Pricing can be a Goldilocks-like challenge. If you ask for too much, customers won’t want to pay; if you sell your wares for too little, your margins (and ultimately your business) will suffer. In other words, your prices need to be just right, like Goldilocks’s porridge. This is called price optimization, and it’s a key factor in achieving sustainable sales and profitability.
What is price optimization?
Price optimization uses data analysis to determine the optimal price point for your product or service. Businesses adjust price points based on consumer demand, market conditions, competitor pricing and activity, seasonality, and current events.
An intentional pricing strategy can help you balance maximizing sales and sustaining profitability. It’s also a key tool for attracting and satisfying customers, winning against the competition, and adapting to market changes.
Price optimization strategies
You can select from several different optimization models. Many businesses use more than one price optimization strategy, and larger companies often build complex proprietary algorithms taking multiple pricing models and key pricing variables into account.
Here are a few different price strategies you can implement, depending on your business and objectives:
Dynamic pricing
Dynamic pricing is a category of pricing modelsin which you change the price of a product to adjust for changes in customer demand, supply, or price elasticity. Dynamic pricing strategies can be time-based—for example, airlines raise ticket prices during the high-demand holiday season—or segment-focused, like offering special prices to teachers or a steep discount for new customers.
Value-based pricing
Some businesses, like software providers, may adopt different pricing tiers. Limited features available to one user may be free or inexpensive, while “pro” features and access for multiple team members cost more.
Cost-plus pricing
This straightforward method allows you to set prices by adding (marking up) a specific percentage or dollar amount to the production cost of a single unit. Cost-plus pricing typically does not consider or prioritize competitor prices.
Loss leaders
This method involves pricing certain products below production costs to attract customers who will purchase other profitable items. For example, grocery stores often implement loss-leader pricing with the expectation that shoppers will buy other full-price items (like cereal) in addition to the loss leaders (like milk).
Bundle pricing
This method often focuses on a certain customer segment, offering a discount on multiple items to get customers to buy more. For example, bundle pricing can be applied to:
- Physical products and software services
- A starter kit of items aimed at new customer
- Specific customer segment tools for CRM software
- A collection of home goods with a similar aesthetic
How to optimize pricing
- Gather data
- Segment your customer base
- Analyze price sensitivity
- Set pricing objectives
- Test and adapt
- Monitor market changes
In an ever-changing marketplace, pricing optimization can help your ecommerce business stay competitive, maximize revenue, and achieve customer satisfaction. While specific price optimization solutionswill vary based on your company and industry, the process generally involves these steps:
1. Gather data
Collect relevant data on your products, customers, and the broader market landscape, including competitors. This data can come from a mix of internal and external sources:
- Internal data: Consider historical sales data, demographic information about your customers, inventories, production costs, product differentiators, churn data, demand fluctuation over time, and customer surveys.
- External data: This includes your competitors’ prices and offerings, analyst reports about overall market performance and future outlook, and predictions about geopolitical or weather events that could affect your sourcing or customer demand.
📖Read more: The Ultimate Guide To Price Monitoring Tools for Ecommerce
2. Segment your customer base
Use demographic and customer data to analyze your base. Uncover patterns to help you categorize your customers into different segments, each with unique buying behaviors, preferences, and needs. Some may be on-and-off customers who respond well to a discount price; others may be frequent, long-term customers who focus on high-value items.
3. Analyze price sensitivity
Your various customer segments may have different sensitivities to price and price changes. This concept is called price elasticity, or the way demand for a good or service changes alongside changes in its price.
You can use your historical sales data to assess how price changes typically affect demand and, as a result, sales. Here’s the price elasticity of demand expressed as an equation:
Price elasticity of demand = Percentage change in quantity demanded / percentage change in price
This step aims to determine how demand varies with price changes and to find the price thresholds at which customer demand increases or decreases. If your product’s price elasticity is greater than one, demand is elastic. If it’s less than one, demand is inelastic.
