Pricing is the single biggest lever in your business. A 1% improvement in price, assuming volume holds, drops straight to the bottom line. Yet most merchants set prices once -- based on a gut feeling, a competitor's listing, or a simple cost-plus formula -- and rarely revisit them.
The problem isn't that merchants don't care about pricing. It's that they don't have the data infrastructure to price intelligently. Your ecommerce platform shows you revenue. It shows you units sold. What it doesn't show you is which price points maximize profit, which products have room for a price increase, and where your markdowns are eating your margins.
This guide covers how to move from gut-feel pricing to data-driven pricing using analytics you likely already have access to.
Cost-plus vs. value-based pricing
Most merchants start with cost-plus pricing: take your cost of goods, add a target margin, and that's your price. If a product costs $20 and you want a 50% margin, you price it at $40.
Cost-plus is easy to calculate and guarantees a minimum margin on every unit sold. But it has a fundamental flaw: it ignores what the customer is willing to pay. If customers would pay $55 for that product, you're leaving $15 on the table per unit. If they'd only pay $32, you're losing sales to competitors.
Value-based pricing flips the approach. Instead of starting with your cost, you start with what the market will bear -- then work backward to determine if the margin justifies carrying the product.
In practice, most merchants need a hybrid approach:
- Cost-plus as a floor. Never price below your minimum acceptable margin. For most ecommerce businesses, this is 30-50% gross margin depending on the category.
- Value-based as a ceiling. Use market data, competitor pricing, and customer behavior to find the maximum price the market will support without significantly hurting volume.
- Analytics to find the sweet spot. The optimal price lives somewhere between your cost-plus floor and your value-based ceiling. Data tells you where.
Using sales velocity data at different price points
The most direct way to understand price sensitivity is to look at what happened when prices changed. If you've ever run a sale, adjusted prices on a product, or tested different price points, you already have this data -- you just need to extract it.
Here's what to look for:
Sales velocity before, during, and after price changes. If you dropped a product from $45 to $39 and units per day went from 4 to 7, you can calculate the price elasticity. In this case, the 13% price decrease produced a 75% volume increase -- which means revenue went up (from $180/day to $273/day) and the product is price-elastic.
But revenue isn't profit. If your cost is $18 per unit:
| Price point | Units/day | Revenue/day | Margin/unit | Profit/day |
|---|---|---|---|---|
| $45 | 4 | $180 | $27 | $108 |
| $39 | 7 | $273 | $21 | $147 |
| $35 | 9 | $315 | $17 | $153 |
| $29 | 12 | $348 | $11 | $132 |
In this example, $35 maximizes daily profit -- not $29 (highest volume) or $45 (highest margin per unit). Most merchants would never find this without analyzing the data.
Seasonal patterns. Some products have price points that work at different times. A candle priced at $28 might sell steadily at that price most of the year but could move at $34 during the holiday season when demand spikes and gifting drives urgency. Pricing doesn't need to be static.
Category-level patterns. You might find that your skincare products are price-inelastic (customers buy regardless of modest price changes) while your accessories are highly elastic (a $3 increase kills volume). This tells you where you have pricing power and where you don't.
Margin analysis by product
Revenue hides margin problems. A product generating $50,000 in monthly revenue looks great until you realize it has a 12% margin after COGS, shipping, and returns -- delivering just $6,000 in gross profit. Meanwhile, a product generating $15,000 in revenue at 55% margin delivers $8,250 in gross profit.
Effective pricing requires product-level margin visibility:
Gross margin per SKU. Calculate (Price - COGS) / Price for every product. Sort by margin and you'll typically find a wide spread -- some products at 60%+ and others below 20%. The low-margin products deserve scrutiny: can they be repriced, renegotiated with suppliers, or discontinued?
Contribution margin per SKU. Go beyond gross margin to include variable costs: shipping, payment processing fees (typically 2.5-3%), packaging, and platform fees. A product with a 40% gross margin might have a 28% contribution margin after these costs. This is the real number that matters.
Margin per order. Some products have high margins individually but are typically purchased with discount codes or as part of bundles that reduce the effective margin. Analyze margin at the order level, not just the product level.
Return-adjusted margin. If a product has a 15% return rate, your effective margin drops by that rate (plus reverse logistics costs). A $50 product with a 45% gross margin and a 15% return rate has an effective margin closer to 35% after accounting for returns.
