Industries II: How AI is Revolutionizing Retail and E-commerce

Welcome to the second installment in our "Power of Prediction: AI Across Industries" series. In this post, we'll explore how artificial intelligence (AI) is transforming the retail and e-commerce landscape, empowering both businesses and consumers.

The rise of platforms like Shopify has democratized online retail, allowing almost anyone to open their own E-commerce store. However, this accessibility has also brought new challenges. Thousands of entrepreneurs with little to no background in e-commerce technology now face the complex task of effectively acquiring and serving customers in a digital marketplace.

The Eternal Challenges of Buyers

Consumers have always faced two key challenges when they’re interesting in buying something:

1. Find products that meet their specific requirements
2. Get the best value in terms of price and quality

Most will hold off making the actual purchase until both needs are met. How do most of us go about achieving this? 

If the product is available in a traditional Brick-and-Mortar store, you are likely to spend the effort going to a store, examine alternatives, and potentially consult with a sales associate. Before finalizing the purchasing decision, many still rely on advice from friends and family and wait for sales events to get a deal - knowing many stores are exploiting that need by offering sales essentially all the time.

Online retail is different. With the many shopping experiences shifting online, buyers face a different set of challenges: Most E-commerce sites overwhelm you with the number of alternatives that present to you. Even if the product is associated with a known brand, you can’t trust product quality with so many knock-offs available, though online reviews do provide reassurance. Hunting for the best deal seems easier, as you can instantly access extensive price comparisons.

AI Solutions for Key Challenges in E-commerce

Now that we have understood the evolving buying landscape, let’s turn our attention to E-commerce. How does typical E-commerce software support the buying process?

1. Finding Products: Matching products to requirements based on partial information
2. Personalization: Tailoring the shopping experience to individual preferences, including presenting useful buying advice

Artificial intelligence is addressing these challenges through various innovative approaches:

1. Semantic Search

AI-powered semantic search goes beyond simple keyword matching. It understands the context and intent behind a user's query, allowing for more accurate and relevant product recommendations - especially if the buyer isn’t very familiar with the jargon of the domain. Cross-modal semantic search take a step further by letting buyers match pictures to actual products.

2. Recommendations

Using all the data systems collect about your behavior, purchase history, AI models can capture all that detailed information in profiles, and then E-commerce systems can adapt their behavior based on those profiles. For example:

• Suggest products that are likely to interest individual shoppers. We are all familiar with this technology - when similar products show up below what we’ve selected.
• Adapt the shopping experience in real-time, like what is presented to you after login.

Case Study: AI-Driven Personalization in Fashion Retail

A multi-channel fashion retailer with both brick-and-mortar stores and a robust E-commerce platform caters to a diverse customer base across various age groups and style preferences. Their challenges include high cart abandonment rate and low customer retention.

This retailer implemented an AI-driven personalization system that included:

• Predictive Analytics: Capture patterns in customer browsing and purchase history using machine learning.
• Dynamic Product Recommendations: Tailor product suggestions based on individual preferences and behavior.
• Personalized Marketing: Send targeted emails with curated product selections.

To make AI-powered E-commerce a reality, they expanded their existing e-commerce platform and CRM with AI capabilities, leveraged their historical customer data and product catalog for model training, and iterated on implementation details using A/B testing.

AI-driven personalization can significantly improve customer engagement and sales - this retailer saw 42% increase in click-through rate on product recommendations, 22% increase in average order value, and 35% reduction in cart abandonment rate. And customer testimonial qualitatively confirm the shift in shopping experience:

"The personalized recommendations feel like I have my own personal shopper. I discover styles I love that I might have otherwise overlooked!" - Sarah T., loyal customer

How can you implement AI-powered E-commerce?

Okay, so you’re ready to have AI innovate your E-commerce. What options do you have to proceed?

1. Build Your Own Semantic Search

For tech-savvy companies, building a custom semantic search solution involves:

1. Selecting a vector database
2. Developing a custom embedding model
3. Integrating the model into your search application

This approach offers maximum flexibility but requires significant technical expertise.

2. Turn-key Solutions

Many businesses opt for turn-key solutions from established enterprise search providers such as Coveo, Algolia or Lucidworks. These platforms offer powerful AI-driven search and personalization capabilities that you customize without significant development, but often come at a premium cost.

Another option is to build your store using a combination of AI-powered tools from major tech companies and your own application development. For example, you can get AI tooling from one of the major hyper scalers, and payment processing solutions from providers like Square. These tools are somewhat cheaper than Enterprise search solutions above, but you need to fund your own development.

3. AI-powered E-commerce for Midmarket Businesses

As another alternative, Featrix provides an embedding approach to predictive modeling that also makes personalization and recommendations a lot easier than above approaches. You can send event streams with a single API call and get recommendations back with another API call. To customize, you can change your data formats, provide positive and negative events, and apply weights. Learn more on the Featrix Feeds product page 

"The great thing about Featrix Feeds is that we’re able to bring AI to the customer beyond just using ChatGPT to write copy" — Marketing Consultant

There is no turn-key solution in Featrix for semantic search yet, but it is possible in principle, and we’ll describe it in a forthcoming blog.

The Future of AI in Retail and E-commerce

 As AI technology continues to evolve, we can expect even more innovative applications in the retail and e-commerce space, such as virtual shopping assistants or predictive inventory management. By embracing AI-powered solutions, businesses can provide more personalized, efficient, and enjoyable shopping experiences for their customers, while also optimizing their operations and boosting their bottom line.

“It’s about making connections through the data that you might not have made as a human being. AI has the uncanny ability to tease out things about the consumer you might never think about.” — Ryan Bezenek, vice president of IT, Ariat International

What  next?

• We’d love to hear about your specific situation and challenges. Drop a note to hello@featrix.ai, or comment on this blog.
• Check out part 1 of the series: How Churn Prediction has evolved with LLM and Embeddings.
• If you’re ready to try the SDK and API yourself, sign up for a free trial