4. Set pricing objectives
Whatever your overall business goals are, your pricing process should directly reflect them. You might want to lower prices to sell more quantities of an item reaching the end of its usability or going out of season. Or perhaps you want to increase the perceived value of a service by increasing the price.
Some businesses might aim to capture more value from a specific customer segment, while others try to optimize the profit margins of a particular item. Use price elasticity of demand to determine optimal pricing by setting higher prices for inelastic goods and lower prices for elastic goods to maximize revenue.
📖Read more: What Is a Price-to-Value Strategy? Definition and Benefits
5. Test and adapt
Conduct pricing experiments and gather data about each experiment’s impact on sales and profitability. You might try one price optimization strategy to increase customer lifetime value (CLV) among existing customers, and another discounting the initial price for new customers. Monitor results continuously, and use these insights to change your price optimization strategy.
For example, a retailer may experiment with selling a product for 10% more in a specific market and monitor sales volume and revenue. When they compare it to a control group at the original price, they find sales declined only slightly—and the decline was offset by the higher price per transaction, making it a net positive. The company might consider rolling out the increased price across all markets.
6. Monitor market changes
Beyond the internal data from your price testing, stay abreast of external information like changes in market conditions, consumer trends, and competitors’ pricing. This external market data is often essential to a successful price optimization strategy—and to maintaining a competitive edge. Not every price strategy will work for your business at a specific time. Products, markets, and customer preferences evolve over time, so determining optimal price points is an ongoing process.
Price optimization examples
Here are a few examples of how well-known companies use price optimization solutions to maximize profitability:
Dynamic pricing: Uber’s surge pricing
The ridesharing app Uber popularized surge pricing, a form of dynamic pricing used to align supply with demand. Uber raises prices during high-demand periods (like right after a concert ends or a Saturday night in the city) to attract more drivers to areas with a shortage. This strategy ensures riders can find a car while boosting revenue for Uber and its drivers.
Loss-leader pricing: Costco’s $4.99 rotisserie chickens
Despite prolonged high inflation, the rotisserie chickens at Costco remain at $4.99. The company works hard to keep it that way, despite losing money on every chicken sold.
This is because the low-priced chickens get people into Costco’s massive warehouses. To get to their chicken, shoppers stroll past shelves of tens of thousands of items—then pass them again to get back to the checkout area. Costco’s bet is customers will grab additional items, netting them a total profit on the entire purchase.
Algorithmic pricing: Amazon
Amazon is the behemoth of retail companies in large part because of its algorithmic power. The company’s proprietary pricing algorithms optimize prices for millions of products on its platform, considering competitor pricing, historical sales data, customer behavior, weather, and current events. The company continuously analyzes this data, dynamically adjusting prices to stay competitive and maximize profits.
Price optimization software
Price optimization is important, but it may feel overwhelming for smaller businesses and entrepreneurs new to pricing decisions. This is where pricing optimization software can help.
Some price optimization tools focus on a particular pricing model, like AI dynamic pricing. Others are centered on a specific part of the product pricing process. For example:
- Shoplift facilitates A/B testing
- NA Bulk Price Editor lets companies change batches of prices at once and launch flash sales
- Discounty arranges tiered and bundle pricing
Price optimization FAQ
Is price optimization legal?
Yes, price optimization is a legal and common practice in which businesses adjust prices based on market conditions and customer behavior to maximize revenue or profit.
How do you optimize a pricing strategy?
Price optimization involves collecting data, understanding customer preferences and behavior, analyzing market trends, and using pricing models to determine the most effective strategy to maximize sales and profitability.
What is an example of price optimization?
One example of price optimization is Amazon’s algorithmic pricing model, which considers factors like customer search and buying behavior, competitors’ pricing, historical data, and external factors like weather and current events.
How do you calculate the optimal price?
Calculate optimal prices using price optimization models and algorithms that account for factors like production costs, competitor prices, and consumer demand.
