I am a bit tired of hearing the same tired discussions on omnichannel in e-commerce conferences. Omnichannel was a thing in 2010 but now it is part of the hygiene and best practice e-commerce companies are relying on artificial intelligence.
Using AI, businesses understand customers better, offer relevant products at the right time and through the right channel. AI also enables businesses to identify competitors’ strategies by enabling faster and more reliable analytics. Business Insider predicts that 85% of customer interactions will be controlled without a human until 2020.
And how will AI change e-commerce? Here are the top use cases:
Improving customer experience results in higher revenues for e-commerce companies and personalization is one of the most important levers for personalization. To learn more, check out our detailed article on the topic.
Recommendation systems are probably the easiest way to turn AI capabilities into sales, that’s why companies have been investing in better recommendations since 2000s. To learn more, check out our detailed article on the topic.
Site search is a valuable tool for the eCommerce industry. Customers ensure whether your business offers the product they are looking for via site search. This type of customer is more likely to buy since they have a product in their mind. However, if you don’t provide a relevant result for the written queries, customers will leave your website in a second.
To enhance the effectiveness of site search engines, businesses can use AI-powered search engines, also referred to as insight engines. Insight engines enable businesses to combine search with machine learning capabilities so that internal search becomes more personalized and accurate.
Improving products and services
An improved e-commerce product results in additional sales without new marketing activities.
Has it happened that just as you are browsing a product, a limited-time offer pops up on your screen? This can feel like the system reading your mind to prepare the right offer at the right time but it is essentially applied statistics.
A cool example in this category comes from Pinterest, the image sharing social network. Pinterest allows users to select an item from any online photo and then use Pinterest’s image recognition software to identify similar items.
Pinterest visual search engine
Along with finding matching products, an AI system can provide shoppers complementary product recommendations based on product attributes like size, color, shape, fabric, or brand.
Auto-generated product descriptions
Product descriptions are shop assistants of eCommerce. Customers make their purchase decision after reading the description. If they have not already decided to buy, this is a business’ last chance to influence customers.
Creating content is challenging and time-consuming for businesses. It takes about 18 months to complete 10,000 descriptions while AI content generation tools could do it in a few hours. By leveraging AI, businesses can also create a dynamic description that addresses the interests of each buyer.
Conversion rate optimization testing
Artificial Intelligence improves the productivity of testing programs. For example, Qubit is a personalization software that helps businesses deliver 1:1 e-commerce customer experiences. Qubit employs AI to verify A/B and multivariate testing results to identify false positives among user actions that lead to conversion.
Analytics is the science of producing insight from the patterns in complex data to make better decisions. Analytics is not a new concept for businesses. It has been a relevant topic for organizations since the mid-90s. Starting with sales and customer data, businesses used analytics in their decision-making processes but, in the age of AI, it is evolving with advances in machine learning.
Retail analytics is using enterprise data to find insights related to customer behavior, supply chain & inventory management, and target marketing. With those insights, eCommerce companies can make improvements in their performance. For more information about AI applications in retail, check out our detailed article on the topic.
Self Checkout Systems
For e-commerce players that want to expand their audience, physical stores can be interesting. Technologies such as those used in Amazon Go can be a good fit for e-commerce players expanding into physical retail.
Website performance improvements
Review and forum moderation
Customer reviews became cornerstones of online shopping. Fake reviews can easily erode customer trust. Some e-commerce companies use AI to challenge this issue, it is commonly called “astroturfing”.
For instance, Amazon is fighting with “astroturfing” with AI. Amazon system ensures that prominence and weight of verified customer purchase reviews are boosted.
Fake products can negatively impact the consumer’s perception of the original company. Al can also help spot fake products. 3PM Marketplace Solution helps marketplace owners ensure that their merchants provide original products with the help of a self-learning algorithm.
Business function specific use cases
Customer Service Applications
Chatbots and voice assistants in customer service
Digital assistants like Siri and Alexa are playing an increasingly important role in people’s lives. Similar assistants and chatbots can offer your customer real-time answers for their questions, solve their problems and even identify cross-sell opportunities.
For example, North Face is piloting IBM Watson as a sales assistant. Watson asks customers question like “where you are going to wear this jacket?” or “which time do you choose to wear this jacket?”. After obtaining responses, the program generates targeted recommendations.
Answers to questions like “Which camera is better for indoor photography?” can reduce significant friction and encourage more customers to buy.
Supply Chain Applications
Artificial intelligence can process more data than humans. Therefore, AI-powered demand forecasting solutions can provide more accurate results and prevent cash-in-stock and out-of-stock cases that businesses may encounter.
There are multiple fields AI can enhance workflow in eCommerce companies’ warehouses. Some examples are:
- Warehouse Robots
- Damage detection
- Predictive Maintenance
For more information on AI application in logistics, feel free to check our detailed article on the topic.
According to research from Demandbase,
- Generate a better sales close rate (59%)
- Increase revenues (58%)
- Improve website traffic and engagement (54%)
- Convert more leads (52%)
Automating back-office tasks such as report creation
Identifying prospective customers
For B2B e-commerce companies, identifying potential customers is crucial. For example, anonymous website visitor identification vendors help businesses identify the companies of website visitors so that marketing team can reach out to relevant decision makers within those companies.
Using AI to improve cybersecurity
We are living in a world where data is considered as the greatest asset of businesses and the average cost of a data breach is ≈4 million$. Due to these reasons, eCommerce companies should invest in AI security tools to protect their enterprise and customer data.
With so much potential, AI will be more impactful in the e-commerce area in the coming years. If you don’t use some of the solutions we mentioned to power your business, this may be a good time to start that. Just let us know the area you are interested in and we can help you choose the right AI partner for your e-commerce business: