The role of artificial intelligence in eCommerce

The global AI market size was valued at $328.34 billion in 2021. A report published in Fortune Business Intelligence projects the market to reach an outstanding market value of $1394.30 billion by 2029, growing at a CAGR of 20.1%. [1]

These statistics are no surprise considering that AI has permeated every aspect of our lives. There seems to be no limit to artificial intelligence use cases, from AI-powered voice assistants to automated cars.

AI is already reshaping the eCommerce industry. According to recent statistics, AI will manage up to 95% of customer interactions on eCommerce platforms by 2025[2]. Currently, e-commerce businesses leveraging AI capabilities can analyze business and customer data to streamline business operations, offer personalized services, and accurately predict market dynamics.

This article will explore the role of artificial intelligence in eCommerce, including artificial intelligence use cases, how to leverage AI in eCommerce, and the benefits it presents to the industry.

Artificial intelligence (AI) use cases in eCommerce

Over the past couple of years, numerous businesses across the eCommerce sector have adopted AI capabilities. According to a Gartner report, the number of businesses utilizing AI in eCommerce grew by 270% between 2015 and 2019[3]. Additionally, four out of ten organizations plan to utilize AI capabilities in their business [4].

Major eCommerce platforms like Amazon and Ebay utilize AI in product upselling and cross-selling across their platforms. These applications include personalized product recommendations, customer interactions through AI chatbots, and retargeting potential customers.

Let’s have a look at the most important AI use cases in eCommerce:

Improved personalization across different devices

AI-powered technologies provide a deep level of personalization that is poised to revolutionize product and service offerings in the fast-growing eCommerce world. AI-based personalization takes a rather different approach to traditional methods by focusing on a multi-channel approach.

Take Boomtrain, for instance. The AI engine analyzes different customer touchpoints across multiple channels, including websites, mobile applications, and email campaigns, to create a universal customer view [5]. With such applications, eCommerce businesses can deliver a seamless customer experience across multiple channels.

AI-enabled smart solutions

AI-powered technologies are introducing customers to a wide range of products suited to their specific needs. Sentient Technologies [6], an AI-based technology company, for example, has a program that can recommend new products to online shoppers based on their previous purchases and data insights from their interactions with eCommerce platforms.

Amazon, one of the most successful eCommerce giants, is also introducing Alexa Voice Shopping. The voice assistant can let customers review the best deals on Amazon and place shopping orders with their voice. The chatbot can also provide wardrobe tips, including the latest fashion trends, best wardrobe combinations, and a comparison between outfits to help customers decide what would look better on them [7].

Increased utilization of AI in the fashion eCommerce industry has significantly reduced the number of returned goods from online purchases. For instance, Zara, a fashion brand, is leveraging AI capabilities to suggest the best apparel size based on a customer’s measurements along with their style preferences [8]. Since the company started using the program, it has seen a significant reduction in product returns and an increase in return purchases.

Enabling customer-centric search results

The average rate of cart abandonment across eCommerce platforms is just under 70%[9]. This translates to more than $18 billion in losses each year [10]. Most customers abandon their carts because the products displayed are often irrelevant.
To curb this problem, Twingle[11] has developed an AI-based program that enables search engines to think like humans through analytics and natural language processing. By narrowing and contextualizing search results, the program can significantly improve search results for online shoppers.

Another example of a business improving search results on eCommerce platforms is the US-based startup Clarifai. The company takes a different approach from other companies by focusing on the visual elements of the search [12].

Clarifai enables developers to build smarter apps that empower eCommerce businesses to develop a customer-centric experience through advanced video and image recognition. Pinterest is also leveraging a similar technology through its recent Chrome extension update that enables customers to search for any product online by simply selecting a photograph.

Read more about AI in Digital Marketing: How To Use Data for Better Customer Experience, Customer 360

Re-ranking products in real-time

AI-powered product search and discovery platforms can automatically re-rank products in real-time to show products that are more likely to lead to a conversion. For instance, if a product outperforms similar products in the same category, the product will begin to move up in the rankings, thus leading to higher conversion rates. This way, customers are not limited to relevant results but also get to see the most impressive ones.

AI-assisted merchandising

Almost all eCommerce websites have tons of search queries that should present a significant number of results, but for some reason, they don’t. In most cases, this is due to a misspelling or use of a synonym not detected by the system.

Legacy systems traditionally required merchandisers and engineers to manually set up redirects and synonyms for multiple products. But with the ever-changing customer behavior, coupled with the constant streaming of new additions to product catalogs, this mode of operation is quickly becoming a painstaking and time-consuming process.

Instead of tasking engineers with building extensive manual rules that only serve as band aids to an ever-growing problem, eCommerce businesses can leverage AI technologies to improve their product discovery systems.

