Why A/B test your pricing is essential on Amazon?
" Based on our observations, more than 50% of third-party Amazon private label products are priced below their optimal levels. What's more, sellers often take too much time manually setting prices. Priceloop's goal is to simplify the whole process and revolutionize the pricing industry." — Dr. Richard Schwenke, CEO of Priceloop and former Co-Founder of Contorion
Through numerous customer interactions and collaborations, we have gathered insights regarding the two primary pricing challenges Amazon sellers face:
- Insufficient knowledge or expertise
- Lack of time
The majority of Amazon sellers dedicate their time to product sourcing, optimizing listings, and managing advertising strategies, including PPC bids. Regarding pricing, sellers usually engage in manual assessment of factors such as cost of goods sold (COGS), FBA fees, and occasionally advertising expenses. They then incorporate a profit margin and compare it with competitors' prices to determine the final pricing for the product.
However, this approach has its drawbacks and suboptimal aspects.
Lessons learned from Amazon 1P & 3P sellers
It is essential to avoid setting a price once and sticking with it indefinitely. Amazon understands this concept well. By comparing a typical 1P seller (where Amazon determines the product price) with a typical 3P seller (where the seller has pricing control), we can observe intriguing distinctions.
Amazon tests different pricing strategies for 1P sellers
By the fluctuating purple line depicted in the chart below, we see how frequently Amazon triggers price experimentation for 1P sellers. Each point on the purple line represents a different price that has been set. This iterative testing allows Amazon to assess customers' willingness to pay the products and optimize profit and/or revenue/sales accordingly
3P sellers often stick with the original prices on Amazon
In contrast, 3P sellers, who have autonomy over their pricing decisions, tend to adopt a distinct approach. As illustrated in the chart below, they typically calculate a price and maintain it over an extended duration, resulting in a flat line over time.
Higher price does no equal to lower sales
Traditionally, Amazon sellers tend to be apprehensive about raising prices, fearing that it would drive customers to competitors and result in reduced sales. These concerns often stem from gut instincts. While it is generally true that higher prices correlate with lower sales, there are instances where a slight price increase (<15%) has only a minimal or negligible impact on sales. One possible explanation for this phenomenon is the perception of higher product quality.
Let's examine the following real data as an illustrative example:
- In the first example, we observe the typical relationship between demand and price. As prices increase (x-axis), sales decrease (y-axis).
- In the second example, we encounter an atypical demand/price pattern, where sales actually rise alongside higher prices.
These insights are valuable to acquire! In this scenario, a higher price yields greater profitability, enables increased unit sales, and consequently generates higher revenue—a situation that benefits all parties involved.
Why A/B test your pricing on Amazon?
Improve your sales and profit
Adopting a data-driven approach to test various price points enables you to gain insights into customer responses to different prices in various period of time. This empowers you to optimize your products in terms of profitability and/or sales.
Optimize your Amazon save badge
In general, Amazon automatically applies the save badge banner when you sell below the "Was Price." The "Was Price" refers to the median sale price customers have paid for your product on Amazon, excluding promotional prices. By experimenting various pricing strategies and aligning your prices with this "Was Price" logic, you can potentially obtain Save Badge visibility for approximately 50% of sales for that specific product.
How Priceloop APE empowers you?
Our Amazon Pricing Engine (APE) leverages the latest machine learning technology to autonomously analyze various price options for your products. APE determines the optimal price, considering factors such as revenue, profit, and sales. Moreover, it efficiently maximizes the visibility of Save Badges for your products, eliminating the need for manual intervention.
How does it work?
1. Data extraction
APE streamlines the process by extracting the necessary data from the Seller Central account through the Amazon API. The seller only needs to provide information such as the minimum margin, maximum price, COGS, and TACoSs once.
2. Machine learning
By applying an advanced machine learning algorithm, APE identifies the optimal candidates/ASINs for price testing and exploration.
3. Continuous A/B testing
APE automatically conducts price testing on the selected candidates/ASINs, exploring different price points.
4. Optimal price update
Following the testing phase, APE autonomously determines the optimal price based on revenue, profit, and/or sales targets. It then integrates these optimized prices into your Amazon account using the API.
5. Save badge optimization
Additionally, APE maximizes the visibility of your product's save badge through an automated optimization process.
What you can expect from using APE?
The results achieved with APE have been remarkable for our clients, and they manifest in the following three key performance indicators (KPIs):
- A profit uplift ranging from 10% to 15% (on PC3 level).
- A sales uplift ranging from 20% to 30%.
- Elimination of manual effort for the seller, resulting in a 0% manual workload.
Request a demo and earn your free trial
If you are interested in experiencing a demonstration of APE, feel free to contact us, and we will gladly provide you with a comprehensive showcase of its functionalities.