AI in eCommerce: data, applications and trends
The use of AI in eCommerce and its countless possibilities for business has never been so discussed.
Currently, e-commerce is one of the leading adopters of Artificial Intelligence, with use cases ranging from personalized product recommendations to price optimization, smart logistics and sales/demand forecasting.
In this article, we will see how AI allows retailers to evolve customers’ purchasing journeys and what the main trends are.
Main types of AI in eCommerce
To start, let’s look at three incredible statistics related to AI in eCommerce:
- By 2032, the eCommerce AI market is expected to reach US$45.72 billion
- 84% of online companies place AI as their top priority
- The use of AI in e-commerce improves by more than 25% customer satisfaction, revenue and cost reduction
These statistics demonstrate the growing importance of AI and the potential benefits this technology can provide to businesses and consumers.
Next, let’s see some of its applications that are already changing the world of online shopping.
Personalized product recommendations
Just as the way people shop has changed, so have their expectations.
Today’s consumers want to receive a personalized experience and research points to a 40% increase in revenue of the stores that deliver this experience.
To do this, AI distills insights from customer behavior data – such as searches, clicks and purchases – feeding them into data filtering tools that use algorithms to recommend the most relevant items for each buyer.
Personalized product recommendations help customers find what they want quickly, generating satisfaction that leads to brand loyalty.
For the retailer, this also translates into more sales opportunities with cross-product offers or upsells, for example.
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Smarter searches
Just like personalized product recommendations, AI in ecommerce identifies patterns in online and offline data to understand customer intent.
Machine Learning Algorithms take analysis further to make data contextual.
For example, if a shopper searches for “handbags” and the AI can determine that they have an upcoming wedding event, it may return results for party bags rather than items with a more casual style.
Additionally, because AI technology constantly “studies” the user, it learns about individual preferences and can make more accurate recommendations.
This hyper-segmentation can help solve every retailer’s nightmare: abandoned shopping carts.
– How to reduce ecommerce cart abandonment rates
Globally, the average shopping cart abandonment rate is 70,21%.
Through smarter research, which understands buyer intent, retailers can show the right product, in the right place, at the right time, avoiding as much as possible that the consumer loses interest in the product.
Logistics and Forecasting
As the adoption of AI in e-commerce grows, retailers are beginning to use this technology to manage the complexities inherent in their operations, optimizing storage processes and revolutionizing supply chain management.
To improve the logistics and forecasting process, Artificial Intelligence extracts information from multiple sources, including transactional, behavioral, demographic, and e-commerce data (e.g., how a customer reacted to a promotional email).
By applying techniques such as machine learning, data mining, optimization algorithms and neural networks, retailers can analyze large amounts of data in real time to identify patterns and make predictions.
This is particularly useful for:
- Inventory Management: Data analysis of sales history, current market trends, and social listening insights to generate demand forecasts accurately.
- Seasonality forecasts: Accurately forecast demand for one-off, annual or infrequent events (like Black Friday).
- Price optimization: Creating dynamic pricing based on supply and demand helps you calculate the minimum discount needed to secure the sale.
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Additionally, AI allows retailers to automate repetitive tasks and simplify workflows, which can significantly reduce the time and cost associated with warehouse operations.
This is useful for things like:
- Supply chain management: a survey carried out by McKinsey showed that AI adopters in e-commerce improved logistics costs by 15%, inventory levels by 35%, and service levels by 65%.
- Express delivery: 99% of consumers say that fast delivery is important when shopping online, which is why 42% of retailers are working on how to offer same-day delivery.
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Service Assistants
Generative AI in eCommerce can now power online conversational commerce, answering frequently asked questions, which frees up the team responsible for customer service to work on more complex tasks.
AI assistants in ecommerce are able to respond to queries at any time of the day or night, sharing product recommendations based on retargeting campaigns and providing real-time updates for package tracking.
For retailers, AI assistants offer the advantage of avoiding costly mistakes, optimize product descriptions based on keyword analysis and perform A/B testing automatically.
– 6 ways to use chatbots to increase e-commerce sales
The Future of AI in Ecommerce
The biggest trend on the horizon? Conversational commerce.
The technology behind voice assistants like Alexa and Siri will only advance, and as it does, it will create a voice-enabled shopping experience that will impact consumer behavior.
Thanks to natural language processing, conversational commerce is already beginning to facilitate personalized shopping experiences at scale.
AI in ecommerce will be a catalyst in ensuring that customer data and purchasing history are utilized to deliver the best possible experience to online shoppers.
Furthermore, a survey by the Institute of Electronic and Electrical Engineers (IEEE) carried out in five countries shows that AI will be the main technology of 2024.
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