How to Quickly Optimize Pricing for E-commerce Products

Planning to sell a new product online?

Early price optimization gives you a significant advantage. That’s because it can help you reach profitability faster, and most small marketers that I’ve worked with don’t do it at all as a deliberate step.

How should you price your product?

You have plenty of pricing strategies to choose from. For example, if your product is very similar to others on the market, you might undercut the competition to attract early sales (penetration pricing). Alternatively, you might position yourself as a “masstige” or even premium product/brand.

However, many new products are innovative with no clear competition for pricing reference. The value to the consumer may be hard to quantify (ruling out value-based pricing), and the cost of goods are low, allowing wide pricing latitude. So should you sell high-volume at $29.99, or go for $49.99?

Find optimum pricing using your demand curve

 

The above is an actual demand curve for a new product we’re testing. You’ve probably seen charts like this in a long-ago microeconomics class.

Our test product has a simple sales funnel: traffic is from Facebook ads, which hits our sales landing page, then enters a standard checkout flow.

The primary (left) vertical axis shows the product price. The horizontal access shows the resulting sales conversion rate (% of traffic that results in a sale).

As you’d expect, the higher the price, the lower the conversion rate.

The blue line is the profit (gross per unit sold). You can see the profit peaks at a conversion rate of about 1.75%, which is reached with a price of about $45.

So $44.99 is an optimum price point to maximize profit.

E-commerce Pricing strategy: Choosing a nonoptimal price

The above only considers maximizing profit-per-sale, and there may be reasons to deviate from that point.

For example, if you have limited product supply, you may choose to sell fewer units at a higher price, and rely on bonus items to increase conversion.

You may also want to optimize your profit margin instead of gross profits, which generally have different optimal pricing. In this example you can see the optimum profit margin happens at $40, but the optimum gross profit at $45.

In the case of our test product, we’re selling at a lower price ($19.99) in order to get more conversions and data points, which lets us optimize our sales funnel faster. From the chart you can see that our $19.99 price actually results in $0 profit (breakeven), because the goal of test-marketing is to learn, not to profit.

How to generate your product demand curve

Simply do a split-test on your pricing. Test at least two prices with enough traffic to reach statistical significance. If your e-commerce software doesn’t easily allow split testing, do it sequentially instead.

Three or more price points is even better. With just two points, you must assume a linear price-demand curve, and your results may be affected by price point effects.

Calculate conversion rate in the standard way:

Conversion (%) = 100 * (total sales) / (sales page visitors)

 

Consider all your variable costs to calculate profit:

Profit (per unit) = Price – (Cost of Goods) – (Cost Per Order) – (fulfillment, etc.)

Profit = P – COG – CPO

 

For our test product, the COG (including free shipping and fulfillment) is $7. The CPO is (ad cost per click) / (conversion rate). So for example a $0.60 CPC / 2% = $30 CPO.

Price elasticity of e-commerce products

Once you calculate your demand curve, the slope of your curve indicates your product’s price elasticity. A steep curve means a small change in price results in a large change in sales volume. Or you may find your curve is fairly flat (“inelastic”), meaning that your market is relatively price insensitive.

Knowing your demand curve can help you plan your whole e-commerce strategy, including how to craft better offers, such as running BOGOs or bonus items.

 

Published by

Alex Frakking

Alex applies lean product development principles to help product developers test, refine, and market their consumer products. He writes about concept validation and optimization, simulated test marketing, and product distribution strategy.