Using analytics to find your optimal price points

Pricing is usually guesswork

Most e-commerce founders set prices based on costs plus a markup, competitor matching, or gut feel. These approaches might work, but they rarely optimize. You’re likely leaving money on the table or pricing yourself out of sales.

Your analytics contain signals about optimal pricing. Learning to read those signals helps you price more profitably.

The pricing optimization goal

Optimal pricing maximizes profit, not revenue or conversion rate.

The trade-off:

Lower prices typically increase volume but reduce margin per unit. Higher prices typically reduce volume but increase margin per unit. Optimal price is where profit (margin times volume) is maximized.

Why this matters:

A 20% price increase that loses 10% of volume increases profit significantly. A 20% price decrease that gains 10% volume might reduce profit. The math isn’t intuitive, which is why analysis helps.

Measuring price elasticity

Price elasticity tells you how much demand changes when prices change.

The concept:

Elastic demand: Small price changes create large volume changes. A 10% price increase loses 25% of sales. Inelastic demand: Price changes have limited volume impact. A 10% price increase loses only 5% of sales.

Why elasticity matters:

With elastic demand, price increases hurt profit (you lose too much volume). With inelastic demand, price increases help profit (volume loss is small relative to margin gain).

How to estimate:

Compare periods with different prices for the same product. Calculate the percentage change in volume relative to percentage change in price. This gives rough elasticity.

Using historical price changes

If you’ve changed prices in the past, that data reveals elasticity.

The analysis:

Identify products where you’ve changed prices. Compare volume before and after the change, controlling for seasonality and other factors. Calculate how much volume changed relative to price change.

Confounding factors:

Other things change besides price. Marketing spend, seasonality, competitor actions. Try to isolate price impact by controlling for what you can.

Building a dataset:

Track every price change you make along with before/after volume data. Over time, you build a dataset that reveals patterns.

Promotion data as price insight

Promotions are temporary price changes that generate pricing data.

What promotions reveal:

A 20% off sale shows you how volume responds to lower prices. Compare promotional volume to baseline volume. The difference indicates price sensitivity.

The discount depth question:

Does 20% off generate twice the volume lift of 10% off? Often not. Understanding where the volume response flattens helps optimize promotion depth.

Promotional analysis:

Track each promotion’s lift over baseline. Calculate lift per discount point. Identify where diminishing returns begin.

Competitor price monitoring

Competitor prices create context for your pricing decisions.

What to monitor:

Track key competitors’ prices for similar products. Note when they change prices. Observe how your volume responds to their price changes.

Price gap analysis:

What happens when you’re 10% above competitors? 20% above? Below? Track conversion rate and volume at different price gaps.

Competitor response to your changes:

When you raise prices, do competitors follow? When you lower prices, do they match? These responses affect your optimal strategy.

Conversion rate by price point

Conversion rate at different price points indicates customer price sensitivity.

Cross-product comparison:

Compare conversion rates across products at different price points. Do $30 products convert at similar rates to $50 products? Where do conversion rates drop off?

Threshold identification:

Look for price thresholds where conversion drops significantly. A product might convert well at $49 but poorly at $55. These thresholds matter for pricing decisions.

Adjusting for other factors:

Conversion rate varies for reasons beyond price. Product appeal, page quality, and traffic source matter. Control for what you can when drawing pricing conclusions.

Margin optimization analysis

Identify products where margin improvements are most impactful.

Volume-weighted margin analysis:

High-volume, low-margin products have the most improvement potential from price increases. Even small price increases on high-volume items significantly impact total profit.

Margin floor identification:

What’s the minimum margin that makes sense for your business? Products below this floor either need price increases or discontinuation.

Testing price changes

Direct price testing provides the clearest elasticity signals.

Before/after testing:

Change price and measure volume change. Simple but subject to other variables changing simultaneously.

Geographic or segment testing:

If possible, test different prices in different markets or segments. Compare results while other factors stay constant.

Gradual increases:

Raise prices gradually rather than dramatically. Monitor volume response at each level. Stop when volume decline offsets margin gain.

Bundle and quantity pricing

Bundle pricing creates value while optimizing margin.

Bundle analysis:

Track bundle take rates at different discounts. What discount level drives sufficient bundle adoption? Calculate overall margin impact.

Quantity break analysis:

If you offer quantity discounts, track uptake. Are customers buying more units to reach discounts? Calculate whether the volume increase offsets the discount.

Shipping threshold optimization

Free shipping thresholds are pricing in disguise.

Threshold analysis:

Track order value distribution around your free shipping threshold. What percentage of orders fall just above versus just below?

Threshold testing:

Test different thresholds. Does raising the threshold increase AOV enough to offset any conversion loss?

Building pricing analytics capability

Develop ongoing pricing intelligence.

Track every change:

Log all price changes with dates and reasons. Build a database of pricing experiments over time.

Regular analysis cadence:

Review pricing data monthly or quarterly. Look for patterns and opportunities.

Margin monitoring:

Track margin by product and category. Identify where margin erosion is happening and address it.

Metrics for pricing analytics

Focus on these pricing-related metrics:

Volume change relative to price change (elasticity). Conversion rate by price point. Promotion lift by discount depth. Price gap versus competitors. Margin by product and category. Bundle and quantity discount take rates. Order distribution around shipping thresholds. Revenue and profit response to price changes.

Pricing doesn’t have to be guesswork. Your data contains signals about how customers respond to price. Learn to read those signals and price for profit optimization.