Pricing for Amazon Sellers
How to find the optimal price for your products with predictive pricing
Table of Contents
Amazon is the world’s largest online retailer with sales totaling 110.714 million Euros. It built this success through many years of offering a huge selection of products at competitive prices, matched by outstanding customer service (e.g. same-day delivery, free shipping, etc.). This helped the e-commerce giant establish itself as the first port of call for consumers looking to shop online. Amazon boasts more than 300 million active customer accounts, and 66% of all online buyers begin their shopping journey on the platform, if only to start their product research.
This market power is the reason for Amazon’s overwhelming appeal to retailers: Selling their products through the platform as third-party vendors helps them achieve a level of visibility for their products that is matched by very few other e-commerce platforms. At the same time, they get to draw on the trust that consumers place in established retailers like Amazon, providing ideal conditions for rapid growth and record sales.
According to McKinsey, online marketplaces such as Amazon are expected to account for 50 to 60 percent of total sales growth in the retail sector by 2025.
However, selling on Amazon doesn’t guarantee success for retailers: to fully leverage the platform, they need to understand its unique pricing terms and adjust their pricing strategies accordingly. If they fail to do so, their products are likely to flop.
On the following pages, we’ve summarized the unique characteristics of Amazon’s pricing ecosystem and provide insights and tools for sellers on how they can optimize their pricing strategies and multiply their success through artificial intelligence.
Amazon's Pricing Model
Amazon’s commercial success is owed in large part to its dynamic pricing model. The e-commerce giant adjusts its prices as much as 2.5 million times per day (!), which is unheard of in traditional retail. By moving away from traditional pricing models that include the occasional day-long or week-long discount, Amazon managed to grow its profit by about 25 percent.
While some consumers were – and remain – upset about the platform’s price volatility, the majority accepts the fluctuating prices, which do have their own benefits as they provide frequent opportunities for unique bargains. In general, Amazon is perceived to offer more-than-competitive prices. These low prices – which are key for Amazon’s high level of customer loyalty and which also benefit the thirty-party sellers on the platform – are a result of dynamic pricing.
For third-party sellers, however, it is critical to have the same level of insight on price elasticity and consumers’ readiness to buy as Amazon. As undercutting competitors at any cost is not always the best solution, Amazon sellers should strive to achieve high rankings for relevant search terms on the platform, and they must understand that low prices are just one factor of several to achieve a listing on the first page. These include aspects such as positive reviews and customer service.
While Amazon ads allow sellers to buy visibility on the platform, they are not a substitute for smart pricing strategies. They can be used to support growth, but this requires marketing spend and pricing decisions to be synchronized. Otherwise, perceived successes, such as increased sales, may be eroded by high ad spend.
All retailers, from private brands to resellers, must define appropriate price points for their products. To do this, they can harness a range of different strategies, which should be selected according to the seller’s business situation, as well as their product and business goals.
Calculating Overhead Cost
In its simplest form, pricing is based on the costs incurred to produce and/or distribute a given product. This includes all direct costs as well as overhead costs, which cannot be directly allocated to any specific product. This includes inventory and payroll costs, etc. Sellers can determine their sale price by adding the desired profit margin to their overhead.
While this may sound fairly straightforward, it’s rarely as simple as it seems. In reality, it can actually be quite challenging to determine the exact cost of a product, particularly when it comes to marketing costs.
Penetration pricing can be a perfect strategy for sellers or brands that are new to the market and want to spark interest around their products. With this strategy, retailers will first lower their prices, often accepting cuts to their profit margins. However, once they have an established customer base, they start raising their prices.
On Amazon, low entry prices can help boost rankings in the platform’s search results. This drives sales and can help generate large numbers of positive customer reviews within a short amount of time, which in turn may later encourage prospective customers to buy from a given seller despite slightly higher prices.
Contrary to popular belief, it is not always necessary and wise for Amazon sellers to offer the lowest price possible. In fact, if you are the only vendor offering a given product, you get to benefit from greater pricing flexibility than vendors of commodity products, as those face high competition. If you offer a high-demand product and want to establish your business as a quality brand, charging higher prices can help bolster customer confidence in your product.
Key questions to help define product prices:
- Do you offer a commodity product or a private label product?
- Are you an established player in the marketplace?
- What are your key priorities: driving sales, improving margins, etc.?
- Are you planning to expand your product offering?
- What brand image are you looking to convey through your pricing strategy?
- Is low inventory cost a significant factor in your pricing strategy?
