In the highly competitive retail industry, keeping your product’s price stagnant is not wise. While it is important to reprice in a timely manner, doing so without reference to competitive pricing or an 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’ prices, 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.
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 requires an analysis of internal and external data to create and optimize a mathematical pricing model. Internal data provides supportive information while external data removes blind spots in your pricing algorithm to allow you to set competitive prices and exploit opportunities.
Algorithmic price changes based on various data makes you price competitive over other sellers. Likewise, the process ensures that you are not under priced or over-priced against your key competitors.
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 retail price optimization is to optimize prices at the right time to maximize revenue. The process helps retailers utilize dynamic pricing to analyze where they fall within their competitive landscape. They increase prices to maximize profit when possible, all the 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 Solution. The retailer accepted our price change recommendations we made 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 a suitable strategy for this category of products, and b) incorporating it into the price optimization mathematical model is crucial.
You must optimize your product price over time. Correlating your price with sales data shows price elasticity and suggests the price point that will yield the highest sales revenue for the product.
For example, imagine you have 50 customers buying your product. You will 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.
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 using Product Pricing Analysis, and correlating the numbers with the latest market data, you can build an intelligent dynamic pricing algorithm.
If you want to learn more about how GrowByData’s Competitor Price Intelligence supercharges your capabilities in this area, please feel free to reach out. Happy to talk.