Where Revenue Comes From

Ahmad owned a small household and lifestyle store in Klang Valley. He sold hundreds of different items—kitchen tools, home décor, cleaning supplies, personal care products. Sales were steady but unspectacular. Every month, he ran discount campaigns hoping to boost revenue. Some campaigns worked. Most didn't. He had no idea why. He kept discounting because that's what competitors did, but his profit margins kept shrinking.

Ahmad hired a data analyst to study one year of sales records—every transaction, every campaign, every product sold, every customer who walked in. The analyst looked at four questions: what drives revenue, which campaigns actually work, how to manage inventory, and which customers are worth keeping. Ahmad wanted simple answers he could use immediately.

The data showed that products and basket size drove revenue, not discounts. Customers who bought more items spent more money. Campaigns with wider product variety performed better than campaigns with deep discounts. Full-price sales brought in higher revenue than discounted sales because they attracted customers who spent more and returned fewer items. November and December were peak months. February was the weakest. The patterns were clear once someone actually looked at the numbers.

Campaign analysis revealed that only a few campaigns generated most of the revenue. One campaign massively outperformed the rest. It featured a broad mix of products and ran longer. The worst campaigns offered aggressive discounts but limited product selection. Customer spending stayed consistent across all campaigns—between RM88 and RM96 per transaction—so discounts didn't increase basket size. They just reduced profit margins.

Product demand was surprisingly uniform. Most items sold one or two units per day. There were no bestsellers and no disasters. Sales spikes came from campaigns, not seasons. This meant Ahmad could keep lean inventory without risking stockouts. Growth wouldn't come from finding a magic product. It would come from improving the product mix as a whole and running better campaigns.

Customer analysis split buyers into three groups. Most people bought once or twice and disappeared. A second group made a few purchases but didn't become regulars. A small third group kept coming back, bought frequently, rarely returned items, and generated most of the long-term value. Campaigns with broader product ranges attracted more repeat customers. Discount-heavy campaigns brought one-time buyers who never returned.

Ahmad restructured his approach. He stopped broad discounting. He focused year-end marketing on November and December when demand naturally peaked. He used targeted promotions in February to counter the slowdown. He invested more in campaigns that featured product variety instead of price cuts. He tracked which campaigns brought repeat customers and doubled down on those formats. He kept inventory levels stable and stopped chasing fictional bestsellers.

Today, Ahmad knows where his revenue comes from. He plans campaigns around what the data shows works, not what feels right. He identifies high-potential customers early and focuses retention efforts on them. He sets realistic targets based on seasonal patterns. His profit margins improved because he's not discounting everything hoping something sticks. The analysis didn't uncover magic—it just removed the guesswork.