AI for Ecommerce: How It’s Transforming the Future

Ecommerce is one of the leading adopters of artificial intelligence (AI), with use cases from personalized product recommendations and enhanced customer service to pricing optimization, smart logistics, and sales/demand forecasting. Organizations that adopt AI business strategies generate at least 20% additional revenue and reduce costs by an average of 8%. The lucrative returns on offer have attracted significant global investment, which has more than quadrupled between 2015 and 2021.

The pandemic accelerated the trend for ecommerce, and the shift to online shopping is set to stay and grow in popularity. In 2021, 17.8% of sales were made from online purchases. Two years later, this has increased to 20.8% — and by 2025, it’s anticipated that nearly a quarter (23%) of all purchases will be made online. 

However, despite the importance ecommerce presents to the global economy, it places retailers in a predicament because the product alone is no longer enough. To successfully get in front of customers online, retailers need to cut through the noise.

This is where AI helps.

In this article, we look at how AI allows retailers to evolve their customer journeys and create personalized experiences that keep shoppers coming back for more. We’ll also consider how AI helps with internal operations to improve overall competitiveness, as well as look ahead at what the future of ecommerce looks like for those who successfully adopt AI.

Main Types of AI for Ecommerce

Before we continue, consider these three incredible statistics related to AI in ecommerce:

• By 2032, the ecommerce AI market is expected to reach $45.72 billion 
• 84% of ecommerce businesses place AI as their top priority
• AI for ecommerce delivers more than a 25% improvement in customer satisfaction, revenue, or cost reduction

These statistics help demonstrate the growing importance of AI in the ecommerce industry, and the potential benefits it can provide for both businesses and consumers. AI can help businesses have better customer interactions and deliver personalized recommendations, while also streamlining internal processes to make shopping more efficient. 

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Personalized Product Recommendations

Just as the way people shop has changed, so has their expectations. Today’s customers want to receive a personalized experience when shopping online, and when retailers deliver that experience, they’re rewarded with a 40% increase in revenue. With only 1 in 10 retailers admitting to fully implementing personalization across all channels, it’s a huge untapped opportunity. 

So how does AI deliver personalized product recommendations?

AI distills insights from past customer behavior data — such as searches, clicks, and purchases — by feeding it into data-filtering tools, which use algorithms to recommend the most relevant items to a particular customer. 

It’s most often seen on websites, where retailers highlight sections that are “Inspired by your shopping trends,” suggest related add-on items in a cart, or share location-relevant content based on where the customer is.

Personalized product recommendations enhance the shopping experience because they help customers find what they want quickly, as well as suggest additional products they may benefit from.

From the retailer’s perspective, it can greatly boost customer loyalty and provide opportunities to cross-sell or upsell. According to research by McKinsey, the business impact of using AI for personalization alone results in:

• 10-30% more efficient marketing and cost savings
• 3-5% increased customer acquisition
• 5-10% higher satisfaction and engagement

Smarter Searches

In addition to personalized product recommendations, AI also enables retailers to understand the intent behind a shopper’s search query. When the average ecommerce bounce rate is between 20-45%, smarter searches are shown to reduce this number by offering up more relevant results.

So how does AI search for ecommerce enable personalized searches that know exactly what a customer wants?

Like personalized product recommendations, AI identifies patterns in both online and offline data to understand customer intent. Machine learning algorithms take the analysis further to make the data contextual. For example, if a shopper searched for “hats,” and the AI was able to determine they have an upcoming wedding, it might return results for fascinators rather than woolen winter hats.

Furthermore, because AI technology is constantly “studying” the user, it learns about individual preferences and can make more accurate recommendations. So, for a search like “best holiday clothes,” the results would show clothing from the shopper’s favorite brand(s) and appropriate for a city break, which has just been booked.

This hyper-targeting can tackle every retailer’s nightmare: abandoned carts. Globally, the average shopping cart abandonment rate is 70.21%. Through smarter searches, which understand the shopper’s intent, retailers can show the right product, in the right place, at the right time.