Retargeting potential customers

Marketing teams fail to follow up on at least 33% of marketing leads [13]. This means eCommerce stores lose up to one-third of their business through invisible cracks. Despite the current upsurge in business and customer data, most businesses don’t take advantage of the perpetual goldmine that could potentially enhance their sales cycle.

Some retail stores are already using AI-powered facial recognition software to curb shoplifting and monitor the average time customers spend in their physical stores. For instance, if a customer lingers around a particular product section, that could be a positive indicator of their interest in the product. In that case, the store may also invest in marketing the product to other customers.

As AI capabilities develop, experts will be able to come up with special offers based on customers’ in-store dwell times. This way, businesses will change their sales strategies by responding directly to customer interests.

Creating a more efficient sales process

Traditional cold-pitching sales methods have become obsolete. According to research, the average person spends almost 40 hours of their waking life online. [14] That’s why businesses must tailor their marketing solutions to integrate with the platforms their potential customers spend the most time in.

By integrating artificial intelligence into a business’s CRM, marketing teams can reach their customers directly. Several AI systems like Siri and Alexa enable natural language processing and voice input. This allows CRM systems to answer and solve customer queries. The data gathered can also help the sales team identify new opportunities.

Take North Face [15], for instance. This large eCommerce retailer is stepping up its game using artificial intelligence to better understand its customers. The company enables its customers to find the perfect jacket in the store using Watson, an IBM AI solution.

Improving personalization with chatbots

Chatbots are computer programs that simulate human conversations on internet platforms like websites. These programs can take on some of the responsibilities of running an eCommerce store, like marketing and handling customer queries.
Additionally, chatbots can automate order processes and offer a more efficient and cost-effective way of providing customer service. This is especially vital in a market where 81% of customers say that a positive customer service experience increases their chances of making another purchase [16].

Some systems require integration with shopping carts in order to retrieve information such as shopping items, product details, and product quantities. Chatbots can retrieve this information and provide customers with accurate information.

Optimizing pricing strategies

Matching the right price with the right customer at the right time has the same effect as placing the right product in front of the right customer. In both cases, you get to drive home the sale.

Companies have been altering prices based on customer demand for quite a while now. By merging customer data with AI capabilities, eCommerce businesses can make informed decisions on how much customers are willing to pay for certain products.

Retailers generally use two strategies to set dynamic pricing:

Predicting the best price for each product: Numerous online businesses are already using AI to alter product rankings. In the same way, businesses can use AI to alter the prices of certain items based on the likelihood of customers purchasing the products when they see certain price points. The strategy typically analyses customer interactions on the platform and sets a universal price for products to achieve maximum sales volume and revenue.

Presenting each customer with the best price: AI systems can look at and analyze what a particular customer clicks, adds to their cart, and purchases. The systems can then alter the item’s price to increase the likelihood of a purchase. For instance, AI systems can entice first-time customers with lower prices or give more price-sensitive customers slightly lower rates.

Inventory management and sales forecasting

Online store businesses often have to deal with challenges emanating from ever-changing customer demands, evolving product catalogs, and supply chain disruptions. Trying to figure out how much inventory you need or have on hand manually can be very tasking.

However, with an AI platform specifically designed for online store inventory management, you can take the guesswork and tedious tasks out of the equation. Artificial intelligence in eCommerce provides a deep analysis of customer data, such as seasonal buying behavior, which allows businesses to predict how much stock they need accurately.

Benefits of artificial intelligence in eCommerce

Seamless automation

Automation in business typically means leveraging systems that help you accomplish tasks with little to no human intervention. Artificial intelligence in eCommerce can handle everything from scheduling emails on a CRM to leveraging technology to help with employee onboarding.

AI can also help eCommerce stores automate repetitive tasks vital to the stores’ operations. These include things like loyalty discounts, product recommendations, low-level customer support, and more.

More targeted marketing and advertising

Despite being a priority in eCommerce, only a few retailers have implemented personalization across channels. To stand out from the crowd and gain a competitive advantage, you need to send more personalized messages and interact with your customers more frequently.

AI in eCommerce has enabled deep personalization techniques that offer customers custom content. By analyzing customer data from purchase history and other customer interactions, businesses can zero in on what customers really want and deliver messages that resonate best with their target audience.

It might be interesting for you: How Artificial Intelligence Is Transforming Influencer Marketing Industry 

Reduced operational expenditure

One of the biggest perks of using artificial intelligence in eCommerce is making automation more flexible and comprehensible. Businesses that leverage AI capabilities can automate more tasks, thus reducing reliance on human labor and operational costs.

Numerous AI implementations are already in place specifically designed to reduce capital-intensive and repetitive business processes. Even AI applications that are not specifically designed to reduce costs do so indirectly by simply being more economical to operate than human workers are.

One of the most common examples of using AI in eCommerce to reduce operational expenditure is processing customer data for marketing purposes. Businesses relying on human employees for these processes incur significant expenses and are more susceptible to errors.

