Analyzing Sales Trends for Better Forecasting

Your forecast accuracy is probably terrible. Here's why and how to fix it. Most sales leaders are forecasting based on rep gut feeling instead of buyer behavior. I analyzed 1,000+ deals last quarter and found the pattern: Traditional forecasting asks "What's your confidence level on this deal?" "When do you think it will close?" "How committed is the buyer?" Buyer-behavior forecasting asks "When did the buyer say they need to make a decision?" "What's their documented evaluation timeline?" "What competing initiatives are they prioritizing?" The difference is massive. Reps guess. Buyers have actual timelines. The best sales leaders I work with have completely separated "rep forecast date" from "customer decision date" in their CRM. This creates healthy tension between hope and reality. If there's no customer decision date with evidence, the deal doesn't belong in your forecast.

Most businesses panic when they see their average order value (AOV) drop 25%. They then… - Slash prices - Rush promotions - Question their premium products But smart retailers know better — they investigate patterns first. Here are a few to get you started: 1. Sales data Your 6-month trends reveal the first signs of change: - Did price changes affect order value? - Which products are selling more or less? - What's the pattern in shopping cart composition? - What does purchase frequency tell us? - What's hiding in abandoned carts? - Are premium products getting abandoned? 🧩 Let’s say you see premium items getting abandoned at checkout repeatedly. Looking deeper, you might find a specific price threshold — leading to an opportunity for strategic bundling. 2. Website behavior Tools like CrazyEgg, LuckyOrange, Hotjar, and FullStory show complete interaction patterns: - Most visited pages - Heat map patterns - Premium product engagement 🧩 Are customers spending time on review sections but leaving? You might need stronger social proof and not necessarily lower prices. 3. Customer voices Data tells half the story, and your customers tell the other half. Direct fact-finding reveals… - Customer sentiments on new premium products - Views on popular vs. unpopular items - Feedback on existing products Social media conversations add another layer of insight. 🧩 Suppose your focus groups reveal confusion about premium features. This could signal you need better education — not different products. 4. Competitive landscape A comprehensive look at your market reveals if competitors… - Launched promotions that coincided with the change - Introduced new products during your AOV drop - Brought innovative solutions to the market - Lowered their existing product prices 🧩 Did you notice your AOV drop right when a competitor introduced similar products at lower prices? This is a direct connection between market changes and your sales patterns. 5. Long-term trends Customer surveys help you identify shifts in popularity before they hurt your bottom line. 🧩 If they show customers gradually losing interest in a once-popular product category… You’ve spotted a trend that explains your dropping order value (and suggests you should act accordingly). 💡 Remember this: Numbers don't drop without reason. Patterns don't form by accident. Solutions don't come from guessing. Understanding your customers' behavior is the difference between reacting and leading.

Because with wrong demand forecasting everything else falls apart... This infographic shows 10 red flags in demand forecasting and how to turn them green: 🚩 # 1 - Over-reliance on historical data How to Turn Green: incorporate external data like market trends, competitor activity, and consumer sentiment to enrich forecasts 🚩 # 2 - Ignoring promotions and discounts How to Turn Green: build a promotions-adjusted forecasting model, considering historical uplift from similar campaigns 🚩 # 3 - Forgetting cannibalization effects How to Turn Green: model cannibalization effects to adjust forecasts for existing products 🚩 # 4 - One-size-fits-all forecasting method How to Turn Green: use demand segmentation (for example, high variability vs. stable demand); do not treat all SKUs equally 🚩 # 5 - Not monitoring forecast accuracy How to Turn Green: track metrics like MAPE, WMAPE, bias, and forecast value-add (FVA) to improve over time 🚩 # 6 - High forecast error with no accountability How to Turn Green: tie accountability to S&OP (sales and operations) meetings 🚩 # 7 - Poor collaboration with sales and marketing How to Turn Green: hold regular cross-functional meetings to align forecasts with upcoming campaigns 🚩 # 8 - Over-reliance on intuition How to Turn Green: balance judgment-based inputs with statistical and AI-driven models 🚩 # 9 - Infrequent forecast updates How to Turn Green: move to a rolling forecast system that updates regularly based on the latest data 🚩 # 10 - Past sales (instead of demand) consideration How to Turn Green: make the initial predictions based on the unconstrained demand; not on sales that are impacted by cuts and out of stock situations Any others to add? #supplychain #salesandoperationsplanning #integratedbusinessplanning #procurement

Carpe Sales (Easy method to capture insights) As the year draws to a close, Many businesses have access to Sales data for products on a weekly basis. This data can reveal either markets that work, Or channels or retailers that do not work, In the context of the business strategy And in creating long term goals. Here is an easy t-test method to compare Any two markets or channels or outlets Where you sold products in 2024. Statistical insights matter! ________________________________________ Example of Store vs. Website sales: As seen in the Post-it, over 50 weeks In-store sales per week averaged 511 units, With a standard deviation of 65 units. Online sales per week averaged 531 units, With a standard deviation of 75 units. It is tempting to say that the website did better Because it had higher average sales of 20 units/week Or 1000 units over the full year. However, hypothesis-testing is necessary. * Ho: Website does not perform better than Store. * Ha: Website performs better than Store. * Difference of Means t-test * Calculate standard error and difference * Observed t = -1.42 * Critical t = -1.64 (alpha = 0.05) * Absolute value of Observed t < Absolute Value of Critical t * Fail to reject Ho at 95% confidence. Thus, we cannot conclude that website sales are better. __________________________________________________________ Actionable Insights: 1. You do not need complicated tests. 2. Your data team can help you with these. 3. Do not jump to conclusions based on totals. 4. Dive deeper into metrics before choosing strategy. 5. Dashboards can hide such insights due to aggregations. Follow Dr. Kruti Lehenbauer or Analytics TX, LLC on LinkedIn #PostItStatistics #DataScience #AI or #Economics tips. P.S. Do you use any quick statistical tests in your business?