Customer Demand Analysis

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

Sales does NOT equal Demand. This is a comparison between Sales and Demand: Concept ↳ Sales: actual customer purchases ↳ Demand: true market need or desire for a product, whether fulfilled or not Data Source ↳ Sales: historical transactions: shipments, customer pickups, invoices ↳ Demand: includes sales, lost sales, backorders, market research Affected by Availability ↳ Sales: yes; limited by stock, capacity, and fulfillment ability ↳ Demand: no; exists even if not fulfilled Time Period ↳ Sales: past (what actually happened) ↳ Demand: present + future (what people want now and later) Ownership ↳ Sales: typically owned by Sales or Commercial teams ↳ Demand: owned by Demand Planning Use in Forecasting ↳ Sales: input for trend analysis ↳ Demand: basis for planning, replenishment, and capacity Risk for Forecasting ↳ Sales: If used alone: stockouts (missed demand), excess (inflated history), and cash flow issues ↳ Demand: if based on wrong assumptions, same outcome: stockouts, excess inventory and cash flow impact Any others to add?

Stop guessing what customers want. Your competitors' reviews have the answers. Here's my exact process for extracting opportunities from your competitor reviews: Step 1: Gather competitor reviews automatically Use this prompt on Chat GPT Deep research: "Task: Collect up to 100 English-language customer reviews (or as many as are publicly available if fewer than 100) for [Competitor Product/Service] from the following platforms: Amazon Google Reviews Industry forums (e.g., Reddit) [Companies official website] Etc. Requirements: Include both positive and negative feedback for each platform. Only include reviews written in English. There is no restriction on date range – include reviews from any time. If fewer than 100 reviews are available on a platform, include all available. Organize the reviews into a table grouped by platform, with two columns: one for Positive Reviews and one for Negative Reviews." Why it works: → Ensures comprehensive data across multiple platforms → Captures both praise and complaints for complete picture → Structured format makes analysis easier in next steps Step 2: Extract key customer pain points Prompt: "Analyze these reviews and identify the top 5 recurring pain points. For each, include customer quotes and rate the emotional intensity on a scale of 1-10." Why it works: → Focuses on patterns, not outliers → Captures authentic customer language → Prioritizes by emotional impact Step 3: Identify unmet needs across competitors Prompt: "Create a comparison matrix showing which customer needs remain unmet by all analyzed competitors. Highlight the biggest market gaps." Why it works: → Visualizes patterns across competitors → Identifies true market gaps → Prioritizes highest-value opportunities Step 4: Validate findings with targeted research Prompt: "Based on these unmet needs, create 5 survey questions I can use to validate these findings with my own audience." Why it works: → Connects directly to identified gaps → Keeps surveys focused and completion-friendly → Validates before investing resources Step 5: Prioritize opportunities by impact and effort Prompt: "For each opportunity, help me estimate: 1) Revenue impact, 2) Development complexity, 3) Time to market, and 4) Competitive advantage duration. Then rank them." Why it works: → Balances reward against effort → Considers long-term competitive advantage → Forces clear prioritization What product would you like to enhance using this method? Share below and I'll help you craft the perfect prompts for your specific situation.

Find new unmet customer needs by four ways of looking … Identifying unmet customer needs, pains or dreams are crucial. To increase your chances of accurately detecting customers’ problems and dreams, you must diversify how and where you look. That’s why I introduce in my new book ‘Breaking Innovation Barriers’ the ‘Four Ways of Looking’, a new model, originally developed by Louis Barsoux, Michael Wade, and Cyril Bouquet. It involves two main approaches: improve your vision of mainstream users and challenge your vision by looking at unconventional users. 1. The Microscope Strategy. By zooming in on the experiences of your mainstream users you can identify unsurfaced needs through regular focus groups, interviews, or questionnaires. You step into a role of an anthropologist to understand the passions, frustrations, needs, and wants of your users. 2. The Panorama Strategy. By this way of looking, you can find unmet needs of mainstream users by looking at aggregated data, such as errors, complaints, and accidents, that amplify weak signals. Digital tools make it much easier to observe the behaviour of large numbers of individuals. The ‘big data’ needed can be collected from multiple sources like apps and smartphones and can be analysed for trends. 3. The Telescope Strategy. With this strategy you study fringe users, extreme users, nonusers, or even misusers. Demands from small niches are often dismissed as irrelevant. But when you zoom in on users at the periphery, you might uncover pain points that are relevant to the masses too, especially when they are lead users. 4. The Kaleidoscope Strategy. You can also look at distant groups together and find similarities that show unmet needs. It’s like spotting patterns in a kaleidoscope. The challenge, especially for managers in established companies, is to think beyond the usual groups like suppliers, distributors, and competitors. Make use of digital tools and AI to quickly analyse masses of data and identify patterns. Use this new model to diversify you way of finding new unmet customer needs. #customerneeds #jobstobedone #innovation #customerinsights

