Have you ever bought additional products because of the personalized recommendations and non-intrusive upsell approach? That’s one of the ways artificial intelligence in e-сommerce is stepping forward to make the buying process more smooth and customer-oriented.
According to the statistics, 80% of all customer interactions will be managed by AI technologies (without any human agent) by 2023. Moreover, according to a survey by Tractica, AI in e-commerce is booming at such a pace that the revenue is expected to reach $36.8 Billion worldwide by 2025 (according to this e-commerce use case diagram).
So let’s dive right into how the artificial intelligence boom will transform e-commerce in the nearest time.
Artificial intelligence in e-commerce: How it transforms the industry
First of all, you need to know that the word “Artificial intelligence” combines three main components: data mining, natural language processing, and machine learning.
Data mining is responsible for gathering current and historical data to make predictions about customer preferences. Natural language processing is all about studying the interaction between humans and computers and learning how machines interpret natural human language. And, finally, machine learning collects algorithms to apply past experience or provide examples for solving a certain problem. Deep learning is all about involving layering algorithms to gain a greater understanding of the data.
All the factors we have mentioned above make artificial intelligence a powerful tool to optimize the e-commerce process and study customer behavior to make smarter decisions. Artificial intelligence will help customers to:
Provide a more personalized shopping experience
Brand loyalty has significantly decreased because of the pandemic. As a result, 76% have changed their stores, brands, and channels. With the help of personalization, which goes along with data and analytics, brands can make their connection with the customer stronger, increase loyalty and draw customers into the stores.
“For us, some e-commerce priorities that were previously five years out are now more of a three-year horizon. We need to more quickly understand how to satisfy that consumer and accelerate our timelines accordingly.”
— Todd Vasos, CEO at Dollar General.
Companies need to define the right moments in the customer journey where they can provide the greatest benefit and evaluate technology options that balance the level of investment and high fidelity. Also, companies need to use analytics on consumer reviews and the input from in-store interactions between sales associates and consumers to create an army of brand champions to guide continuous improvement.
Increase customer retention
According to McKinsey, while personalization is a top priority, only 15% of retailers have fully implemented personalization and customer retention across all channels.
E-commerce companies can improve the connection to consumers by improving the product and service discovery based on consumer preferences. These will help brands focus on fewer quality products that meet consumers’ needs, rather than offering hundreds of products and hoping one will hit the mark.
Improve the sales process
With the help of AI, e-commerce brands can create a more efficient sales process by gathering data about their customers, automating follow-up abandoned cart inquiries, and more. For example, a simple interaction with chatbots with simple questions can help move customers through the funnel and win their attention.
AI e-commerce solutions that work
Let’s see how AI has enhanced user experience in e-commerce and helped thousands of companies come to the next level. Take a look at this e-commerce use case diagram:
Personalized Product Recommendations
AI can gather all the data about customer’s search, such as history, third party data, content data, and other information to offer the necessary reference to the user. The personalized recommendations are usually provided based on past customer behavior and lookalike customers. Retailers use machine learning to capture and analyze data. Then, they use it to personalize the experience, implement marketing campaigns, optimize pricing, and generate customer insights.
Machine learning models process customer data and find out recurring patterns across various features. Such an approach helps marketing analysts discover customer segments that would be more difficult to do with the help of intuition and manual examination of data. It is a perfect combination of artificial intelligence and human intuition that can change the way we buy.
According to Forbes, 60% of shoppers choose retailers with optimal prices. Artificial intelligence (AI) and machine learning for e-commerce can help optimize prices and keep your profit margins. In such a way, pricing managers can switch to strategic tasks and increase their incremental profit. So how does it work?
These prices do not increase your marginality and do not cut the sales of other items in the product portfolio. Self-learning algorithms analyze large sets of data and suggest the most relevant price whenever it is necessary. Such algorithms analyze the unrevealed relationships between the products in the portfolio. Finding such links, they suggest individual prices that maximize the revenue and sales of the entire product portfolio. AI can help automate all the time- and labor-consuming tasks and help the team implement more customer-centric decision-making processes.