Amazon Pricing Policies
Amazon sellers enjoy great freedom when it comes to pricing. However, Amazon does provide some pricing guidelines. Sellers can view these in their Amazon Seller Central accounts and must comply with them when selling on the platform. Here is a summary of the most important rules:
- The price that a seller charges for single items or multipack products must not exceed the price they charge on other websites or online marketplaces.
- The price for any product sold as part of a multipack must not exceed the price charged for that same product when sold as a single item.
- Amazon Sellers must not provide misleading reference prices. Sellers must keep their reference prices up to date; for an advertised reference price to be valid, the seller must have previously sold significant quantities of the respective product at the advertised reference price. Otherwise, Amazon reserves the right to remove it from the product page.
- Excessive shipping costs are prohibited: to assess this, Amazon considers public carrier rates as well as buyers’ opinions, and the reasonableness of handling charges.
- Depending on their respective Amazon plan, sellers must adhere to different price limits: Individual sellers may charge a maximum of USD 10,000 per item, while the price limit for professional sellers is USD 300,000 per item. However, exceptions apply (e.g. for collector’s items).
Optimized Pricing on Amazon Accomplishes Two Goals
On Amazon, sellers need to beat both their competitors and the platform’s algorithm.
Similar to Google, sales on Amazon tend to focus on the first two pages of search results. Besides optimized keywords and product page design, conversion rate is one of the most important ranking factors for search results on Amazon. Pricing is the key parameter that influences conversion rate.
For commodity products, high conversion rates can be achieved by winning the Amazon Buy Box. For these items, Amazon automatically selects a default vendor for the Buy Box button. Although it takes just a few clicks to access a list of other sellers, 80 to 90% of customers purchase from the default vendor selected by Amazon. While Amazon won’t disclose which criteria sellers need to fulfill for winning the Buy Box, pricing seems to be a key factor.
The Best Approach to Price Optimization for Amazon Sellers
Amazon is an incredibly dynamic marketplace – new sellers join the platform daily, and Amazon steadily expands its own product offering, updating prices by the minute. For sellers to succeed, their prices must reflect this dynamic environment, regardless of their initial pricing.
What is Dynamic Pricing?
Dynamic pricing is a method where retailers continuously and (semi-) automatically adjust the prices of their products to match market demand in order to increase sales opportunities and optimize profits. Instead of fixed prices, they offer variable prices.
While price optimization has long been common practice in retail, methods have changed dramatically in recent years due to technological advancements.
Rules-based Pricing – How do Most Amazon Repricers Work?
For decades, rules-based pricing used to be state-of-the-art in retail and online commerce. To this day, many Amazon repricers use this approach, where price changes are tied to variable factors and pricing follows fixed rules that are based on the principle “If X occurs, change price by Y%”. Initially, retailers had their employees monitor these factors at fixed intervals and adjust prices manually, but today there are digital tools such as Amazon repricers; these support rules-based pricing by monitoring parameters and adjusting prices automatically.
Users can view and enable or disable the available pricing rules in their tool’s settings. While we cannot provide an exhaustive list of all possible pricing rules in this White Paper, the rule types presented here illustrate the principles they’re based on.
Pricing for Commodity Sellers
The simplest method in dynamic pricing is to focus on one’s own margin. Sellers specify a range they’re willing to accept as their profit margin. If their products don’t sell at the maximum price (i.e. obtaining a maximum margin), the repricer will gradually lower the price within the defined range until sales start to increase.
Given Amazon’s fierce pricing environment, it makes good sense to have a look at competitors’ prices when selling commodity products. By linking their prices to those of their competitors, sellers can match competitors’ price cuts and ensure they don’t lose a disproportionate number of customers to the competition. Sellers can also employ monitoring to implement strategic price cuts – this is done by including competitors’ price increases in the list of parameters to be monitored.
The software continuously monitors the marketplace and adjusts prices several times a day, or even hourly, according to specified rules such as: Continuously underbid the prices of competitors X and Y for product A by 5%. To achieve the greatest number of benefits, sellers can apply multiple different pricing rules in conjunction with real-time response to market dynamics.
Pricing for Brands
Sellers offering less comparable or more exclusive products shouldn’t exclusively rely on benchmarking against the competition, as this may unnecessarily limit their sales potential and undermine their positioning as a quality and premium brand. Sellers in this sector should employ different pricing strategies, and also build on their own experience as they go.
Sellers looking to harness demand trends can specify a time period and a maximum deviation rate from their average sales; this will automatically trigger automatic price increases or cuts at a pre-defined percentage. For example: Products sold in the past 14 days: >20 above average 🡪 increase price by 10%
This pricing approach is an option for sellers who control their own logistics operations and don’t rely on Amazon Warehouses. Here, the software monitors inventory levels to detect any decrease in demand, and to avoid excessive inventory. When adding new products, the software can be configured to implement price cuts to help increase sales and free up warehouse space.
