In the highly competitive retail industry, keeping your product price stagnant is not wise. While it is important to reprice in a timely manner, doing so without a reference or underlying pricing strategy can be destructive. To develop a solid repricing strategy, it is imperative to analyze internal data like marketing, promotions, sales and inventory numbers and external data like availability and prices of close substitutes, market competitors, demand, as well as customer feedback data like ratings and reviews. This process of collecting and leveraging datasets for a timely re-pricing strategy is known as Price Optimization.
Why a Dynamic Pricing Strategy Is Crucial
Price Optimization helps you continually price your products competitively at the right time while considering the customer’s willingness to pay, with the end goal of maximizing sales. At the heart of any price, the optimization process is an analysis of internal and external data to create and optimize a mathematical pricing model. Internal data provides supportive information while external data remove blind spots in your pricing algorithm to allow you to exploit opportunities.
Price Optimization & Its Importance
Here are four reasons why price optimization is important:
1. Ensure price competitiveness.
Algorithmic price changes based on various data make your price competitive over other sellers. Likewise, the process ensures that you are not under priced or over-priced against your key competitors.
2. Boost sales.
Our data indicate that sales at the updated prices increased by an average of 36% within the first 10 days after each price reset.
The main purpose of price optimization in retail is to optimize prices at the right time for maximizing revenue. The process helps retailers understand the dynamic pricing, analyze where they fall within their competitive landscape and increase prices to maximize profit where possible all while reducing prices to boost revenue and sales volume.
Here’s a real-life example: After experiencing intense competition and high price volatility in the market, a leading retailer in Sporting Goods approached us for Price Intelligence Solutions.
The retailer accepted our price change recommendation of once every 30 days (or monthly). Sales for the updated prices increased by an average of 36% within the first 10 days after each reset. Nonetheless, before each following update, revenue decreased steadily from day 10 to day 30.
This analysis demonstrated that a) competition-based pricing is the best strategy for this category of products, and b) incorporating it into the price optimization mathematical model is crucial.
3. Identify your product’s price elasticity
You must optimize your product price over time. Correlating your price with sales data shows price elasticity, thereby suggesting the price point that will yield the highest sales revenue for the product.
For example, imagine you have 50 customers buying your product. You find that your revenue fluctuates at different prices as shown in the hypothetical table below.
This suggests that the product priced $18.90 generates the highest revenue despite lower conversion than the product priced at $8 and $13.5. To maximize revenue, you should set your price at $18.90.
We should consider historical, internal and external data and correlate that with the latest market data to build an intelligent dynamic pricing algorithm.
4. Lay the foundation for AI/ML-driven Dynamic Pricing
The process eventually provides data to build a self-learning mathematical model powered by AI/ML. Over time, the price optimization process gives you information on the price point that generates the highest sales revenue during a specific period. By considering the historical internal and external data, then correlating those numbers with the latest market data, you can build an intelligent dynamic pricing algorithm.