity market rally. Following four months and $100 billion of outflows this summer, equity funds have enjoyed over $70 billion of inflows during the last few weeks. ◾ We forecast households will be the biggest source of equity demand next year, purchasing a net $520 billion in 2026 (+19% year/year). A macro backdrop of accelerating economic growth, falling unemployment, slowing inflation, and declining cash yields should support continued household demand for equities. ◾ We expect corporates will buy $410 billion of equities in 2026 (+7% year/year). Buybacks among Russell 3000 stocks totaled a record $648 bn in 1H 2025, and the combination of continued earnings growth, rate cuts, and declining policy uncertainty suggests share repurchases should continue to grow in 2026. A resurgence in M&A activity will also boost corporate equity demand, but continued recovery in IPO volumes will provide a partial offset. ◾ Despite debates around US exceptionalism, foreign investors have been the largest source of US equity demand YTD, and we expect a slower pace of buying next year totaling $250 billion (-56% year/year). Foreign investors bought nearly $280 billion in May and June this year, continuing the usual pattern of elevated foreign investor demand after the dollar has weakened, and US equities have underperformed. ◾ We expect mutual funds and pension funds will remain the largest sellers of equities in 2026. We forecast mutual funds will sell $580 billion of equities due to low current cash balances and persistent outflows from active funds. Elevated current funding statuses support our forecast for $200 billion of net equity supply from pensions as they rotate from stocks into fixed income.

It’s difficult to find many industry sectors where demand in mid-2024 is at or around post-COVID high water marks (usually from mid-2021 through Q1 2022). However, one sector that appears to have seen a demand surge is wholesaling of hardware, plumbing, & heating equipment and supplies (NAICS 4237). Two charts below showing this sector has likely seen stronger demand in 2024 than anticipated. Thoughts: •Top chart shows seasonally, and inflation adjusted sales in this wholesale trade sector (data through June). As can be seen, June 2024 saw the highest reading in over 3 years, with demand 12% above 2019 levels. •Bottom chart shows the ratio of inventories to sales. A good rule of thumb is sharp increases in inflation adjusted sales coupled with drops in inventories to sales ratios suggest demand that was stronger than anticipated. We see that pattern here. •What explains these robust June sales? My theory: the extreme heat in the United States. This sector also sells refrigeration equipment of all types (https://lnkd.in/gmtmzx2d), as well as whole home air conditioners (though not room air conditioners, those are sold by appliance wholesalers in NAICS 4236). Implication: I’ve written previously about how 2024’s extremely hot summer weather may be spurring greater demand for electrical generation and distribution equipment (https://lnkd.in/g6evTF_e). These data are consistent with my theory that this year’s extreme temperatures are spurring greater demand in some industries. #supplychain #supplychainmanagement #markets #freight #trucking #logistics 

We talk a lot about content, channels, and campaigns in #demandgen.  However, the most important thing is understanding your customers.  Without that knowledge, you're just guessing. Jumping straight into campaign mode is a big mistake. Even if you already have a file of your Ideal Customer Profile (ICP), it's still worth it to double-check for any new trends to understand your audience in real-time.  👉🏾Who are they?   👉🏾What are their (present) frustrations?   👉🏾What keeps them up at night? Understanding these factors changes everything. There are many ways to gather such insights:  👉🏾Using surveys gives you quantitative data. 👉🏾Interviews provide qualitative depth.  👉🏾Social listening (very important) reveals what people are saying online.  👉🏾Competitor analysis shows the overall landscape. Recently, I was working on a campaign for a new product. My assumptions were way off, especially after reviewing recent customer interviews, which revealed a different path. Together with my team, we updated our messaging and saw a massive increase in engagement. To say the least, market research saved the (our) day! #b2bmarketing #ABM #marketingstrategy