Repricing tools and a dynamic rules-based approach can yield much larger revenue growth than pricing that is based on empirical values and intuition. Thanks to automation, digital price monitoring permits much faster response times than manual market monitoring. Also, given the effort and complexity involved, it would be impossible to combine multiple different price rules without the help of digital tools. Nevertheless, this approach is not optimal and – given the technological means available – simply outdated.
What are the Disadvantages of Rules-Based Pricing?
These are the key drawbacks of rules-based pricing:
- These applications cannot learn from the results achieved through the specified pricing rules. They execute pricing rules even if these turn out to actually decrease sales.
- Rules-based systems can only incorporate a limited number of parameters, and they can quickly become complex and confusing, prohibiting effective management.
For example, if a business distributing barbecues decides to increase prices by 10% each year in April (as April usually marks the start of the outdoor grilling season), the pricing application will continuously execute this rule. However, if there happens to be a regular off-season peak in demand in November, the software will not show that. To identify this sales opportunity, the business will instead need to manually evaluate its own analyses and adapt its existing pricing rule accordingly.
As a general rule, traditional dynamic repricers do not respond when existing rules become irrelevant or financially harmful due to changes in external factors.
What if there is an increase in demand because a competitor is having delivery issues? This requires manual intervention. Your flagship product got praised by an influencer? It might be wise to promptly adjust the price of your product. Rules-based systems don’t respond to such changes at all, or respond too late. Another downside of rules-based pricing is the fact that, rather than gauging customers’ willingness to pay, it uses past market data or competitor behavior as a snapshot benchmark. This competition-focused approach harbors the risk of causing downward-spiraling prices and a race to the bottom among competitors.
Leveraging Machine Learning and AI for Dynamic Price Optimization
What influences a customer’s propensity to buy, and how important are these factors? With rules-based pricing tools, Amazon sellers don’t get any insight on effective drivers of demand, or on the price elasticity of their products.
With rules-based pricing, sellers run the risk of optimizing for sales levels that are actually far below their potential. For example, if they’re based in a country where higher prices are seen as a sign of quality, they may miss out on revenue by competing with cheaper businesses from other regions.
Truly optimal pricing can only be achieved by calculating price elasticity based on all internal and external factors.
What is Price Elasticity?
Price elasticity is a measure of the change in demand that follows a price adjustment. In general, higher prices will lead to a decrease in demand, and lower prices will lead to an increase in demand. The higher the level of price elasticity, the greater the impact of price adjustments on demand.
State-of-the-art pricing solutions utilize artificial intelligence (AI) or machine learning algorithms to filter databases for all factors that are relevant to the price elasticity of a given product, and to automatically optimize these to achieve the sales or profit targets defined by the seller.
The larger and more comprehensive the databases that the software evaluates, the better the price optimization achieved by the machine learning (ML) algorithms. Two crucial factors for this are a) the available pool of data on individual metrics, and b) the number of metrics that are reflected in the data. However, very few companies have an actual lack of data. Product data and sales as well as inventory are tracked everywhere today, but to date haven’t been leveraged optimally. Information such as regional weather conditions, current events, and supplier details could easily be integrated via third-party tools.
Once the data sources are aggregated in the tool, the machine learning algorithms can learn to understand how price adjustments affect customer behavior. They can also learn to identify the factors that have a significant impact and those that do not. Users are always in control: They can customize the parameters that are to be considered by the AI, such as maximum and minimum prices.
Whereas previous analytics tools were only capable of extrapolating past trends into the future, AI-based pricing solutions predict future price-demand curves for products much more accurately, as they draw on both historical and current data. These applications employ ML prediction algorithms to visualize demand scenarios for different price points, providing pricing managers with a nearly instant forecast of how strategic decisions will impact revenue, profit, and sales.
However, the biggest value proposition of smart pricing solutions is that users no longer need to set pricing rules themselves. Instead, the software asks them to simply specify their business goals. The AI algorithms then translate these goals into a variety of different actions that will help accomplish them as fast as possible.
The Achilles' Heel of Marketing: How Ad Campaigns Sabotage Pricing Success
In the past, most companies approached marketing and pricing decisions separately, despite there being a clear overlap between both areas. And, while retailers were aware of this overlap, they lacked the appropriate tools to leverage its potential. As a consequence, consultations between marketing and pricing teams remained superficial or did not take place at all, resulting in detrimental impacts on the balance sheet.
