Why Your Million Dollar Amazon Business Needs Algorithmic Repricing

If you’re running a large Amazon business, you inevitably have more complex pricing needs than smaller retailers. For rapidly growing Amazon sellers, at some point it becomes clear that manually repricing each of your ASINs individually is simply unrealistic. The market is too dynamic, and your active inventory is too large. This is when many sellers decide to opt for a rule-based repricer.

Rule-based repricing: a race to the bottom

Most repricing software on the market are rule-based, which means they change your prices based on pre-set rules, such as undercutting the lowest competitor by a certain amount. This can be fine if your goal is simply to sell as much quantity of product as possible. Yet the downside is that it leads to a race to a bottom, both for the individual seller and the marketplace as a whole — leaving money on the table for everyone involved.

The algorithmic alternative

In contrast to rule-based repricing, algorithmic repricing manages to bypass these issues by dynamically analyzing and adjusting to big data, seller metrics, and marketplace trends to select the ideal price for an ASIN.

An independent study by Northeastern University revealed a direct correlation between algorithmic pricing, higher Buy Box share, and increased profitability.

Here are a few reasons why algorithmic repricing may be the right choice for your large-scale business:

It makes objective decisions

While rule-based repricers adjust your prices based on parameters set by humans, algorithmic repricers are driven by millions of complex data points. It would be impossible for an individual to determine what sort of rule should be applied and when, because each ASIN, each business, each category and each competitor is unique and dynamic. Algorithmic repricing is the only kind of repricing that is able to synthesize the complex combinations of factors to make truly unbiased decisions.

It takes into account all seller metrics

Algorithmic repricing won’t leave money on the table because it’s optimized to take into account all the variables Amazon uses to determine who wins the Buy Box. A seller with strong metrics can very often price higher than a lower-performing competitor and still win the Buy Box. Algorithmic repricing gives the right amount of weight to the various metrics Amazon cares about, allowing strong businesses with great customer service to price upwards and make more profits.

It adapts to changing market conditions

It’s true — many repricers on the market change prices at regular intervals. But only algorithmic repricing continuously learns and adjusts not only to competitors’ prices, but to dynamic market conditions such as stock levels, competitive landscape, and seller performance. This means more accurate pricing based on a multitude of variables.

It saves you time

While rule-based repricing takes a long time to set up and tweak, algorithmic repricing is designed to save you time by translating big data into actionable insights. This allows you to focus your time and efforts on making high-level business decisions such as product sourcing and scaling upwards.

Conclusion

Rule-based repricing may suffice for sellers who are just starting out, but a growing business needs a software that can handle its increasingly complex needs. Algorithmic repricing keeps profit margins high without compromising sales by taking a bird’s eye view of the Amazon Marketplace.

This article was contributed by Feedvisor, the pioneer of Algo-Commerce – the discipline of using big data and machine- learning algorithms to make business-critical decisions for online retailers.

Feedvisor’s algorithmic repricing and revenue intelligence solutions power millions of pricing decisions daily, providing online retailers with actionable insights to maximize profitability and drive their business growth.

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Leor Farkas is a writer for Feedvisor. She’s a native New Yorker who lives in Tel Aviv, where she has been producing content for the hi-tech world. She earned her B.A. in English and philosophy from Sarah Lawrence College.