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Machine Learning en Commerce électronique: Comment Machine optimisation de prix de Supports d’apprentissage

Que vous soyez en cours d’exécution un magasin établi ou à ses débuts, vous pouvez avez ont considéré l’adoption d’apprentissage automatique dans votre pile de commerce électronique.

Apprentissage automatique est une technologie de pointe qui fournit les propriétaires de site e-commerce avec une multitude de prestations.

Dans ce post, Bien que, we’re going to reflect on how e-commerce stores can utilize machine learning within their pricing optimization process.

Vous apprendrez:

  1. Why vendors struggle to set the right prices
  2. What machine learning is
  3. Ce qui optimisation de prix est
  4. How to do A/B price testing with machine learning
  5. A practical example of machine learning within e-commerce

Why e-commerce vendors struggle to set the right prices

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

Maintenant, 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.

Après tout, you want the same kind of results the article talks about, droit?

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.

Par exemple, some e-commerce 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. Cependant, 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 simply assumptions.

What is machine learning

Machine learning is a type of intelligence artificielle. 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 e-commerce to help you learn more about the processes you have in place.

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.

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 e-commerce 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 businessbased on the overarching business goals.

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

Pour cette raison, 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.

E-commerce 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 best initial price to set products in order to generate the most revenue and profit
  2. The best overall price to keep your products at
  3. The best price to discount your products to based on people’s willingness to buy

An example of machine learning in e-commerce

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.

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

  1. 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

Première, 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
  • Inventaire
  • Commentaires des internautes

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. Par exemple, 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 modelling the data.

En ce sens, 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. Maintenant, when it comes to answering questions about your t-shirt business, you’re equipped with enough data to support your ideas.

Réflexions finales

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, e-commerce vendors are forced to think fast.

That’s why forward-thinking store owners implement technology like machine learning within their e-commerce 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.

Quoi de plus, you don’t need to programme 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?

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Commentaires

1 commentaire

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Johan

Very interesting article. It is definitely the future.


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