6 Innovative Ways of Using Machine Learning in E-Commerce

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6 Innovative Ways of Using Machine Learning in E-Commerce

Machine Learning or ML is an AI technology that has been dubbed the next “big” thing in the modern business landscape.

While e-commerce businesses keep on getting complex and highly competitive, technological disruptions have made it a highly volatile space for business strategies and tactics.

However, with the smart ML-based tools and e-commerce analytics systems delivering actionable insights, such as demand forecasting into the unstructured heaps of data gathered over time, businesses are beating the odds everywhere.

As per the recent stats, 45% of the retail industry employed Machine Learning for online as well as offline customer engagement. 

Take a look at the following visual that shows the use cases of retail machine learning:

Dark Blue: Future Projections

Light Blue: Current Values

Use cases of retail machine learning
Source: Statista

As visible, there are 9 distinct use cases that offer tremendous scope for ML in e-commerce setups. 

What is the Difference Between Artificial Intelligence and Machine Learning?

You may have read this far and wondered “wait a minute, aren’t you speaking about AI rather than machine learning or machine learning?’. It’s either yes or no. Similar to thumbs and fingers, all machine learning is AI However, not all AI is machine learning.

What is Machine Learning?

Machine learning is one of the subsets of artificial intelligence. Machine learning technology uses data to make predictions or perform actions. The more information the technology is exposed to, the more precise its outputs.

What is Artificial Intelligence?

A wider range of tech falls under the umbrella of AI. Artificial intelligence is any technology that mimics human behavior. This could be learning, but could also mean detecting, reasoning, or adapting.

Deep learning is a separate subset of AI. It’s when complex neural networks analyze and learn from massive datasets. Here we are referring to the large volume of data that is available today.

Role of AI in E-commerce

AI is one of the key enabler technologies in e-commerce and is facilitating various services and functionalities for both, the vendor and the customer.

AI-powered customer solutions allow business owners to deliver streamlined customer experiences such as shopping inside the chat windows, query resolution, and delivering personalized product recommendations. Because of such customer-oriented services, the customers are able to make the most of every penny they invest and access better services.

Some other ways AI has revolutionized the e-commerce industry are:

  • Chatbots – 24/7 assistance, guided shopping, contextual information gathering and tackling repetitive queries
  • CRM – Better decisions stemming from advanced and in-depth analysis of customer data via AI tools
  • Customer service – Deliver services and recommendations in a highly targeted and personalized manner
  • Automation – Operation and process automation in multiple scenarios – warehouses, data management, inventory management, KPI, performance metrics, etc.

ML for E-commerce: Is There A Different Reality for Enterprises and SMEs?

One of the major concerns most SMEs have while thinking about adopting the AI or ML capabilities in their business model is the budget. They feel that large enterprises have the right amount of resources and budget to support this adoption.

However, they overlook the fact that ML can be leveraged in various forms. They need not have state-of-the-art ML tools or enterprise legacy systems in order to opt for smart e-commerce practices. They just need to choose the best way to leverage the technology for their business.

Hence, we are going to share 6 innovative ways to use Machine Learning in e-commerce. 

Further, we will also share how to choose the best way to use ML for your e-commerce setup.

Benefits of Machine Learning in E-commerce

1. ML Increases Conversions

ML boosts your conversions in two ways – product recommendations and offering on-site search engines. 

Machine Learning algorithms allow you to offer search results keeping many things in mind, such as:

  • Natural Language
  • Previous shopping and search history
  • Showing results on the basis of search query typed in natural language (even when it doesn’t mention the brand name or specific products)

By showing search results that are more aligned to the search queries, the AI solutions, like on-site search engines improve the overall customer experience and also increase conversions.

Further, ML-based online shopping assistants can offer highly personalized product recommendations, based on the analysis of visitor behavior and activity.

2. Improves In-House Operations

AI can deliver highly smart and actionable insights into customer data, website performance and sales or support stats. All this refined information comes in the form of reports that assist and empower your in-house teams to streamline their operations. 

Examples include:

  • AI chatbot offering the contextual and historical data about a query to an agent for faster query resolution
  • Showing website heatmaps
  • Recording visitor browsing sessions to show generic user behavior and experience on your website

From warehouse costs to sales and support to visitor experience – ML can help you improve your in-house operations based on actionable data-driven insights. 

3. Smarter Business Decisions

ML equips you with reliable and highly optimized business and market data. 

This allows you to make more informed business decisions, such as:

  • Product viability 
  • Audience availability for new products 
  • Shipping and payment decisions 

Next, we discuss various ways you can use AI solutions in e-commerce. 