Increased productivity

The benefits of using artificial intelligence in eCommerce go beyond automating processes and reducing operational expenditure. They also spill into daily operations within the business. Automation significantly reduces employee burden by taking over numerous repetitive business tasks like fraud detection and prevention, document management, and business intelligence. This way, employees have more time on their hands to focus on other business operations.

Reduced reliance on human personnel

Communicating with customers is vital in any B2C business. However, online shopping has different needs compared to shopping in a brick and mortar store. Although customer assistance is still required, online stores leveraging AI technologies like chatbots don’t necessarily require human personnel to do it.

By leveraging AI capabilities for customer support, inventory management, and purchase processing, online stores significantly reduce the need for human labor, which costs much more than AI technologies.

Application of artificial intelligence in eCommerce

Jumping on the latest trends can seem enticing. However, when you consider the monetary risks associated with the improper implementation of expensive AI systems, it pays to have a road map to get you there. Here’s how to implement AI in eCommerce.

Create a strategy

Before you begin your implementation process, you have to consider what you want to accomplish with your AI system. Once you’ve figured that out, you can devise a strategy to help you achieve your AI goal.

Focus on artificial intelligence use cases relevant to your business strategy

For artificial intelligence in eCommerce use cases to be successful, they have to fulfill business objectives and data differentiation and be readily available models with proven efficacy. Therefore, you should focus on revenue-generating AI use cases in eCommerce for scenarios appropriate to your business strategy.

Leverage third-party expertise

Unless you are an expert on AI technologies, you need to bring in a third-party team to help you implement your project.

Build a full-scale solution

After reviewing the results of what your team has produced, you should move on to building a full-scale solution. It might take some time before your AI system works as intended at first, but with a few alliterations and some getting used to it, your team will be more comfortable with working with the AI system.

Final thoughts on artificial intelligence (AI) use cases

AI has permeated nearly every aspect of our lives, and the eCommerce sector is no exception. All major retailers leverage AI capabilities to boost sales, improve customer experiences, and streamline business operations.

Over the next few years, AI will revolutionize online shopping in nearly every aspect, from product recommendations to pricing and how customers interact with eCommerce platforms. See our AI consulting services to find out more.

References

[1] Globenewswire.com. AI Global Market Size to Reach $30 Billion by 2029. URL: https://bit.ly/3N7ifka. Accessed October 23, 2022
[2] Financedigest.com. AI Will Power 95% of Customer Interactions By 2025. URL: https://www.financedigest.com/ai-will-power-95-of-customer-interactions-by-2025.html. Accessed October 23, 2022
[3] Gartner.com. Survey Shows 37% of Organizations have Implemented AI in Some Form. URL: https://gtnr.it/2RBBWqO. Accessed October 23, 2022
[4] Adobe.com. 2019 Digital Trends: Financial Services in Focus. URL: https://adobe.ly/3SxI3Y1. Accessed October 23, 2022
[5] Zetaglobal.com. URL: https://zetaglobal.com. Accessed October 23, 2022
[6] Wikipedia.com. Sentient Technologies. URL: https://en.wikipedia.org/wiki/Sentient_Technologies. Accessed October 23, 2022
[7] Amazon.com. Alexa Voice Shopping. URL: https://amzn.to/3Nc8CRh. Accessed October 23, 2022
[8] Econsultancy.com. How Zara is Using in-Store Tech to Improve its Frustrating Shopper Experience. URL:  https://econsultancy.com/how-zara-is-using-in-store-tech-to-improve-its-frustrating-shopper-experience/. Accessed October 23, 2022
[9] Bymard. com. Cart Abandonment Rate. URL: https://baymard.com/lists/cart-abandonment-rate. Accessed October 23, 2022
[10] Dynamicyield.com. Shopping Cart Abandonment. URL: https://bit.ly/3Fo9dxD. Accessed October 23, 2022
[11] Github.com. Twingle. URL: https://github.com/samskivert/twingle. Accessed October 23, 2022
[12] Clarifai.com. URL: https://www.clarifai.com. Accessed October 23, 2022
[13] Conversica.com. Study Finds Sales and Marketing Wasting 50% of Time and Money on Leads. URL:  https://www.conversica.com/study-finds-sales-and-marketing-wasting-50-of-time-and-money-on-leads/. Accessed October 23, 2022
[14] Datareportal.com. Time Spent Using Connected Tech Continues to Rise. URL: https://datareportal.com/reports/digital-2022-time-spent-with-connected-tech. Accessed October 23, 2022
[15] The Northface.com. URL: https://www.thenorthface.com/. Accessed October 23, 2022
[16] Cdn2.assets-served. Zendesk CX Trends, 2022. URL: https://bit.ly/3FmKmKm. Accessed October 23, 2022