Logistics and Forecasting

While AI offers a lot to enhance the customer experience, it can also have a significant business impact behind the scenes. As the adoption of ecommerce continues to spread, retailers can use AI to manage the complexities inherent to their operations by optimizing warehouse processes and revolutionizing supply chain management.

For AI to support logistics and forecasting, it’ll pull data from various sources, including transactional data, behavioral data, demographic data, and ecommerce data (e.g., how a customer reacted to a promotional email). By applying techniques like machine learning, data mining, optimization algorithms, and neural networks, retailers can analyze vast amounts of data in real time to identify patterns and make predictions. This is particularly useful for AI use cases in ecommerce like:

• Inventory management — analyzing historical sales data, current market trends, and social listening insights to generate accurate demand forecasts 
• Seasonality predictions — accurately predicting demand for one-off, annual, or infrequent events (like Black Friday, where online sales rose 2.3% year-on-year)
• Pricing optimization — creating dynamic pricing based on supply and demand helps to calculate the minimum discount needed to secure the sale

Additionally, AI allows retailers to automate repetitive tasks and streamline workflows, which can significantly reduce the time and cost associated with warehouse operations. This is helpful for things like:

• Supply chain management — McKinsey research shows AI adopters have improved logistics costs by 15%, inventory levels by 35%, and service levels by 65%
• Delivery — 99% of consumers say fast delivery is important when making online purchases, which is why 42% of retailers are working on how to offer same-day delivery

AI Assistants

AI-powered chatbots currently handle 70% of online customer conversations. However, following the launch of generative AI, the value of the ecommerce sector has ballooned to $5.92 trillion as retailers rush to level up their current chatbots with new functionality. Generative AI in ecommerce can now power conversational commerce online, which frees up commerce-driving team to work on less menial tasks. 

For shoppers, AI assistants will respond to more complex queries at any time of the day or night, share product recommendations based on retargeting campaigns, and provide real-time updates for accurate package tracking. 

For retailers, AI assistants will break down language barriers in the supply chain to avoid costly mistakes, identify counterfeit goods, and optimize product descriptions based on analysis of keywords and automatic A/B testing.

But how exactly do AI-powered chatbots work?

AI’s deep learning algorithms can determine individual preferences to provide appropriate recommendations. For example, by analyzing customer reviews, the technology could understand that garment sizes run large and recommend a shopper purchase a size down as they try to add a new sweatshirt to their cart. 

Similarly, chatbots trained with natural language processing (NLP) can tailor recommendations to a specific shopper at a specific point in the buying journey. Imagine a parent is booking a party for their child’s birthday. The chatbot could recommend they also book a cake now to avoid disappointment, share details of a local bakery — and even suggest various styles of numbered candles to coincide with the child’s age.

The Big Trend To Look Out for in AI Ecommerce

The biggest trend on the horizon? Conversational commerce. Voice-enabled assistants are already an ingrained part of our daily lives — think Apple’s Siri, Google Assistant, Amazon’s Alexa, and Microsoft’s Cortana. The technology behind these AI voice assistants will only advance, and as it does, it’ll create a frictionless voice-enabled shopping experience that’ll affect buyer purchasing behavior.

Thanks to natural language processing, conversational commerce is already beginning to facilitate personalized shopping experiences at scale. Artificial intelligence will be a catalyst in ensuring that customer data and a customer's purchase history is being put to work to offer the most seamless experience possible to online shoppers.

In 2017, voice-enabled shopping was worth $2 billion. Five years later, the market has grown exponentially to reach $40 billion — and much of that’s attributed to only 10% of consumers who use voice assistants to purchase daily.

The Future of AI in Ecommerce

In order to embrace AI in a meaningful way for your business, you’ll need an all-in-one solution like Bloomreach. Whether you need AI-driven product discovery or intelligent omnichannel marketing, our technology enables you to deliver customer experiences so personalized, they feel like magic. 

Here are some of the impactful results that ecommerce brands have driven with Bloomreach:

• The Thinking Traveller increased booking inquiries by 33%
• 4Home achieved 800% ROAS (return on advertising spend) 
• Bensons for Beds increased ecommerce sales by 41% YoY 

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