Never judge a business by its front office but by its back-end logistics. Managing sourcing across India, Pakistan, and Bangladesh has taught me that logistics isn't just about moving boxes—it's what makes or breaks a retail operation. Here's why: The global logistics market hit $9.2 trillion in 2023, with Asia-Pacific contributing 42% of this value (McKinsey Global Institute). Yet, companies lose 20-30% of their logistics costs to inefficiencies. (McKinsey & Company) The real cost of weak logistics shows up in: → Inventory Stockouts: 8.3% of retail sales are lost to out-of-stock situations, costing retailers $1 trillion annually (IHL Group)  → Dead Stock: The average retailer ties up 25% of working capital in excess inventory (Gartner)  → Broken Promises: 69% of customers won't shop with a retailer again after a late delivery (Retail TouchPoints)  → Emergency Shipping: Rush shipping can cost 5-10x more than standard rates (Deloitte) In 2024, due to various disruptions in logistics caused by war, instability, and climate change-induced natural disasters, I witnessed firsthand how fragile supply chains can be. Geopolitical turmoil, including events like the Red Sea Crisis and the Ukraine conflict, further exacerbated these disruptions, underscoring the critical need for resilient and adaptable supply chain strategies. Companies with robust logistics weathered the storm, while others faced existential crises. Today's successful businesses need: 📌 Strategic warehouse placement near key markets 📌Real-time inventory tracking across locations 📌Multiple transport routes for critical supplies 📌Robust risk mitigation plans In my experience, managing an annual sourcing volume of $100 million, the difference between profit and loss often comes down to one question: Can you get your product where it needs to be when it needs to be there? What's your biggest logistics challenge? Share your experience below. #SupplyChain #LogisticsManagement

Inflation isn’t just an economic challenge—it’s a test of agility for businesses. As costs rise and purchasing power shifts, companies that rely on gut instinct risk falling behind. The real winners? Those who use data-driven insights to navigate uncertainty. 1️⃣ Understanding Consumer Behavior: What’s Changing? Inflation reshapes spending habits. Some consumers trade down to budget-friendly options, while others delay non-essential purchases. Businesses must analyze: 🔹 Spending patterns: Are customers shifting to smaller pack sizes or private labels? 🔹 Channel preferences: Is there a surge in online shopping due to better deals? 🔹 Regional variations: Inflation doesn’t hit all demographics equally—hyperlocal data matters. 📊 Example: A retail chain used real-time sales data to spot a shift toward economy brands, allowing it to adjust promotions and retain price-sensitive customers. 2️⃣ Pricing Trends: Data-Backed Decision-Making Raising prices isn’t the only response to inflation. Smart pricing strategies, backed by AI and analytics, can help businesses optimize margins without losing customers. 🔹 Dynamic pricing models: Adjust prices based on demand, competitor moves, and seasonality. 🔹 Price elasticity analysis: Determine how much a price hike impacts sales before making a move. 🔹 Personalized discounts: Use customer data to offer targeted promotions that drive loyalty. 📈 Example: An e-commerce platform analyzed customer behavior and found that small, frequent discounts led to better retention than infrequent deep discounts. 3️⃣ Demand Forecasting & Inventory Optimization Stocking the right products at the right time is critical in an inflationary market. Predictive analytics can help businesses: 🔹 Anticipate demand surges—especially in essential goods. 🔹 Optimize supply chains to reduce excess inventory and prevent stockouts. 🔹 Reduce waste in perishable categories like F&B, where price-sensitive demand fluctuates. 📦 Example: A leading FMCG brand leveraged AI-driven demand forecasting to prevent overstocking of premium products while ensuring budget-friendly variants were always available. 💡 The Takeaway Inflation isn’t just about rising costs—it’s about shifting consumer priorities. Companies that embrace data-driven decision-making can optimize pricing, fine-tune inventory, and strengthen customer loyalty. 𝑯𝒐𝒘 𝒊𝒔 𝒚𝒐𝒖𝒓 𝒃𝒖𝒔𝒊𝒏𝒆𝒔𝒔 𝒂𝒅𝒂𝒑𝒕𝒊𝒏𝒈 𝒕𝒐 𝒊𝒏𝒇𝒍𝒂𝒕𝒊𝒐𝒏𝒂𝒓𝒚 𝒑𝒓𝒆𝒔𝒔𝒖𝒓𝒆𝒔? 𝑨𝒓𝒆 𝒚𝒐𝒖 𝒖𝒔𝒊𝒏𝒈 𝒅𝒂𝒕𝒂 𝒕𝒐 𝒓𝒆𝒇𝒊𝒏𝒆 𝒚𝒐𝒖𝒓 𝒔𝒕𝒓𝒂𝒕𝒆𝒈𝒚? 𝑳𝒆𝒕’𝒔 𝒅𝒊𝒔𝒄𝒖𝒔𝒔 𝒊𝒏 𝒕𝒉𝒆 𝒄𝒐𝒎𝒎𝒆𝒏𝒕𝒔! #datadrivendecisionmaking #dataanalytics #inflation #inventoryoptimization #demandforecasting #pricingtrends

