Repricing has changed dramatically over the last few years. With that change comes a change to your Amazon repricing strategies as well.
As repricing tools begin adopting cutting edge technologies like Artificial Intelligence, Machine Learning, and Automation, they can become very complex. Although, the best Amazon seller software tools bridge the gap between complexity and ease-of-use for sellers.
What Will Change for Amazon Repricing Strategies Going into 2019
Advanced and Dynamic Amazon Repricing Strategies
Entering your Minimum and Maximum prices can be tedious and down-right overwhelming, especially when you have thousands of active listings to reprice.
Aura is pioneering a movement away from this as a whole and focusing more heavily on what we call Automatic pricing models - automatically setting your Min/Max’s based on ROI, Profit Margin or Profit.
Using automatic pricing models further reduce the steps required from a user to begin repricing their large volume of inventory. All we need is your costs.
Because you can manage a handful of similar repricing strategies, this doesn’t restrict users into generalizing their inventory but actually gives them more control and flexibility when it comes to setting up their strategies.
Most tools lean heavily on a users strategy and basic rules. Although a users strategy is essential to repricing success there’s a lot that can be done from the tools perspective.
An advanced repricing tool needs to be smarter than the user. An example of this is Aura realizing a listing has became suppressed. Rather than relying on the strategy, Aura knows exactly how to de-suppress that listing by looking at the data.
This protects users with little input from them.
When you begin mixing advanced features like the example above with dynamic pricing models and automation, the outcome is an easy to use tool that does more while requiring less from the user.
In our opinion, this is the future of repricing.
Set It and Forget It Is Becoming More Common
Taking this idea a step further, AI and Machine Learning can be applied to reduce the user's input. Because repricing tools still rely on the users created repricing strategy, there is room for error.
Advanced tools like Aura aim to reduce the error as much as possible. This can be done by using data to develop actual algorithms and models so that each listing is treated differently than another.
This level of complexity could never be done by a human or even a repricing strategy created by a human.
Instead, Aura is able to use data to develop a unique repricing strategy/model for each listing, taking in every variable that matters.
As an example, we can develop a profile for each seller you compete with so if we see them jump onto another listing you sell on, we know how to best reprice against that seller specifically.
Further reducing the required steps for a user means a more hands-off approach to repricing that simply cannot be replicated by hand.
This will be the difference between repricing companies that have more sales staff than engineers and those - like Aura - who prioritize engineers and data scientists.