How to Analyse Your Sales Data Without Hiring an Analyst
Most sales data sits unexamined in Shopify, Stripe, or a spreadsheet. This post shows five practical analyses any operator can run to find their best products, their most valuable customers, and the revenue patterns their competitors are missing — no analyst required.
- Your sales data contains answers you are paying to ignore
- Analysis 1: product revenue concentration
- Analysis 2: customer cohort retention
- Analysis 3: day and hour sales patterns
- Analysis 4: order value distribution
Your sales data contains answers you are paying to ignore#
Every time a customer buys something, a data point is created. The product they chose. The price they paid. When they bought it. Whether they came back. Whether they bought something else. Across a year of trading, most small businesses accumulate tens of thousands of these data points. The majority of those businesses use about five of them: total sales, orders this month, and which product sold the most units. The rest of the data sits in a database, generating storage costs and no insight. Businesses that mine this data systematically — even using simple analyses — consistently outperform those that do not, because they find growth opportunities faster and spot decline signals earlier.
Analysis 1: product revenue concentration#
Start with the 80/20 rule applied to your products. Export all sales transactions from the last 12 months, group them by product or SKU, and rank by total revenue. In most small businesses, 20% of products account for 80% of revenue. Now apply the same analysis to margin, not just revenue. You will almost always find that your top-revenue products are not identical to your top-margin products. Sometimes the products driving the most revenue are barely profitable, subsidised by higher-margin lines that get less attention. Once you know which products are actually driving profit, you can make rational decisions about which to promote, which to reprice, and which to discontinue. Most operators find one or two margin surprises in their first run of this analysis.
Analysis 2: customer cohort retention#
A cohort analysis groups customers by when they first bought from you — say, all customers who made their first purchase in January — and tracks what percentage of each group came back in subsequent months. It sounds complex. The basic version is not. For each month of the last year, count how many new customers made their first purchase. Then count how many of those customers made a second purchase within 90 days. That second-purchase rate is your early retention indicator. If your January cohort had a 35% second-purchase rate within 90 days and your September cohort had 18%, something changed in how you acquire customers or what experience they had. Identifying that change — a new acquisition channel, a product shift, a delivery issue — is the first step to fixing it.
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Analysis 3: day and hour sales patterns#
When your customers buy matters more than most operators realise. Export your orders with timestamps and map them by day of week and hour of day. You will typically find that 60 to 70% of your sales happen in a predictable window. For many retail businesses, it is Tuesday through Thursday, 11am to 2pm and 7pm to 9pm. For food and grocery, it clusters around weekday evenings and Saturday mornings. Once you know your peak purchase window, you can time promotional emails, restock notifications, and social media posts to land when buying intent is highest. Businesses that align their marketing timing to their actual purchase pattern typically see a 15 to 25% improvement in campaign conversion rates without spending more.
Analysis 4: order value distribution#
Average order value is a number most operators know. The distribution beneath that average is one most do not examine. Group all orders into buckets: under £20, £20 to £50, £50 to £100, £100 to £200, over £200. What percentage of your orders sit in each bucket? This tells you where your pricing anchors are working and where they are not. If 80% of your orders cluster in the £20 to £50 range and you have premium products at £150, either customers are not seeing those products or they are not converting. Conversely, if you have a large percentage of very small orders below a profitable threshold, a minimum order value or bundling strategy will immediately improve your unit economics. The distribution tells you which direction to push.
Making sales analysis a weekly habit rather than a quarterly event#
The analyses above are most valuable when run regularly, not as one-time exercises. A product that was your top margin driver in January may have been undercut by a competitor by March. A customer cohort that was retaining well may have started churning in response to a price increase. The operators who spot these shifts earliest have the most time to respond. AskBiz lets you ask questions about your sales data in plain English — "Which product category has the highest margin this month?" or "How does my repeat customer rate compare to last quarter?" — and get answers directly from your connected Shopify or Stripe data. The analysis runs automatically. You ask the question, you get the answer, and you make the call.
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