The use of AI and ML are drastically changing the face of ecommerce

The use of artificial intelligence and machine learning are drastically changing the face of ecommerce

By Mike Scally, Chief Technology Officer, Luzern eCommerce, as seen in Internet Retailing.

Artificial intelligence (AI) and machine learning (ML) are becoming increasingly embedded into the online shopping experience, providing customers with a more personalised experience and helping ecommerce businesses get the most out of their sales and marketing efforts. One of the first applications of AI and ML in ecommerce was in the form of recommendation engines, something that any Amazon sellers or shoppers would be familiar with. The ability to suggest the right products to the right people provides dual benefits – increases sales and improves the customer experience. This application is still a key feature today.

Things move fast in this space. For example, with the use of chatbots, we now see AI and ML being used on a daily basis for online customer engagements. Robots are used to interact with customers at every step of their journey, providing them with the information they need, thus helping to automate the engagement process and streamline the buying experience. That said, this approach should be further balanced with an understanding that automated responses may not always satisfy customers, and access to a human may sometimes be necessary in order to ensure customer loyalty and retention. In my opinion, there needs to be a balance between technology and human-driven solutions to ensure that businesses can create a dynamic customer experience that maximises efficiency without compromising on expectations and quality of service.

We need to talk about Chat GPT

By now, practically everyone has heard about Chat GPT. Launched in November 2022, the chatbot was built on top of OpenAI’s GPT-3 family of large language models. The natural language processing tool, driven by AI, enables human-like conversations with chatbots and much more. This really is a game-changer for ecommerce professionals in so far as it enables them to quickly create product descriptions and other content for their listings. This cutting-edge tool uses natural language processing (NLP) and predictive analytics to generate relevant content in a fraction of the time it would take using traditional methods.

With Chat GPT, businesses can populate product listings with A+ Content quickly and easily. Product descriptions are generated based on data points such as brand, features, benefits, sales promotions, and more. These descriptions can be tailored to each individual listing for maximum impact. Additionally, Chat GPT allows users to include specialised SEO keywords and phrases into the automated text so that products will rank higher in search engine results pages. The ability to quickly create multiple Amazon listings is amazing. The technology can generate detailed, comprehensive descriptions for each listing that are tailored to a product’s features and benefits. By allowing users to instantly generate different versions of similar products with just a few clicks, we already see these AI tools helping with the tedious task of copy-pasting content from one listing to another. It’s no wonder these AI Chat tools are becoming popular with ecommerce businesses looking to save time and increase efficiencies. Again, ensuring a human oversees the interactions and outcomes will be key to ensuring brands build helpful, yet ethical chatbots.

The ecommerce CTO’s view of what’s next

One of the areas where I see AI and ML making a real impact on ecommerce brands and retailers is where it can be applied to improve the accuracy of demand planning and inventory forecasting. New technologies are being developed to help ecommerce businesses ensure that they have the right levels of inventory on hand to manage stock levels and ensure they can meet customer demand. Avoiding out-of-stocks can be a real challenge, but by having automated planning with ML capabilities, you can ensure that you’re not losing revenue due to stock outs and also ensure you don’t over invest in excess stock levels. Data-driven insights help identify, prioritise, and act on urgent issues.

Accurate forecasting means capturing the big-picture trends and seasonality of your business. Analytics engines use ML, algorithms, and AI to continuously improve forecast accuracy over time. This ensures that businesses are able to keep up with fluctuating customer demands and changing market conditions. With AI-powered analytics, you can adjust forecasts for individual SKUs and different channels, and as it “learns” it will become faster and more accurate. 

AI and ML are powerful tools for analysing customer behaviour to drive better performance, marketing, and overall profitability. Market Basket Analysis allows you to identify popular product pairings that may be hidden in complex orders. This can provide a valuable user experience through one-click purchasing, as well as increase profitability from multi-buy and bundling opportunities.

Propensity modelling is one of the most powerful tools utilised through ML. This technique examines customer data to identify those with the highest likelihood of converting or those who are the most likely to buy a product or service. By focusing resources on these customers, businesses can reduce their marketing costs and increase sales performance. With this knowledge in hand, businesses can use personalised messages to nurture leads through the sales process, leading to higher conversion rates. Additionally, companies can also leverage propensity modelling to reduce cart abandonment rates by providing timely and relevant reminders or offers when a customer leaves an item in their shopping cart.

AI and ML are powerful tools for analysing customer behaviour to drive better performance, marketing, and overall profitability. Market Basket Analysis allows you to identify popular product pairings that may be hidden in complex orders. This can provide a valuable user experience through one-click purchasing, as well as increase profitability from multi-buy and bundling opportunities.

Advertisers can use AI to analyse data and enhance their advertising campaigns. AI-powered models can help identify the best keywords and customer segments to target for maximum return on investment (ROI). Furthermore, these models enable algorithms to adjust bids in real-time to optimise spend.

Product pricing is an important part of a successful business strategy. AI can be used to gain a comprehensive overview of the product landscape, including where it is sold and the delivery lead times for each country. Moreover, AI-powered models can apply rules and logic to optimise sales and guarantee the best experience for customers.

I see AI and ML being used to help automate the generation of product content and translations. This helps to speed up the process of creating multilingual product descriptions and ensures that they remain accurate and consistent across all languages. In conclusion, AI and ML are transforming the e-commerce landscape and providing businesses with a range of opportunities to enhance the customer experience and boost sales. As the technology continues to evolve, e-commerce businesses should ensure that they are taking full advantage of the latest AI and ML applications in order to stay ahead of the competition.

Another key area is advanced security technologies, which are making online transactions more secure than ever before. AI and ML are also being used to help reduce fraudulent transactions. Sophisticated algorithms are able to detect patterns and behaviours that are indicative of fraudulent activity, helping to protect businesses from financial loss. 

Risky business?

I’m acutely aware that the use of AI in ecommerce can introduce both benefits and risks. On the benefit side, AI can bring great improvements as outlined above. However, on the riskier side is the potential for bias causing ethical implications within algorithms that are used to automate decision-making processes such as price setting or product recommendations. It is important for businesses to consider these risks when implementing AI into their operations. It is also important not to overcomplicate applications when simpler algorithms will suffice. This will help your organisation to provide quality customer service without needing to invest in overly complex AI or ML systems that could become difficult for staff members to work with or understand.

In my view

In summary, using AI and ML offers an abundance of opportunities. By ruling out those that don’t add value, ecommerce brands and retailers can increase their performance in regards to efficiencies, cost management, and customer satisfaction. Applying the right technology that delivers tangible benefits to customers is how ecommerce businesses can unlock the value of new technologies today.

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