Forecasting is a common application of data science, and it's crucial for businesses to manage their inventory, especially those with perishable items effectively. In a recent tech blog, the data science team from Afresh shared an innovative approach to accurately predict demand, incorporating non-traditional factors such as in-store promotions. Promotions are common in grocery stores, helping customers discover and purchase discounted items. However, these promotions can significantly alter customer behavior, making traditional forecasting methods less reliable. Traditional models struggle to incorporate these factors, often leading to higher prediction errors. To address this challenge, Afresh’s data science team developed a deep learning forecasting model that integrates various features, including promotional activities tied to specific products. The model's performance was evaluated using a normalized quantile loss metric, showing an 80% reduction in loss during promotion periods. This example highlights the superior performance of this solution and showcases the power of deep learning in solving a critical issue for the grocery industry. #machinelearning #datascience #forecasting #inventory #prediction – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gWRgTJ2Q 

🚗 Car Market Analysis Dashboard | Excel • SQL • Python I’m excited to share my Car Market Analysis Dashboard, where I explored pricing behavior, vehicle preferences, regional demand, and customer trends in the used & new car market. This project was end-to-end built using Excel, SQL, and Python, allowing me to combine data extraction, transformation, and advanced analysis into a single, insight-driven solution. 🔧 Tools Used: SQL → Data cleaning, filtering, joins, and structured data preparation Python → Exploratory data analysis (EDA), aggregations, trend identification Excel → Interactive dashboard design, KPIs, and visual storytelling 📊 Key Insights from the Dashboard: SUVs and automatic transmission vehicles dominate the market, highlighting comfort-driven demand Toyota leads across listings, reinforcing brand trust and resale strength Black, gray, and silver remain top color choices due to higher resale value Urban regions like Lagos and Abuja drive the majority of car listings Brand New and Foreign Used cars command higher average prices 💡 Business Impact: This dashboard helps dealerships and decision-makers: Optimize inventory based on demand trends Identify high-performing brands and body types Discover regional growth opportunities Improve pricing and marketing strategies This project reflects my ability to connect raw data to real business decisions using multiple tools, while focusing on clean visuals and actionable insights. 📌 Feedback and suggestions are always welcome!

Back in 2018, we had a big problem at Tesla. We needed to scale Model 3 production from 20k to 100k cars per quarter. But the existing supply chain systems simply couldn’t handle this growth. With only a month of cash left, we had to keep the cars moving. We were far too dependent on spreadsheets for planning. They couldn’t keep up with the business and it was having a serious negative impact. Neal Suidan and Michael Rossiter, both leading global demand planning, created something remarkable out of necessity: a unit-level planning system that could simulate and track individual cars through the entire supply chain and match them to demand. This reduced Tesla's inventory from 75 days to just 15, unlocking billions of dollars in working capital at a time when every dollar mattered. Fast forward 7 years and it occurred to us that thousands of companies can use this. They are now bringing that framework to customers with Atomic. Most planning software requires costly integrations and months of setup. Atomic uses AI to eliminate the dreaded spreadsheets, and gets clients onboarded in an hour. The results speak for themselves: - 20-50% reduction in inventory costs while improving in-stock rates - 40+ hours saved per week for planning teams - 3.5x increase in inventory turnover, freeing up millions in cash Today, they announced $3M in seed funding to bring this capability to companies still trapped in supply chain spreadsheet hell. Can’t wait to see what Atomic accomplishes next. https://lnkd.in/e4HrHgqB

