Price is the single most important driving factor in determining your profit. But it can be hard to get pricing exactly right. There are a several pricing methods to choose from, so which one is right for your business? In this guide, we’ll discuss five pricing methods and the pros and cons of each.

After reading this article, you’ll better understand the different options available to your ecommerce business and be able to pick the one that is best for your unique situation.

Method 1: Demand Pricing

When ecommerce retailers shift pricing based on certain variables like seasonality or time of day, they are applying demand pricing. Uber does this – charge more when demand is high, less when there are fewer riders looking for rides. Another example is seasonal pricing like charging more for bathing suits and less for snow shovels as the warmer months approach.

Pros:

  • Optimizes pricing based on variable factors, which means you’ll sell more product during what would normally be your “slow season”.

  • Because a customer’s perceived value changes as their needs change, your customers will likely be willing to pay the higher amount because you’ve changed it based on a variable that causes their perceived value to be higher.

  • In some cases, it may help cover increased costs during higher demand periods. Conversely, for some companies, demand pricing can help cover fixed costs by increasing volume when business would otherwise be slow.

Cons:

  • Many products and businesses don’t have a measurable factor that can be readily used as an indication of demand to raise and lower prices, or demand simply does not vary in any predictable way. As a result, demand pricing may not be a viable option in many cases.

  • Demand pricing can leave money on the table because it misses consideration of the individual customer and is based instead exclusively on either macro factors or factors internal to the company.

  • It also ignores product-level variances as demand pricing is typically applied to an entire categories or even entire catalogs.

Method 2: Cost-Plus Pricing

Cost-plus pricing is one of the most popular pricing techniques because it’s simple and ensures you realize a positive contribution. It takes into account your costs and your desired gross profit and nothing else.

Pros:

  • Easiest strategy to employ.

  • As long as you’ve calculated correctly, each sale will yield your desired gross profit.

Cons:

  • Often leaves money on the table, either by charging too much and therefore losing sales (e.g. to competitors) or by charging less than buyers are willing to pay and therefore missing out on profit.

  • Doesn’t take into account external variables like competitor’s pricing.

  • It’s static and doesn’t change to reflect market conditions as they change.

  • Essentially ignores the customer by not considering how the price compares to the value the customer gets from the product.

Method 3: Dynamic Optimized Pricing

Dynamic optimized pricing is a pricing method where live prices change based on a set of data and an analytical framework. That data set could be small or enormous and the framework could be a simple set of rules or advanced AI. Generally speaking, the greater the data inputs and the more sophisticated the analytics, the greater the optimization and profit improvement. In addition to the data sets and analytics options, there are many effective optimization approaches. Quite compelling results can be realized when employing two or more optimization approaches simultaneously. We’ll identify just a couple here.

A common approach is to establish a demand curve and select the price on that curve that yields the maximum profit (assuming that’s the goal). To do that, the pricing optimization tool runs live experiments to test different prices and records the corresponding purchases for each price. Through this process, when combined with cost data, the pricing tool discovers the profit-maximizing price.

Another approach is to optimize price with respect to the visitors or customers. In this approach, pricing varies based on a characterization specific to each visitor or customer.

Pros:

  • Dynamic optimized pricing can demonstrably deliver the greatest possible profit of all the pricing methods.

  • Depending on the implementation, an AI-enabled service can automate price changes in real-time as it learns, therefore there is no lost time or profit waiting for manual, periodic updates.

  • Dynamic optimized pricing empowers online retailers with tremendous control over how it is applied. For example, the pricing optimization can be used on some products and not on others. Products that must have a set price for various reasons (e.g. MAP), can keep a set price, while other products can utilize the dynamic optimized pricing method to modulate prices and maximize gross profit.

  • Dynamic optimized pricing services that are AI-powered get better as you use them. With each transaction, the AI acquires new, additional data and analyzes it, thereby continuing to further optimize price and maximize profit.

  • Given its inherently dynamic nature, an AI-enabled approach has the critically valuable benefit of changing as the competitive landscape and other external factors change. Retailers are never left with stale pricing that does not make sense in the context of the then current reality (and that kills sales and/or profit).

Cons:

  • For dynamic optimized pricing to work effectively, it needs data– and enough of it. Without sufficient data, the AI is not able to make decisions that have reliably predictable results. That doesn’t eliminate this pricing method as an option for most retailers, but it means that the chosen solution must address this.

  • A dynamic solution like this requires some complex backend capabilities and non-trivial front-end functionality.

  • There are many real world scenarios that must be taken into account when implementing dynamic optimized pricing. For example, customers could be upset if they return to the site later and the price is different. The dynamic pricing solution you choose should have anticipated scenarios like this and should have functionality that avoids any problems.

Method 4: Competitive Pricing

The competitive pricing method sets prices relative to those of your competitors. This method is especially popular for sellers of commodities where there are a lot of resellers of identical or similar products on the market. Unlike cost-plus pricing, your gross profit is variable. There are many tools and services available that help companies monitor competitors pricing and some even automatically adjust prices. While this method is highly popular, it comes with some significant drawbacks.

Pros:

  • Can be useful in winning extremely price sensitive buyers.

  • Useful for scale resellers pursuing volume in highly competitive markets.

Cons:

  • Competitive pricing doesn’t build any customer loyalty. Buyers are only shopping with you to get the lowest price. If you raise prices, you may retain a small subset of clients who appreciate your superior customer service, but most will discontinue shopping with you once you don’t have the lowest price on the market.

  • eCommerce retailers using the competitive pricing method often find themselves with extremely thin margins. This is especially true when competing with huge retailers that can count on massive volumes- like Amazon and Target. But most ecommerce retailers can’t live on thin margins- or worse, a loss on some products, just to have the lowest prices on the market.

  • Ultimately, competitive pricing is a “race to the bottom” with retailers successively under cutting each other until there is no profit left.

Method 5: Value-Based Pricing

Value-based pricing is based on the value you believe buyers place on your product. This method often takes some testing- moving your pricing up or down a bit to see what buyers are willing to pay for your product. The most important part of value-based pricing is that you need to truly demonstrate the value of your products to your customers in order to charge the highest possible price.

Pros:

  • Can provide more gross profit than cost-plus pricing.

  • Takes into account price elasticity because it’s based on the willingness of customers to buy at certain price points.

Cons:

  • Can take some trial-and-error while you test to discover what the value to your buyers is.

  • Since it groups all your buyers together and assigns an average perceived value rather than actual perceived value of each buyer, it does not maximize profit. This is because it doesn’t take into account differences in individual buyers’ perceived value, meaning if your product is priced at $200 and one buyer perceives the value as $220, they will likely buy your item. If another buyer only perceives the value as $180, there is less of a chance they are willing to pay $200 for the item.

  • Once the ideal price is discovered, the price is generally left alone. That’s problematic as the world is not static; as things change and customers’ perceived value changes, the price that maximizes profit also changes.

While there are many pricing models for ecommerce retailers to choose from, dynamic optimized pricing consistently delivers the greatest profit.