Sales and Operations Planning (S&OP) helps businesses scale up by aligning various functions - sales, marketing, production, finance, and supply chain. Demand planning is a second step in S&OP process that involves forecasting customer demand to ensure that products can be produced and delivered efficiently. The goal of demand planning is to minimize the risks of overproduction or stockouts. Key elements of demand planning in S&OP include: 📊 Data Collection and Analysis: Gathering historical sales data, market trends, economic indicators, and other relevant information to make informed forecasts. This can also include qualitative data from sales teams and customer feedback. 📈 Forecasting: Using statistical methods and algorithms to predict future demand. Forecasts can be short-term, mid-term, or long-term and may vary in granularity (e.g., by product, region, or customer segment). 🖇 Collaboration: Engaging different departments to agree on a common demand forecast. This ensures that all parts of the organization are aligned and working towards the same goals. 📋 Scenario Planning: Developing different demand scenarios based on varying assumptions to prepare for uncertainties. This helps in creating flexible plans that can adapt to changes in the market. 🔧 Monitoring and Adjustment: Continuously tracking actual sales against forecasts and adjusting plans as necessary. This feedback loop helps to improve the accuracy of future forecasts and allows the organization to respond quickly to market changes. 🔗 Integration with Supply Planning: Ensuring that demand plans are aligned with supply capabilities. This involves coordinating with supply chain, manufacturing, and inventory management teams to ensure that the necessary resources are available to meet anticipated demand. 

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.

I’m going to break down a classic framework here. Here’s how I look at estimating your total number of paying customers. If you have an e-commerce business you should pay attention. Learn how to sharpen these numbers to light the path to profitability and growth opportunities. Size up your total market potential. Zoom in on realistic demand. Converting interest into revenue. Market sizing - it's an integral part of the strategic conversation whenever we're assessing a business concept or mapping growth. I want to dig deeper into the classic breakdown - TAM, SAM, and SOM. These acronyms may seem abstract but really nailing down estimates guides decisions and sets expectations. First, TAM - the total addressable market. This bird's-eye view represents the absolute ceiling of potential customers. For a behemoth like Facebook, TAM encompasses essentially everyone on the planet. Less universal ideas have more contained TAMs, but it's still an expansive perspective. Then we have SAM - the serviceable addressable market. Here we narrow down to the segments we can readily capture based on geography, demographics, our business model and capabilities. SAM analysis spotlights untapped pockets within TAM to inform expansion decisions. Finally, SOM brings it to the bottom line - the serviceable obtainable market of likely buyers who'll actually convert. We size this through data analysis and projections of consumer behavior. Realistic SOM forecasting is critical for financial modeling and carving an achievable path to profitability. These market lenses enable some key strategic planning: First and foremost, gap analysis between SOM and higher levels grounds viability. A wide gulf exposes flawed assumptions or a non-starter idea. Secondly, SOM drives revenue planning through pricing optimization and customer LTV modeling based on purchase cycles. We expand transaction volume and wallet share over time. And thirdly, the distance between SOM, SAM and TAM informs go-to-market prioritization and growth capital allocation as we stair-step to scale. Of course, market estimation isn't an exact science. But refined TAM, SAM and SOM analysis, updated iteratively as data flows, provides essential guidance for startups through established players alike. I’d love to hear your thoughts and experiences leveraging sizing estimates to steer strategy. What challenges have you run into?

