machine learning for amazon sellers

Get Ahead: Machine Learning for Amazon Sellers

keith villano Inventory, Product Listing Optimization, Profit Peak, Sales Tactics Leave a Comment

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As FBA sellers, we’re always looking for new ways to boost our revenue and increase overall efficiency. We want to grow our businesses but not at the expense of sacrificing time to constant monitoring and adjustments. To achieve this seemingly impossible feat, machine learning for Amazon sellers offers promising solutions.

Instead of relying solely on your own brainpower to run your business, wouldn’t it be great to have some artificial intelligence working on your side too?

You could plug in piles of data about your customer behavior, inventory, orders, reviews and sales volume. It would start to notice patterns, trends and suggest changes that could maximize your business.

Machine learning for Amazon sellers also leads into predictive analytics. Hard data and customer behavior can be taken into account to predict future needs. This offers us an opportunity to make performance-enhancing changes and stay ahead of the curve, all without exhausting yourself.

Now, let’s take a quick step back before blowing your mind!

We’re going to cover the basic principles of machine learning for Amazon sellers, how it’s being used currently and how you can use it to grow your FBA business.

 

What is Machine Learning In Simple Terms?

Fortunately, you don’t need to be a software developer or genius to understand the concept of machine learning.

Machine learning is the application of self-learning algorithms to analyze data and provide actionable insights.

What’s so groundbreaking is the fact it does not need to use rules-based programming. That is, it can find new patterns in places it wasn’t even programmed to look!

 

Why Machine Learning is Gaining Interest

Industries across the globe, not limited to e-commerce, are now gathering an insanely large amount of data. Computer processing is becoming more powerful and cheaper, plus storage is becoming more efficient and affordable. Companies continue to mine more data in an effort to learn about their customers and market.

Gathering and storing data isn’t the problem, but processing it to provide valuable business insights is difficult. With machine learning, data feeds the algorithms which then identifies trends and patterns. In fact, the more data you can provide them with, the better it gets!

Machine learning can analyze highly complex data and deliver fast actionable results. Furthermore, high-value decisions can be performed in real time without any human interaction.

As data volume and variety grows, machine learning quickly adapts to make use of it through automated model building.

All this and more is achieved using a variety of different methods, which we’ll touch on below.

 

How Machine Learning Works – Common Techniques Used Today

There’s a variety of different ways these machine learning systems can be run, depending on their application. Here’s a brief rundown of the most relevant techniques being used today:

  • Regression is used to predict numbers and for advanced pricing calculations. In fact, regression algorithms are what powers our intelligent pricing tool Profit Peak.
  • Classification is a “supervised” machine learning technique that identifies members of known classes based upon characteristics. As an example, once the system knows how to identify a plant, it will begin to identify other plants too. It can also be used to identify high-value customers or fraudsters.
  • Clustering is an “unsupervised” machine learning technique that identifies similarities between groups or items and infers a class. These systems can recognize certain characteristics and cluster groups together without being told what they are. Most often used for customer segmentation.
  • Anomaly Detection looks for differences instead of similarities. It can make fast work of spotting unusual activity in a mountain of data. Most often used in fraud detection.
  • Association Rules is a technique for highlighting like-minded people. Often it’s used for making offers and suggestions like “you may also be interested in…” for e-commerce. It can begin to make connections and establish rules such as people who buy kitchen knives also tend to buy other kitchen utensils. When customers begin to fill their baskets, their online shopping cart can begin to offer the most relevant additional products.
  • Time Series Analysis is a method used to analyze data over time. It’s used for forecasting and predicting data based on past data.
  • Artificial Neural Networks use a concept based on the neutron network in the human brain. It enables machine learning to solve problems and learn in a similar process to humans. Large networks can be created and layered to tackle complex problems. It can be applied to speech, object and image recognition, language and emotion.

Are you feeling amazed or a little scared at the capabilities of machine learning? Well, it’s very likely your daily actions are already being measured and taken into account by machine learning processes.