𝗪𝗵𝘆 𝗕𝗮𝘆𝗲𝘀𝗶𝗮𝗻 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝘀 𝗔𝗿𝗲 𝗦𝗼 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 A common challenge in data science is dealing with #heterogeneous data, because different regions, customer segments, or product categories may have vastly different amounts of data. Traditional approaches either 𝗺𝗼𝗱𝗲𝗹 𝗲𝗮𝗰𝗵 𝗴𝗿𝗼𝘂𝗽 𝘀𝗲𝗽𝗮𝗿𝗮𝘁𝗲𝗹𝘆, leading to noisy estimates when data is scarce, or force a 𝘀𝗶𝗻𝗴𝗹𝗲 𝗺𝗼𝗱𝗲𝗹 𝗮𝗰𝗿𝗼𝘀𝘀 𝗮𝗹𝗹 𝗴𝗿𝗼𝘂𝗽𝘀, ignoring real differences. 𝗕𝗮𝘆𝗲𝘀𝗶𝗮𝗻 𝗵𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 𝗺𝗼𝗱𝗲𝗹𝘀 offer a different solution. They allow parameters to vary at 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗹𝗲𝘃𝗲𝗹𝘀 𝗼𝗳 𝗱𝗮𝘁𝗮 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀, letting us incorporate not just the data itself but also its underlying structure, #metadata, and the way it was collected. They capture shared #patterns while accounting for group-specific differences. This flexibility makes them ideal for data that’s nested or structured across multiple dimensions. In 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝗮𝗹 𝘀𝗰𝗶𝗲𝗻𝗰𝗲, Bayesian hierarchical models are widely used because they allow scientists to measure effects at different locations, over time, or at different latitudes, all while capturing broader trends. You can read about such one example here: https://lnkd.in/d6ERwa7q In a business use case, such as 𝗿𝗲𝘁𝗮𝗶𝗹 𝗱𝗲𝗺𝗮𝗻𝗱 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴, Bayesian hierarchical models provide: • 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗱𝗮𝘁𝗮 𝗮𝗰𝗿𝗼𝘀𝘀 𝗿𝗲𝗴𝗶𝗼𝗻𝘀, 𝘀𝘁𝗼𝗿𝗲𝘀, 𝗮𝗻𝗱 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗰𝗮𝘁𝗲𝗴𝗼𝗿𝗶𝗲𝘀, capturing both global trends and local variations. • 𝗦𝗲𝗮𝘀𝗼𝗻𝗮𝗹𝗶𝘁𝘆 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴, assuming common patterns across regions but also allowing for regional differences. • 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 #sparse 𝗱𝗮𝘁𝗮, borrowing information from related datasets to improve #accuracy. You can read more about this application: https://lnkd.in/dnkcKi4b In both cases, I used #PyMC for Bayesian modeling. By allowing flexibility and borrowing strength from related data, Bayesian hierarchical models offer a robust approach to #forecasting, 𝗲𝘃𝗲𝗻 𝘄𝗶𝘁𝗵 𝗹𝗶𝗺𝗶𝘁𝗲𝗱 𝗼𝗿 𝘂𝗻𝗲𝘃𝗲𝗻 𝗱𝗮𝘁𝗮. Let me know if you've used Bayesian hierarchical models, I'd love to hear about other use cases. #BayesianInference #HierarchicalModels #DataScience #MachineLearning #Forecasting #RetailAnalytics #PyMC #EnvironmentalScience #DemandForecasting #StatisticalModeling #BusinessAnalytics #GeospatialModeling #PredictiveModeling #DataAnalysis