Providing hyper relevant shopper experiences at scale is one of the most important, and most difficult, ecommerce challenges I see online retailers suffer from. A recent survey of search practitioners showed that 100% ranked search relevance as highly important, while 96% said it is difficult to deliver. This complexity is commonly attributed to gaps in data and signals required, such as poor product data and limited behavioral analytics. Fortunately there are a plethora of AI based technologies that can compensate for these issues.
Consider AI solutions that are purpose built to address specific channels and aspects of the customer journey. Significant value can be achieved when implementing the right tool at the right time.
Initial Visit & Direct Traffic: Research shows that you have eight seconds to captivate a shopper before they leave. It is critical to present a relevant experience instantaneously that requires no effort from the user. Predictive personalization and intent tools can automatically curate the experience and results using machine learning models. These will instantly align product and content based on the shoppers context, behaviors, similar shopper behaviors, and any historical behaviors. These tools offer configurability to target based on the KPI’s most important to you such as user engagement, conversion, and revenue.
Social Media: AI tools such as semantic extraction can use shoppers’ likes, interests, posts, and examine the content within to determine what products are ideal for them. Computer vision as well as AI tools for social media can be used in the context of YouTube/Tiktok/Instagram to both extract the intent of the shopper and to connect them with the appropriate products through images and video frames. Social media plugins such as messenger apps can incorporate technologies such as GPT to engage the shopper by generating a relevant dialogue.
Organic Traffic: Tools like generative AI, large language models and semantic vector search can enrich the information value of catalog and landing pages, product data, content, and more to help drive organic search engine traffic.
Email and Text Campaigns: For campaigns through email or SMS, using relevant AI tools can help determine the audience, when to send, generate and personalize content, and uncover response insights.
Ads: Finally, there are numerous tools to help drive return on ad spend (ROAS). These determine the optimal keywords to buy, when to buy them, and even suggest or generate the content of your ads and landing pages to drive the highest ROI.
Once the shopper is within your site, applying the correct AI tools for the job still applies. Using search as an example, AI-based technologies such as natural language processing (NLP), relevance algorithms, personalization, semantic vector search, recommender algorithms, chatbot, speech to text, and computer vision are just a handful of critical technologies.
AI is also opening more doors to the way shoppers can search. Computer vision based models can be used to support search using photos or image uploads. Speech to text based models can be used to support voice based search commands. These can be combined with large language models such as ChatGPT to drive a more interactive guided selling or search experience as well as for self service applications in ecommerce.
88% of search practitioners believe that AI is very or extremely important in the future of search. While AI will not solve all search issues, harnessing what it does best—complex processes at scale—is a natural application to the immense data sets and models that help make the customer experience more relevant for your most valuable customers.
Sanjay Mehta is Head of Industry, Ecommerce at Lucidworks and has worked with thousands of ecommerce customers globally in his 20 year history in the space. This includes tenure leadership roles at major ecommerce technology players including ATG, Endeca, Hybris, Oracle, Netsuite, and Reflektion. His focus areas include digital experience, search, personalization, and improving ecommerce results through applied ML/AI.
