TLDR
Fueled by a great increase in online shopping, e-commerce platforms have been utilizing AI to improve the customer experience and optimize their business performance. AI has the power to customize websites for individual users, power chatbots which can understand and respond to complex questions, and get a detailed understanding of business performance.
Outline
E-commerce as we know it
AI use cases today
Challenges
Conclusion
E-commerce as we know it
Fueled by platforms like Amazon, which have simplified online shopping and the Covid-19 pandemic, e-commerce has seen a significant rise in the last year with no sign of slowing down. According to the
, e-commerce increased from 14% of global retail trade in 2019 to 17% in 2020.
In a pre-pandemic world, retailers were already utilizing machine learning (ML) tools such as targeted advertisements on mobile devices and experimenting with different ways to incorporate artificial intelligence (AI) into their systems. As buying habits are increasingly moving towards online, retailers and AI developers have become even more incentivized to incorporate AI/ML into e-commerce.
Mutually beneficial for customers and businesses, Al/ML’s integration into e-commerce has the power to create a more personal shopping experience for the customer while creating the most likely environment for a sale.
AI use cases today
Personalization and more targeted offers
AI is being used to improve e-commerce to be reflective of the customer experience. Using website traffic, algorithms are able to recognize how customers are interacting with websites and what products are most likely to result in a sale. Using this data, businesses are then able to push more popular products to the most accessible parts of their website.
In the past, workers would have to go through product listings and individually attribute them with key terms. ML algorithms have allowed autonomous product labeling, allowing the business to use their human capital in ways that create more value while optimizing business outcomes. This also allows businesses to better identify what their customers are looking for across platforms.
Leveraging the power of ML, e-commerce companies use cookies to track customer data across search history to better understand and serve them on an individual basis. Customer data is first collected and scraped through ML algorithms to detect certain behavior that would benefit a particular website.
Another layer of algorithms are then applied to rearrange the view of websites to reflect the unique search history and interest of every customer.
Chatbots to improve customer experience
E-commerce platforms are increasingly using chatbots to create a more personal and interactive customer experience. Chatbots allow customers to talk with computer software just as they would talk with a customer service representative. Integrating chatbots into e-commerce platforms provides fast, effective, and consistent customer support around the clock.
The most common chatbots have become natural language processing (NLP) chatbots, which use AI to handle complex customer questions. Once limited to the responses they were programmed to replicate, NLP has allowed chatbots to use context and meaning in their responses through neural networks.
Business Operations Planning
Business planning is one of the more challenging aspects of any retail business, for all the uncertainties of supply and demand. To help minimize uncertainties of business operations, AI is being applied everywhere from inventory and price management to coordination of the shipping process.
The rise in e-commerce has triggered a greater demand for shipping and delivery. The increased shipping demand has resulted in packages being delivered later than expected at checkout. To solve this, e-commerce platforms are applying AI to customer shipping data to track the performance of mailer carriers and better predict delivery dates while keeping customers updated on the process.
Inventory management is one of the most important and difficult parts of running a business. E-commerce has given companies access to an abundance of real-time data, which can be applied to AI to predict inventory needs. AI is being applied to predict the demand for certain products by analyzing current and historical customer data. Suppliers are also applying reinforcement learning, where AI is being used to make and act on predictions and adjust itself accordingly. Retailers who perfect the use of AI-generated algorithms within their systems can see a great reduction of operational costs.
Challenges
One challenge that e-commerce platforms run into when using AI is customer privacy concerns. Data privacy is a big concern for some people, and cybersecurity laws haven’t yet caught up with the rapid rate in which tech companies are evolving. One way e-commerce platforms ease customer concerns is by offering them the option to opt out of tracking cookies across platforms.
Conclusion
Like in all industries, AI is rapidly changing the way e-commerce platforms operate. From greater customer insights and business generation through obtaining and scraping customer data, offering an interactive experience with chatbots, or saving millions of dollars with a better sense for inventory and shipping processes, e-commerce has a lot to gain from AI’s implementation.
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