AI-Powered Shopping: The Next Frontier in Personalized Recommendations
Word of mouth recommendations have always been one of the key contributors to consumer decision making. Whether it’s ratings and reviews or recommendation from a friend, could sway consumer’s decision on buying a car or toothpaste.
Later online shopping became wildly popular driven by convenience and frankly, Amazon. With the massive increase in online shopping, recommendations needed to evolve as well. Ecommerce recommendation engines have come a long way since their inception as rigid rules-based tools. These intelligent models have morphed over the years to consider and analyze individual consumer behaviors, providing a personalized shopping experience for users in the ecommerce industry.
Origins of Recommendation Engines in Ecommerce
Originally, ecommerce recommendation engines operated on basic rules, suggesting items based on simple criteria like popularity or category. Amazon popularized this approach even though sometimes it resulted in “funny” recommendations such as “people who looked at lawn movers also bought underwear”.
These early systems lacked the sophistication to understand individual preferences and behaviors, often leading to generic and ineffective recommendations as they used rigid rules based models where recommended products were often locked into “slots” within the decision tree.
Despite their limitations, these rudimentary recommendation engines laid the foundation for the evolution towards more intelligent models and demonstrated that recommending related products in context drove significant increase in revenue and started to influence consumer behavior.
Transition to Behavioral Analysis
The turning point in the evolution of ecommerce recommendation engines came with the integration of behavioral analysis. By tracking and analyzing user interactions, such as browsing history, search queries, and purchase patterns, recommendation engines began to tailor suggestions to individual preferences. Several software and ecommerce companies have delivered solutions that enabled significantly more accurate recommendations. One such company is Adobe with their Target product.
This shift marked a significant improvement in the relevance and effectiveness of product and content recommendations, enhancing the overall user experience. With increased computing capacity running multi-variate testing on different recommendations and behavior became easier and accessible to marketers and merchandisers who might not have the statistical mathematical background to perform the analysis.
These new solutions allow marketers and merchandisers to create various scenarios and let the software generate all the possible combinations for testing. Different versions of the images, headlines, copy and call-to-action tested create a large number of combinations for testing in order to find the best presentation of a product and recommendations for the visitor and track the results over time.
Adoption of Machine Learning and AI
With advancements in machine learning and artificial intelligence, ecommerce recommendation engines have become even more sophisticated. These intelligent models can now predict user preferences with a high degree of accuracy, leveraging algorithms to analyze vast amounts of data and generate personalized recommendations.
Exponential increase in volume, breadth and depth of consumer behavior data collection has created marketplaces for secondary data that companies can leverage. Imagine a scenario where a travel website can access segmentation data from a third party helping them to prioritize luxury items for high net-worth visitors using a machine learning (ML) algorithm.
Through continuous learning and adaptation, recommendation engines can dynamically adjust their suggestions based on real-time user behavior, improving engagement and driving conversions. Machine learning based tools still have the disadvantage of being rules based exhibiting potential bias coming from the training data sets as well as continuing behavior of visitors.
Advent of Conversational Recommendations
The most promising approach is leveraging large language models (LLMs) with a chat based interface and natural language processing (NLP). A combination of user agents, pre- and post-processing algorithms and LLM based AI models provides a superior user experience in many cases. The LLM foundation model gets trained with product catalog and other related data sources building a model that can provide relevant output in context.
Latest approach to recommendation chat bots can recover past conversations and context and continue “discussion” like it happened mere seconds ago. Imagine an online store for pet products where you can continue conversation about your dog’s diet and diverse preference for food and other products asking questions like “Coco doesn’t like the last wet food I bought. Can you recommend something else that has all the nutrients she needs” and the recommendation engine being able to parse the past purchases, infer preferences for chicken and pick up food products based on Coco being a small breed 3-year old dog.
OpenAI and Microsoft have been considered leaders in the area of LLMs but both AWS and Google are catching up rapidly. AWS Bedrock together with Anthropic’s Claude have shown great promise in delivering fact based results. AWS Bedrock also has the added benefit of being able to run the models in virtual private instances. Google just rebranded their approach calling it Gemini AI which also shows interesting approach with multimodal (image, video, sound, text, code) support.
Impact on Retail Ecommerce Professionals
For retail ecommerce professionals, the evolution of recommendation engines presents both challenges and opportunities. By embracing these intelligent models, professionals can enhance their understanding of consumer behavior, optimize product discovery, and increase sales. Latest innovation around LLM based solutions can radically change the approach to recommendations and help merchandisers create human interactions that drive high level of conversions.
However, with the increasing scope and complexity of recommendation algorithms, professionals must also navigate ethical considerations, such as data privacy and algorithm transparency, to maintain trust and credibility with consumers. There’s also a need for merchandisers and marketers to continuously evaluate and monitor AI produced conversations and outcomes as there’s always a risk for model hallucination.
LLM based conversational approach is not going to entirely replace but rather supplement ML based models. ML based decision trees will have a role in situations where consumers have a clear idea what they are looking for such as electronics. When it comes to products which are less structured and require additional context before recommendations, LLM based conversational approach will become the norm.
In conclusion, the evolution of ecommerce recommendation engines exemplifies the industry's commitment to enhancing the online shopping experience through personalization and data-driven intelligence. Retail ecommerce professionals must stay abreast of these advancements to leverage the full potential of recommendation engines in driving business growth and customer satisfaction. Partnering with an AI experienced integrator like Kloud9 will significantly reduce time to market and deliver exciting new ways to engage your consumers.
