How Artificial Intelligence is Changing Amazon for Sellers Every year, the allure of freedom; a word rarely associated with working life, stirs millions of people into starting their own online businesses. For decades this modern-day-siren has ensnared countless victims, but perhaps the hidden dangers of e-commerce are no more…

Every year, the allure of freedom; a word rarely associated with working life, stirs millions of people into starting their own online businesses. For decades this modern-day-siren has ensnared countless victims, but perhaps the hidden dangers of e-commerce are no more…

We get it, the success stories are hard to ignore. After all, who wouldn't want to be their own boss; making millions on a beach somewhere exotic? So where do so many of those hopeful souls fall short?

In short, competition online is cutthroat. Being a new seller on Amazon's marketplace for example, means squaring off with not only the millions of other active independent sellers but also the e-commerce giant itself; which holds close to 40% of the market share.

To survive this cutthroat environment, new entrants must learn strategic pricing.

Why Pricing Matters on Amazon

For Amazon shoppers, price is the primary factor influencing their purchasing decisions, followed closely by fast shipping, positive past experiences with the seller, trust in the brand, and favourable customer reviews.

This means that in this hyper-competitive marketplace, where countless sellers offer similar products, even a slight difference in price can be the deciding factor for a potential sale.

How sellers price their items is not just about profit margins; it's a lever for visibility, sales volume and overall success.

The Pricing Challenge

Given the vastness of the Amazon marketplace, sellers can be forgiven for feeling a bit overwhelmed by the challenge of pricing their products competitively.

A challenge that is magnified by the scale and complexity of Amazon's algorithm, which considers several factors when determining product rankings and visibility. Appeasing the algorithm overload is vital to surviving in this landscape, but at what cost?

Ranking high on Amazon's marketplace, being visible to the majority of customers is key to driving sales volume, yes, but do not make the mistake of assuming that increased sales volume will lead to more business success. In an environment where price is the most meaningful lever one can pull, race to the bottom tactics become common practice to promote rankings. Without the right tools, enough time and prior knowledge of the landscape, new entrants can fall into the trap.

For too long sellers have been manually monitoring and adjusting prices, which can be time-consuming and prone to errors. It's a reactive approach that often leaves sellers lagging behind the market.

This is where an Amazon repricer can help.

What is an Amazon Repricer?

An Amazon repricer is a third-party software that connects to an Amazon store and automatically adjusts the price of a seller's listings in response to competitor price changes.

A traditional repricer connects through an Application Programming Interface (API) called MWS subscription. Through this API, Amazon sends two types of reports to the repricer:

• Inventory reports. These reports contain information about the seller's listings, including: status, quantity and fulfilment type, which the repricer uses to keep track of the seller's inventory.
• Competitor reports. These reports contain information about the top 20 sellers on a listing, including: their price, shipping price, fulfilment rating, handling time and who is currently winning the Buy Box, the prime real estate on Amazon where most sales happen.

The repricer uses the competitor reports to calculate a new price for the seller's listing based on the chosen repricing rule and the seller's minimum and maximum price settings. Then, the repricer sends the new price to Amazon using the same API.

How Does AI Boost Repricing Software's Capabilities?

Advanced Data Analysis and Prediction

AI-powered repricers, use advanced algorithms, including those based on game theory, to enhance their capabilities compared to traditional repricers.

Game theory studies how people or entities make decisions when the outcome depends on what others do. In the context of Amazon repricing, this theory helps AI algorithms understand the competitive landscape and predict how competitors might react to price changes.

Using the theory, AI will simulate different scenarios and consider various factors to make informed pricing decisions likely to lead to better outcomes for the seller.

For example, an AI repricer might determine the optimal price for sunscreen by considering competitor prices, sales history, inventory levels, and repricing behaviours. A traditional Amazon repricer often relies on simpler rule-based systems that may only consider a limited set of competitor data, such as the current lowest price.

Dynamic Pricing Strategies

The ability to analyse vast amounts of data enables AI repricers to implement sophisticated dynamic pricing strategies. Whereas traditional repricers typically adjust prices based on fixed rules or schedules, AI-powered repricers can continuously monitor market conditions and adapt prices instantly in response to relevant factors.

AI algorithms can also analyse historical sales data and market trends to forecast future demand and price sensitivity. This helps the repricer proactively adjust prices to maximise profits and avoid potential losses. Traditional repricers lack this predictive capability; instead, they rely on reactive price adjustments based on current market conditions.

Continuous Learning

AI-powered repricers continuously learn and adapt from the outcomes of their pricing decisions. They analyse the impact of each price change on sales, profits, and Buy Box share. Then, they use this feedback to refine their models and improve future decision-making.

On the other hand, traditional repricers cannot learn and adapt over time. They require manual adjustments to rules and settings as market conditions change.

How Can AI-enhanced Amazon Repricer Benefit Sellers?

One of the main benefits of AI Amazon repricer is that it can significantly lower the barriers to entry by automating complex tasks that previously required extensive experience and resources.

For smaller sellers, this has levelled the playing field, allowing them to compete with larger ones more effectively without needing deep technical knowledge. AI tools handle the heavy lifting, allowing new entrants to focus on sourcing products and growing their businesses.

Here's how that looks in business-to-consumer (B2C) and business-to-business (B2B) settings.

B2C

The primary challenge for B2C Amazon sellers is dealing with price wars and bad actors who aggressively lower prices. AI repricers can counter this. They can analyse the competitors' pricing patterns and strategically adjust prices to create a temporary price floor, discouraging further undercutting. AI repricers can stabilise the market and disincentivise the penny-droppers from continuing their aggressive tactics.

AI's advanced algorithms can also identify the optimal price points that balance competitiveness with profitability. They can adjust prices in real-time based on market conditions, ensuring that sellers always get the best possible price for their products.

With these intelligent pricing strategies, AI repricers can help sellers win the Buy Box more often, increasing visibility and sales volume.

B2B

For B2B Amazon sellers, AI repricers allow for efficient bulk pricing adjustments across multiple products and quantities, which is crucial for those who manage extensive inventories.

AI repricers also allow B2B sellers to create customised pricing strategies for different business goals and customer relationships, such as tiered pricing for different customer segments, volume-based discounts, or special pricing for long-term contracts.

Advanced analytics can provide B2B sellers with in-depth insights into pricing trends, competitor behaviour, and customer purchasing patterns. They can use this data to refine pricing strategies, identify new opportunities, and negotiate better deals with customers.

Future of AI in Amazon Repricing

The future of AI in Amazon repricing is promising. We can anticipate further advancements in predictive analytics, which will allow sellers to anticipate market trends better and adjust their strategies proactively.

Additionally, AI will become more integrated with other aspects of Amazon selling, such as inventory management and advertising, providing a more holistic approach to optimising performance.

Sellers should prepare for increasingly sophisticated AI tools that offer deeper insights and more automated decision-making capabilities.

Conclusion

In conclusion, the landscape of online business, particularly within e-commerce giants like Amazon, is undeniably competitive. The allure of entrepreneurship and the promise of autonomy will continue to draw countless individuals to start their own ventures.

Navigating this cutthroat environment requires more than just enthusiasm and a product; it demands strategic acumen, particularly in pricing. Luckily, new entrants now have an AI -powered tool to help reduce the barriers to entry for prospective sellers.