Spark by MishiPay surfaces this margin data automatically. Connect your store and ask "What are my lowest-margin products by contribution margin?" or "Which products have the highest return-adjusted margin?" You get a ranked list with actual numbers, not just revenue figures that mask profitability.
Price sensitivity testing
If you don't have historical price change data, you can generate it deliberately. Here are practical approaches:
A/B testing prices
If your platform supports it (or you use a tool like Google Optimize), test two prices simultaneously. Show 50% of visitors the current price and 50% a higher price. Measure conversion rate and revenue per visitor -- not just conversion rate alone, since a lower conversion rate at a higher price might still generate more profit.
Run the test for at least two weeks and 1,000 visitors per variant to get statistically meaningful results.
Sequential testing
If A/B testing isn't feasible, test prices sequentially: two weeks at price A, two weeks at price B. This is less rigorous (seasonal effects and marketing activity can confuse the results) but still better than guessing.
Bundle pricing tests
Instead of changing a product's price directly (which can confuse repeat customers), test different bundle configurations. Offer the product at $45 solo or $75 for two. If the bundle conversion rate is strong, it tells you customers see value well above the solo price point.
Anchor pricing
Test the impact of showing a "compare at" price or a crossed-out original price. If showing "Was $55, now $42" dramatically increases conversion versus just showing "$42," the product's perceived value is closer to $55 -- and you might have room to raise the "sale" price to $46 or $48.
Markdown optimization
Markdowns are where pricing strategy and inventory management intersect. Most merchants markdown too late and too deep.
The early markdown principle. A 15% markdown taken when you still have 60 days of supply is almost always better than a 40% markdown taken when you have 120 days of supply and need to clear space. The early, smaller markdown preserves more total margin.
Graduated markdowns. Instead of a single deep cut, use staged reductions: 10% off in week 1, 20% off in week 3, 30% off in week 5. Monitor velocity at each stage. If the product clears at 10% off, you saved 20-30% of margin versus going straight to the deep discount.
The carrying cost calculation. Before deciding on a markdown, calculate the cost of holding the inventory. If storage costs $0.50 per unit per month and you have 200 units moving at 1 per day, you're spending $100/month in storage for inventory that will take 200 days to clear. A 25% markdown that doubles velocity saves you months of storage costs.
Competitive pricing signals
Competitor pricing is a signal, not a directive. Here's how to use it without becoming a price-follower:
Monitor, don't match. Track competitor prices on your top 20 products. If a competitor drops their price on a product you both carry, note it -- but don't automatically follow. Check your own velocity data first. If your sales haven't declined, the market is supporting your higher price.
Use competitor pricing to identify opportunities. If every competitor prices a product category between $35-40 and your margin structure supports pricing at $32, you can undercut strategically to gain share. Conversely, if competitors are racing to the bottom on a commodity product, it might not be worth competing on price at all.
Track the premium you command. If your product consistently sells at $48 while competitors sell similar items at $38, you have a 26% price premium. That premium reflects brand value, better photography, stronger reviews, or superior customer experience. Know your premium and protect it.
Putting it together: a pricing review process
Set a quarterly pricing review. For each product category:
- Pull margin data. Identify products below your minimum acceptable margin.
- Check velocity trends. Are units per day trending up, flat, or down?
- Review any price change history. What happened when prices moved?
- Scan competitor prices. Have competitors adjusted up or down?
- Make targeted adjustments. Test price increases on 3-5 products where the data supports it. Markdown slow-moving inventory before it becomes dead stock.
This process takes a few hours per quarter and can move your overall margin by 2-5 percentage points -- which, on $500,000 in annual revenue, is $10,000-25,000 in additional profit.
The bottom line
Pricing is not a one-time decision. It's a continuous optimization problem, and the merchants who treat it that way outperform those who set prices once and forget. The data you need is already in your sales history -- velocity at different price points, margin by product, markdown performance, and competitive positioning.
The first step is visibility. If you can't see your margin by SKU, you can't make informed pricing decisions. Start there, and the rest follows.
See your margins by product
Spark surfaces contribution margins, return-adjusted profitability, and pricing insights across your entire catalog. Stop guessing, start optimizing.