A few months ago, a marketing team at an e-commerce platform was struggling with customer churn despite running aggressive discount campaigns. The assumption was that offering more discounts would improve retention, but after SQL-driven analysis, the real issue turned out to be low repeat purchase rates among first-time buyers. Reducing Customer Churn with Data Analytics 1️⃣ Identifying At-Risk Customers We analyzed repeat purchase behavior to find the drop-off point. SELECT customer_id, COUNT(order_id) AS total_orders, MIN(order_date) AS first_order_date, MAX(order_date) AS last_order_date, DATEDIFF(day, MAX(order_date), GETDATE()) AS days_since_last_order FROM orders GROUP BY customer_id HAVING COUNT(order_id) = 1 AND DATEDIFF(day, MAX(order_date), GETDATE()) > 30; 🔹 Insight: A large percentage of first-time buyers never returned after their initial purchase. 2️⃣ Finding the Root Cause of Low Repeat Purchases We compared product categories and delivery experiences of repeat vs. non-repeat customers. SELECT product_category, COUNT(DISTINCT CASE WHEN repeat_purchase = 1 THEN customer_id END) AS repeat_customers, COUNT(DISTINCT CASE WHEN repeat_purchase = 0 THEN customer_id END) AS churned_customers, AVG(delivery_time) AS avg_delivery_days, AVG(customer_rating) AS avg_rating FROM orders JOIN customer_feedback ON orders.order_id = customer_feedback.order_id GROUP BY product_category ORDER BY churned_customers DESC; 🔹 Insight: Customers who purchased from low-rated categories (e.g., fragile items, late deliveries) were less likely to return. 3️⃣ Improving Customer Retention with Targeted Offers Instead of random discounts, we personalized retention campaigns based on customer behavior. SELECT customer_id, CASE WHEN last_order_category = 'electronics' AND days_since_last_order > 30 THEN 'Offer 10% discount on accessories' WHEN last_order_category = 'fashion' AND days_since_last_order > 45 THEN 'Send personalized style recommendations' ELSE 'No action needed' END AS retention_strategy FROM customer_behavior; 🔹 Insight: Instead of blanket discounts, category-specific retention strategies performed better. Challenges Faced One-time buyers made up a large chunk of new customers, leading to low retention. Poor delivery experiences negatively impacted repeat purchase rates. Generic discounting strategies weren’t increasing loyalty. Business Impact ✔ 12% increase in repeat purchases by improving category-based retention strategies. ✔ Better allocation of discount budgets, leading to a higher ROI on marketing spend. ✔ Enhanced customer experience, reducing negative reviews and churn. Key Takeaway: Not all churn is due to pricing—delivery quality, product experience, and personalized engagement play a bigger role in long-term customer retention. Have you tackled churn problems with data? Let’s discuss!

It typically starts with a cup of coffee. But is your fintech startup solving a real problem -- or are you just guessing? 🙈 According to research (see sources in the comments): 1/ 35% of startups fail due to a lack of market need (CB Insights, 2024). 2/ 34% of startups shut down specifically because of poor product-market fit (DesignRush, 2025). 3/ 42% of founders highlight lack of product-market fit as their main cause of failure (LinkedIn Startup Report, 2025). 4/ Lack of market validation consistently ranks as the top reason fintech startups fail, even surpassing funding issues (Exploding Topics Report, 2025). 5/ Real-world analysis of 42 failed startups found lack of market need to be the primary cause of their demise (Failory, 2024). Imagine this scenario: In a crowded coffee shop, a fintech founder sketches an idea on a napkin -- an API-first solution enabling freelancers to receive instant payments without hefty fees. Instead of immediately building or prematurely selling the product, the founder organizes an Idea Refinement Session with a strategic advisor, mapping potential revenue streams and regulatory challenges directly onto that napkin. Within just two weeks, 15 potential customers are interviewed. 12 customers express genuine interest and sign Letters of Intent. The founder’s Validation Scorecard lights up green -- a clear signal of validated demand. 💡 Why should you care? → Because startups with proven demand secure funding faster, survive longer, and significantly reduce their risk of failure. → Validated demand sends a powerful message to investors: real traction, real customers, real opportunity. 🧐 The alternative? → Becoming part of the statistic -- startups that fail due to unvalidated assumptions about their market. So, what should you do next? ▶️ Before building or pitching investors, validate demand clearly using these proven options: 1/ Customer Interviews → Understand your customers’ pains deeply. 2/ Letters of Intent (LOIs) → Get formal commitments from potential customers. 3/ Pre-sales or Deposits → Provide tangible proof customers are willing to pay. 4/ Landing Page or Waitlist Sign-ups → Track measurable signs of market interest. 5/ Pilot Projects or Beta Users → Test your idea in real-world conditions. 6/ Crowdfunding Campaigns → Demonstrate public enthusiasm and market appetite. 7/ Validation Scorecards → Objectively measure and communicate traction. Stop guessing. Start validating. Your investors -- and your startup’s future -- depend on it. ▶️ If you are a founder and need help validating your idea, apply to our INSART Fintech Business Accelerator -- drop me an email vas.solo@insart.com #demand #market #product #problem #validation