For example, when marketing managers ran discount promotions on certain products, product prices would sometimes be low at the time of the campaign (due to the fact that they were aligned with the general market), and coincide with high coupon usage. The unfortunate result? The campaign didn’t pay for itself. At first glance, sales were up, but profit margins ended up cannibalized by marketing spend.
The same applied to Search Engine Advertising (SEA). Sometimes, ads would attract a lot of traffic to the landing page, yet conversion remained low because dynamic optimization had led to relatively high product prices. In the end, the advertising cost ate up the profit that had been achieved through the product’s higher price.
However, companies risked running into trouble even if their ad campaigns converted well and prices included a sufficient profit margin. If, for example, the purchasing team failed to consider an upcoming marketing campaign in its planning, the company ran a risk of delivery bottlenecks that might in turn lead to negative customer reviews.
In a best-case scenario, companies would at least gather enough experience over time, allowing them to gauge the potential impact of discounts on customers’ propensity to buy a certain product category. In a worst-case scenario, however, pricing and marketing ended up sabotaging each other.
The Next Stage of Evolution: Smart Synchronization of Marketing and Pricing Decisions
With artificial intelligence, digital technologies have reached a level of maturity that allows us to automate complex tasks such as synchronizing marketing and pricing activities.
In the past, even sophisticated marketing tools applied a two-dimensional approach to pricing: they would track marketing costs such as discount codes or ad campaigns as variable X, and then analyze their impact on sales. However, they often ignored the interplay between marketing activities and pricing, as well as the impact of price changes on conversion rates.
On the purchasing side, pricing managers were also using their own tools to simulate the impact of pricing decisions on margins, but once again, the two dimensions remained separate.
The machine learning algorithms employed in today’s latest pricing tools are capable of processing a wide variety of input data. Our pricing experts thus realized that there had to be a way to add marketing spend (cost per click) to the usual factors considered for price optimization (e.g. inventory levels and discounts, etc.). Developing the AI algorithms to achieve this was going to be a challenging task.
However, our developers succeeded in extending the two-dimensional approach of traditional algorithms to a three-dimensional approach by leveraging artificial intelligence. Users can now see which price points and marketing interventions will maximize their profits and sales, all at a single click. This makes 7Learnings the first-ever solution to optimize profit through a holistic approach that synchronizes marketing management and pricing.
The result clearly outperforms the optimization results achieved by advanced AI pricing tools: Our customers see profit growth of up to 15 percent within a few weeks of implementing the platform.
7Learnings for Amazon Sellers
Amazon sellers are under particular pressure to optimize their prices. Optimized pricing ensures visibility and is key for sales success on the platform.
At the same time, many Amazon sellers lack the budget that would be required to develop a custom pricing tool. Therefore, most sellers rely either on manual pricing strategies or on basic, rules-based repricing tools. Neither of these are optimal solutions.
7Learnings makes predictive pricing accessible for Amazon sellers. Our AI algorithm tracks and analyzes all internal and external data relevant to Amazon selling, helping to identify current drivers of demand and price elasticity across your entire product portfolio. The application automatically optimizes both sales and revenue for each product by synchronizing marketing and pricing decisions to help Amazon sellers achieve their overriding business goals quicker.
Whilst the technology behind our platform is very sophisticated, it’s incredibly user-friendly – our turnkey solution doesn’t require any programming skills, and our structured onboarding process allows new users to get started on their Amazon success story by simply logging into their 7Learnings account.
What is the Onboarding Process at 7Learnings?
- Live demo: Book a free discovery session to get a first impression of our pricing platform. This will provide you with an overview of usability, functionality, and technical requirements, and you will have sufficient time to get answers on any questions you may have. We will also assess whether the application is the right fit for your requirements and goals.
- Data processing and configuration: Following your decision to work with 7Learnings, our onboarding managers will guide you through the configuration and integration stages and help you link the platform to your relevant data sources. If necessary, we will take care of the processing step to ensure that our algorithm can read the data correctly.
- Testing and training: Once our engineering team has completed testing, we will organize an onboarding session to help your teams get the most out of 7Learnings for marketing and price optimization. We will remain available for further questions and development requests after the rollout is complete.
Achieving success on Amazon is no easy feat. There is very little transparency around many of the factors that drive success on the platform. However, pricing is a proven key driver for sales success. Beat the algorithm by leveraging smart price optimization.
7Learnings supports retailers with a predictive pricing platform that helps them optimize their marketing and pricing decisions through a holistic approach. Our AI algorithms optimize prices automatically and dynamically, helping customers align their pricing with their business goals and maximize their business success.
Want to learn more?
Book your free discovery call here.