Machine Learning for eCommerce: A Quick Guide

Tools that use machine learning for eCommerce are a must in the coming year. 

As competition heats up, so does your need to be able to automate and optimize in real-time while also accessing live data you can use to engage shoppers directly. Machine learning is vital for doing just that. 

So, where to start? 

In this post, we will discuss: 

Let’s jump in. 

How Does Machine Learning Work?

Machine learning is a branch of AI that teaches a system to identify patterns. Made up of a collection of algorithms, it becomes more accurate as it analyzes more data, patterns, and outcomes, adapting to these updated "learnings" automatically.

Here is a breakdown of a simplified version of what the steps might look like. Let's imagine you were teaching a system to recognize dogs.

  1. Data collection. If you’re teaching a platform to recognize dogs, you will first need to feed it lots and lots of dog images. 
  1. Organization. Next, you label these data points, labeling the images you fed as “dogs.” 
  1. Training. Feed even more data into the system, prompting it to try to recognize patterns, or in this case, dog features. 
  1. Testing/Validation. Submit new images to the system to see if it can identify dogs. Additionally, adding more data into the system to help it recognize more features — like tails or ears — to find common characteristics. 
  1. Optimization. Teach the system to adjust if/when it makes mistakes by feeding it the incorrect examples again. After all, as the name suggests, it learns from its mistakes.  
  1. Deployment. Once the system is excellent at recognizing dogs on its own, it can be used in real-world applications. 

eCommerce Machine Learning Benefits 

With the rise of machine learning AI in eCommerce tools and functions, brands can automate more tasks, optimize the customer experience across all digital touchpoints in real-time, and decrease operational costs. 

Let’s quickly look at the four main benefits of implementing machine learning into eCommerce functions and business elements. 

1. Improved Backend Efficiency 

Tools that harness the power of machine learning algorithms offer real-time data and insights that can be used to optimize operations — before any inefficiencies become costly. This is particularly important when used for inventory optimization and marketing combined.

Pro Tip: Advanced technology like Kimonix, for instance, can combine customer demand with inventory retail KPIs to automatically push the most profitable products to customers. Products they are actually actively searching for.

2. Enhanced Marketing Campaigns 

By drawing from your growing store and marketing data, strategic marketing and merchandising tools can make your marketing far more relevant. Relevant to each of your potential customers, adapting to their behaviors and preferences, current market trends, and your real-time business objectives. 

3. Increased Conversions, Revenue, and Profits 

Machine learning technologies offer real-time optimization based on live data. They also enable you to optimize strategies before they launch to boost performance from the start. The result is more innovative strategies that show shoppers precisely what they want at that moment (increased conversion potential) for less advertising costs — both in spend and time. All of which help boost revenue and increase profitability. Advanced product recommendation tools that factor in profit-driven KPIs are good examples of these practical benefits. 

4. Upgraded Data for More-Informed Business Decisions 

While machine learning initially requires data, it continuously collects when deployed. This means that eCommerce brands have a steady stream of real-time insights and data they can use to make far-more-informed decisions across all areas of their business. 

eCommerce Machine Learning Strategies and Tools 

Ultimately, using the right machine learning AI tools for your online retail business enables you to dramatically improve your shopping experience throughout your sales journey. The key is matching the right strategy to the right tool. 
Let’s look at the top retail business areas you should consider implementing AI. 

1. Online Merchandising and Product Sorting 

One of the most important areas you want to automate and optimize is your online merchandising and product sorting strategies. By harnessing the power of machine learning, you can be sure you show just the right product to the right customer at the right time. Here are some top merchandising and product sorting tools

  • Kimonix 
  • Yieldify 
  • Cogsy
  • StoreHero

2. Site Search

Another strategic point in your shopping journey worth optimizing with AI is your store’s site search. By adding machine learning tools, you can harness the power of intelligent algorithms to include visual search and personalized results. 

Here are some top site search personalization and optimization tools: 

  • Findify 
  • Sparq
  • Searchanise 

[Source: Searchanise]

3. Inventory Management

Whether it’s automatic restocking or warehouse management, inventory optimization covers a wide range of tasks for online retailers. Adding machine learning tools here can help to keep backend operations more efficient while decreasing costs. 

Some top inventory management tools that incorporate machine AI technology include: 

  • Veeqo  
  • Cogsy
  • Stock Sync
  • TradeGecko

4. Customer Support 

With eCommerce competition increasing at a quick pace, being able to engage with customers 24/7 is becoming a must. You also want to make sure that you can engage with them on any channel at any point during the exchange. Machine learning algorithms can help you automate many processes, reducing the need for huge support teams. 

Some top customer support tools that incorporate machine learning algorithms include: 

  • ChatBot
  • Yotpo
  • Zendesk Chat

5. Customer Engagement 

With the right AI tools, you can not only predict customer behavior but personalize the experience for each specific potential shopper or customer. This is vital if you want to streamline your shopping experience and, in turn, boost sales. 

Here are some popular tools that use machine learning to help you personalize customer engagement: 

  • Klaviyo
  • Dynamic Yield
  • Gorgias

6. Product Recommendations 

Real-time customer data is one of the most valuable components of creating personalized marketing campaigns. This is especially important for a product recommendation, where you want to be able to automatically show a shopper the exact product they are actively searching for in that moment. 

A great tool that does just that is Kimonix. Our groundbreaking machine learning technology prioritizes profits to deliver 1:1 product recommendation personalization. This enables you to show products your customers actively want to buy. 

7. Fraud Detection 

Fraud detection and prevention is another area of your online retail business where machine learning technologies are becoming hugely beneficial. This is because machine learning has the ability to analyze vast amounts of transactional data to look for potentially fraudulent patterns in real-time.

Here are some optional tools that use machine learning to detect and prevent fraud: 

  • Kount
  • ClearSale
  • Riskified

Final Thoughts: Implementing Machine Learning 

As you can see, there are many areas of your business where you can adapt machine learning technologies to optimize efficiency, profitability, and performance. But before investing in implementing machine learning all across your site and buyer journey, you will want to follow these steps: 

  1. Identify the top areas of your business you want to improve 
  2. Get familiar with machine learning technologies 
  3. Compare third-party AI tools' expertise and features
  4. Compare costs and capabilities to find tools that get you the most capabilities for your budgets 
  5. Add one tool at a time and then keep a close eye on essential KPIs and metrics in order to measure and scale 

Still have machine learning questions? Get in touch with us here.

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