Machine learning is increasingly being implemented behind the scenes for a range of applications. Now let’s look at these real-world applications:

From fraud detection to customer segmentation, it’s very likely you’ve actions have been measured and taken into account by machine learning processes.

 

Real World Examples of Machine Learning in Action

Companies of all sizes are now relying on machine learning to help secure and grow their businesses. Its application ranges from identifying savings opportunities to detecting fraudulent activity.

Here are a variety of day-to-day activities that are powered by machine learning algorithms today:

  • Search Engine results
  • Website and mobile device advertisements
  • Fraud detection
  • Customer segmentation
  • Loan requests
  • Equipment failure predictions
  • Pattern and image detection
  • Spam email systems
  • Dynamic pricing models
  • Next-best offers and credit scoring

Finally, what about machine learning for Amazon sellers? We’re going to dig into how it’s already being used and how FBA sellers can take greater advantage of it.

 

What About Machine Learning For Amazon Sellers?

If you hadn’t realized already, the entire Amazon platform is already deeply integrated with machine learning. The algorithms we’ve been describing are the driving force between many of their internal systems. From front-end product ranking systems and recommended products to the back-end fulfillment centers and inventory management.

While machine learning for Amazon sellers can’t be used build, launch and manage entire FBA businesses (yet), it offers various unique insights for optimization and growth.

Within Amazon Seller Central, we’ve already got some machine learning powered predictive analytics at our fingertips. For those hungry for more, third-party software can take provide you with more. Then if you’re selling on your own platforms, there’s even more data and possibilities available too.

It’s time to start making data-based decisions using machine learning for Amazon sellers. Now let’s take a look at what’s available right now:

Optimal Pricing Strategy

Product pricing can be a fickle thing on Amazon. You’ve got competitors pricing, customer perception of quality, supply-and-demand plus various other factors to take into account.

As a result, there’s a fine balance point where profits can be maximized without losing your ranking or sales velocity.

Finding the optimal pricing point for each product can be a lengthy process when completed manually. Why not rely on machine learning regression algorithms to find it based on data? Actually, that’s exactly what Profit Peak does!

It’s really as simple as switching it on, letting it run over time and optimizing your selling price on autopilot. You can learn more about exactly how Profit Peak works here.

Product Recommendations

Amazon has already got product recommendations handled nicely. Their machine learning algorithms and predictive analytics have access to heaps of customer data to work from. As a result, their recommended products can be incredibly accurate.

While shoppers will be quickly segmented into groups to provide relevant recommendations, it gets more intelligent over time. Amazon’s algorithm learns purchase habits and related interests to serve more accurate recommendations over time.

In actual fact, Amazon has been seen using up to 8 different algorithms applied on single pages.

There is nothing us FBA sellers need to do to take advantage of this, accept simply selling your products on Amazon. Let their mighty machine learning algorithms recommend your products to relevant buyers and reap the sales.

Sales Performance

Looking to launch new products in the near future? Of course, you don’t want to go in blind!

You want to have the reassurance that there’s enough demand to make it a highly-profitable venture. Otherwise, you risk losing time and investment on dud products. The opportunity cost is great, so you can’t afford to guess.

But how can you acquire relevant data for potential products you’ve never sold before? You need machine learning powered predictive analysis of sales performance, competitors, reviews and traffic. That way you can have greater confidence in your next product launch.

Jungle Scout offers an exceptional data-driven research method that does just that. Uncovering new lucrative markets and building product popularity is the best way to save time and boost your revenue. You can even go and try out Jungle Scout for free.

 

Final Thoughts

Machine learning for Amazon sellers is already here, but the potential is only just being realized. The future will enable more data to be gathered and analyzed, with greater predictive abilities. That is, the opportunity to make profitable optimization changes to your business.

These days, it seems our brain capacity is becoming the limit to potential business growth. So perhaps it’s time to let machine learning algorithms do the data crunching and show us new opportunities.

Watch this space for more powerful machine learning powered tools in the future!

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