“Imagine a world where every product you sell maximizes your profit. Your pricing managers have time to negotiate more beneficial purchase prices and come up with more effective pricing strategies. Your customers choose you over your competitors over and over again. Your business grows steadily. That world is becoming a reality with AI.”
— Alexandr Galkin, CEO & Co-Founder of Competera - cloud-based pricing software for brick&click enterprise retailers globally.
Simple use cases of AI in e-commerce
Let’s review the examples of AI using cases in retail and how companies use this technology to delight customers. Here are the most popular e-commerce applications examples:
Sephora Color IQ
Sephora Color IQ is an AI-powered e-commerce use case product that scans shoppers’ skin. Using the results, it provides customized shade recommendations for foundation and concealer. With the help of this tool, Sephora increased its foot traffic and created a more personalized, creative, and unique shopping experience for every customer who enters their store.
Olay Skin Advisor
Olay works by scanning faces with the help of AI technology, meaning that the estimates are backed with data. Once a user’s skin has been analyzed via selfie, Olay recommends some of its anti-aging skincare products.
ASOS uses AI to recommend clothing sizes to shoppers based on what they’ve purchased — and kept — in the past. To analyze the customer behavior, ASOS analyzes which items and sizes customers return and which ones they keep.
eBay’s Repricing Technology
eBay AI tool alerts sellers to gaps in the inventory of a particular product so that they can stock up, in addition to making pricing recommendations and helping sellers price their products competitively. The same is similar with amazon ai phone cases.
Tommy Hilfiger Chatbot
Tommy Hilfiger’s uses a simple Facebook Messenger chatbot for providing a personalized and interactive shopping experience. A chatbot asks a series of questions to gather information about the user’s style preferences and make an outfit suggestion based on the data given.
“Before coming up with your own AI technology, analyze the competitors in your niche, download the apps and learn how these companies use AI solutions and for what purpose. Only after that can you start to figure out how your brand can benefit from this type of technology,”
— George Serebrennikov, COO at Proxet (ex - Rails Reactor) – a custom software development solutions company.
AI hard mode: What it is
AI hard mode is replicating the multi-faceted intelligence of human beings. It will be exceedingly better at everything they do because of overwhelmingly greater memory, faster data processing and analysis, underlooked synonym, and decision-making capabilities.
The development of AI hard mode will lead to a scenario most popularly referred to as singularity. And while the potential of having such powerful machines at our disposal seems appealing, these machines may also threaten our existence or, at the very least, our way of life.
At this point, it is hard to picture the state of our world when more advanced types of AI come into being. However, it is clear that there is a long way to get there as the current state of AI development compared to where it is projected to go is still in its rudimentary stage.
For those holding a negative outlook for the future of AI, this means that now is a little too soon to be worrying about the singularity, and there's still time to ensure AI safety. And for those who are optimistic about the future of AI, the fact that we've merely scratched the surface of AI development makes the future even more exciting.
How real business is using AI
Here are the best examples of use case e-commerce artificial intelligence in practice.
Alibaba is using AI to predict what customers might want to buy. By analyzing the customer data, it automatically generates product descriptions for the site. Also, Alibaba is using artificial intelligence is in its City Brain project to create smart cities. The project uses AI algorithms to help reduce traffic jams by monitoring every vehicle in the city.
Baidu uses an artificial intelligence tool called Deep Voice that uses artificial intelligence and deep learning that only needs 3.7 seconds of audio to clone a voice. They use this same technology to create a tool that reads books to you in the author’s voice—all automated with no recording studio necessary.
Ford Edge’s all-wheel-drive system uses artificial intelligence to automatically determine if all-wheel drive is needed — more quickly and accurately than a human driver. In the factory, AI can detect wrinkles on seat fabric.
If you want to leverage the power of AI and machine learning e-commerce, consider working with Proxet. Proxet can help you build conversational AI software. NLP, voice technology, and chatbots are our specialties. We’ve developed virtual assistants for our clients in the healthcare industry. Our top Artificial Intelligence engineers and machine learning experts do their best to help our clients succeed.
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