Machine Learning Ecommerce: Optimizing Prices with ML

Machine Learning Ecommerce: Optimizing Prices with ML

March 26, 2024

Whether you’re running an established store or just starting out, you might have considered adopting machine learning into your ecommerce stack. Machine learning is an advanced technology that provides ecommerce owners with a wealth of benefits.

In this post, though, we’re going to reflect on how ecommerce stores can utilize machine learning within their pricing optimization process.

You’ll learn:

  1. Why vendors struggle to set the right prices
  2. What machine learning is
  3. What price optimization is
  4. How to do A/B price testing with machine learning
  5. A practical example of machine learning within ecommerce
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Why ecommerce vendors struggle to set the right prices

You might read articles about various different pricing strategies and why they work so well.

Now, what usually happens is, you’ll begin reading about a new pricing strategy and you’ll be keen to implement it into your store right away.

After all, you want the same kind of results the article talks about, right?

But, what the articles don’t mention is the concept that each store has unique pros and cons for specific pricing strategies based on their overall objectives.

For example, some ecommerce stores might have a strategy that sees them trying to maximize the profit gained on each product. Others might want to access a new market or location and others might need to increase their overall market share.

Whatever the aim of your business, different pricing strategies will work. However, the best way to ascertain which one is right for you is to use machine learning technology.

When pricing your products, you might have various questions like:

  1. If we want to increase sales by 35% in the next month, what price should we set our products at
  2. Based on the current market activity, what price is fair for these products

In order to price your products right, you need to have a way to be able to answer these questions with answers that go beyond simple assumptions.

What is machine learning?

Machine learning is a type of artificial intelligence. In it’s simplest form, it’s a method you use to improve the way a system performs over time based on experience.

Machine learning is an advanced technology used within ecommerce to help you learn more about the processes you have in place.

Machine Learning

Machine learning goes beyond generalizations to understand what customers like, what customers don’t like and everything in between. You can also use machine learning to better understand how your customers might like information presented to them.

The way it works is by testing and adapting your current processes to establish and learn patterns.

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It uses these patterns to make smart, data-backed predictions about what the best next steps are.

Slowly but surely, using data from your own store, your own visitors and your own customers, machine learning systems refine the way you think about pricing and adapt to suit the customer at hand.

In pricing, specifically, machine learning allows e-retailers to develop and create complex pricing strategies to achieve their desired results faster and more effortlessly.

What is price optimization?

When you optimize the prices of the products on your ecommerce store, you rely on data analysis to better understand how your customers will respond to different price points and establish the best prices for your business – based on the overarching business goals.

When ecommerce was new and fresh, retailers had to rely on simple pricing strategies like cost + strategy or psychological strategies such as the power of nine. However, with technological advances, retailers are able to cleverly predict the demand for a product against the desired price point.

Because of this, you’re able to predict what impact your marketing campaigns might have on your sales and revenue, predict the best price point for a product at any given time or even how much to sell a product for if you want to generate the desired revenue stream in a specific time period.

Ecommerce owners can factor in:

  1. Local demand
  2. Global demand
  3. Seasons
  4. Business operating costs
  5. Business goals
  6. Competitors prices
  7. Weather

To establish:

  1. The initial price to set products in order to generate the most revenue and profit
  2. Optimal price to discount your products based on people’s willingness to buy

An example of machine learning in ecommerce

So we’ve established that machine learning algorithms collect information and data regarding pricing trends. When you start using a system that learns what’s happening on your store and beyond, you’ll have access to a wealth of vital information.

Let’s see how this works in a real example.

Imagine, for a moment, you have an online store that sells t-shirts. You want to know:

  • What’s the best price for next season I should sell the t-shirts at.

You’ve already experienced a load of competition, so you really need to nail your pricing.

Gathering your data with machine learning

First, you’ll want to give the algorithm data. In order for ML to learn and adapt it needs something to learn from.

You could offer:

  • Competitors pricing data
  • Transactional data
  • Past promotions
  • Inventory
  • Customer reviews

The data you feed the technology will depend on your goals. If your goal is to increase prices, then you’ll absolutely have to offer up your transactional data as well as competitor data, if you have it.

Picking goals

Before the algorithm can make predictions, it needs to know the parameters you’ve set. For example, if you know for certain you do not want to sell your t-shirts for less than $5 you can set this rule.

These rules help the algorithm understand your business model better when it comes to applying the results of data to it.

Once your goals are set, you need to start modeling the data.

In this sense, the data the algorithm has previously collected is used to create models. There is a range of different models that can be used from logistic regression to GLMs. The one you use will depend on how complicated your data is.

Your machine learning system will be able to utilize these models to ensure that you’re able to quickly and intuitively find the information you need based on previous data.

Once your system is trained, you’re able to estimate smart prices for new products. Now, when it comes to answering questions about your t-shirt business, you’re equipped with enough data to support your ideas.

Final thoughts

Because pricing is such a crucial aspect of how your business grows, it’s important to get it right. We’ve established that there’s no one-size-fits-all when it comes to pricing strategies.

This is because each business has its own unique set of goals.

And due to decreasing margins and increased competitiveness each day, ecommerce vendors are forced to think fast.

That’s why forward-thinking store owners implement technology like machine learning within their ecommerce ecosystems to ensure decisions are accurate and based on real historical data.

With this, you’ll be able to better understand how your customers will react to each price strategy you decide to implement.

What’s more, you don’t need to program the models yourself. The beauty of machine learning is that the technology learns patterns from data and adapts itself as a result.

Have you started implementing machine learning into your pricing optimization strategy?

Leave a comment below.

Frequently Asked Questions

How do you do price optimization?

Dynamic pricing is a price optimization strategy for commerce businesses. There are many techniques you can apply to optimize pricing. Dynamic pricing comprises several of these techniques.

How is analytics used for dynamic pricing?

Consumer data, demand, supply, historical price information, stock information, etc. are collected and analyzed in the dynamic pricing strategy.

Why is price optimization important?

There are countless players in the ecommerce arena, and one has to find the optimal price/demand ratio that’ll attract many customers and maximize profits.

1 Comment
  1. Johan
    April 10, 2019

    Very interesting article. It is definitely the future.

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