6 Innovative Ways You Can Use Machine Learning in Ecommerce

1. Personalization

Personalized brand offerings are becoming an industry norm with more and more brands adopting smart ways to make their customers happier. 

Personalized offerings include the following:

  • Special discounts 

Based on the purchase and browsing history of the consumer

  • Personalized product recommendations 

Like the ones appearing on your screen when you watch Netflix

  • Personalized product catalogs 

Your customers see the products in the order of their preferences and website behavior

There are endless ways to tailor your brand offerings for your customers.

However, it is also one of the core hurdles. Such granular learning stems from thorough learning about your customers, which has its roots in customer data. One major problem with customer data is the noise. The data is cluttered and seems to make no sense on the surface, at least when inspected and sifted manually. 

This is where ML comes into the picture. It can process huge chunks of customer data, learn from it, and adapt as per the insights. So, you can offer highly personalized brand offerings to your customers, based on business insights. 

2. Customer Segmentation

Customer segmentation means segmenting or dividing your customers into various smaller groups or clusters. This clustering or segmentation is done on the basis of various parameters, that can be in-built (if you are using AI or ML-powered customer tools with pre-built features) and it can be custom segmentation (the smart customer tools allow you to set the segmentation criteria).

Some examples of segmentation criteria are as follows:

  • The purchasing power of the customers
  • Type of products they buy
  • Age
  • Gender
  • Demographics
  • Shopping behavior
  • Shopping patterns and preferences 
  • Previous brand interactions etc

Better segmentation allows you to learn more about your customers and target them with personalized offerings like never before. 

For example, if you are a chocolate manufacturing brand, you will know:

How many customers in which age group from which market zone have been eating which flavor of your chocolates and in which pack sizes.

3. Optimized Pricing

Optimized pricing or dynamic pricing means displaying different prices  to different customers based on various factors:

  • Demographics (Location, Age, Gender etc)
  • Shopping preferences 
  • Purchase histories
  • Browsing histories

Now, you might think: Why does it matter? 

The answer is: To ensure that your brand keeps on getting the revenue, your customers stay engaged and you can boost your sales.

Dynamic pricing considers all the above-mentioned and many other factors and targets the audiences likewise.

For example, showing heavy discounts on items bought or browsed earlier by your customers.

All this is done by smart ML-based customer or business intelligence tools that are designed and developed for e-commerce businesses. Also called Business Intelligence or BI tools, these ML-powered tools transform your decision-making process with fantastic results. 

4. Optimized Search 

ML-powered tools can detect much more than just popular search terms. They can dig into the mounds of search data from the search engine and reveal multiple hidden patterns. You can learn about the search, purchase, behavioristic, and preferences patterns your potential customers or existing customers have while searching for your brand offerings.

Further, these are not just the raw numbers, but actionable insights with proper nudges to draft your market strategy.

5. Customer Support

ML-based customer support allows you to automate your customer service and offer immediate, smarter, and better support. You can offer canned responses, bot-based support, self-service support, and lots of other such capabilities to make your support system excellent.

For example, take a look at how canned responses can offer excellent customer support in real-time:

Canned responses
Source: Youtube

6. Sentiment Analysis

ML can help you conduct sentiment analysis over the entire length and breadth of your customer base. Sentiment analysis allows you to identify how your customers perceive your brand and its offerings. You can dig into the customer comments and data collected over time to find out what makes them feel so about your brand. 

For example, the following visual shows how an ML-based text analyzer interprets underlying human emotions:

How ML-based text analyzer interprets underlying human emotions
Source: ResearchGate.net

Based on your learning, you can improve your brand offerings and align your offerings to your customers’ expectations.

How to Leverage ML for Your E-commerce Business?

Every business has different pain points and business-unique demands. 

So, the key considerations for leveraging the ML in the best manner are: 

  • Identifying the pain points of your e-commerce business model
  • Evaluating the various tools on the basis of their functionalities and capabilities
  • Shortlisting a few tools and then checking out the trial period plans or free plans for a basic experience
  • Making the final choice

Or, you can take the shorter and smarter route and opt for the Artificial Intelligence and machine learning consulting companies that offer Business Intelligence, Analytics, and lots of other data-based services for all types of businesses. 

Pro Tip for SMEs:

If you are an SME, then connect with an AI consulting company that can help you invest in some ML-based e-commerce or customer tool that comes with basic functionalities compatible with your requirements. If you want to go for legacy software, opt for the companies that offer multiple plans as per the business scale and requirements.

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