In the highly competitive retail industry, keeping the price of your product stagnant is not wise. Not only it impacts your sales, but it also limits your pricing wisdom and eventually throws you out of the market. While it is important to make price changes timely, changing prices without any reference and price strategies are destructive. It is imperative to analyze internal data like marketing/promotion data, customer/sales data and inventory data along with external data like availability and prices of the products and its close substitutes, market competitors, demand of the product, customer feedback data like rating and review of products for the price optimization process. Thus, collecting and leveraging all these data to re-price your product timely is called Price Optimization.
It is important to make price changes timely, changing prices without any reference and price strategies are destructive.
Price Optimization is the continuous process of pricing the product right, at the right time considering the customer’s willingness to pay along with the goal to maximize overall sales. The heart of any price optimization process is internal and external data collection and analysis to develop and optimize the mathematical pricing model. The internal data provides supportive information while external data remove blind spots in your pricing algorithm to exploit the opportunity and tackle threats seen to ensure good ROI consistently.
Therefore, let’s take a look at the benefits of price optimization.
Ensures price competitiveness with the advance pricing model
The algorithmic price changes based on various data will make your price competitive over other sellers. Likewise, the process will also ensure that you are not underpriced or over-priced against your key competitors besides making your price tracking difficult and costly as a process to your competitors.
Boost sales to maximize revenue
Sales, for the updated prices, increased by an average of 36% within the first 10 days after each reset.
The main purpose of price optimization in retail is to optimize price at the right time to maximize revenue. It helps retailers understand the dynamics in market pricing and analyze where they fall within their competitive landscape. Meaning increasing prices to maximize profit where possible. And reducing prices to boost revenue and sales volume.
As 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 acknowledged 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 the next update, revenue decreased steadily from day 10 to day 30.
This analysis of pricing shows the competition based pricing is the best strategy for this category of products and incorporating it in price optimization mathematical models is crucial.
Identify the price elasticity of a product
You have to optimize your product price over time. Analyzing your price data correlating with the sales data will clearly show the price elasticity suggesting price point acquiring the highest sales revenue from any product in the given time frame.
For Example – 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 being priced at $8 and $13.5. To maximize revenue, you should set your product’s price at $18.90.
We should consider historical, internal and external data and correlate them with the latest market data to build an intelligent dynamic pricing algorithm.
Lays ground for AI/ML-based Dynamic Pricing
The price optimization model eventually provides data, knowledge, and wisdom to build a self-learning mathematical model powered by AI/ML. Over time the price optimization process gives you information on which price point has generated the highest sales revenue during a specific time period. Considering all those historical internal and external data and correlating the latest market data will help you build an intelligent dynamic pricing algorithm.
To conclude, if you choose the path of price optimization, Success is just around the corner.