Using AI to Mimic Human-Like Behaviors in E-CommerceBy Diego Páramo | Thu, 11/18/2021 - 12:51
Since starting our journey with EPICA, we have always felt that AI would allow us to mimic the way customers engage with physical retailers or companies that sell directly to consumers (DTC). Just think about the buying experience nowadays in e-commerce: the experience offered by these websites is far from the experience you have in a physical store.
But how can you make e-commerce more human-like?
Let’s start with the basics. If you go to a physical retailer, you will be helped by another human, right? This human will give you suggestions about the product that you want to buy. Those suggestions or recommendations are specifically tailored to your needs. But that requires you to provide that human with information to understand what you specifically need and provide the correct recommendations.
Typically, when the recommendations given by the salesperson serve the correct purpose and you buy the right product, you are satisfied with whatever you purchased. This type of experience is, by all means, unique, and if you think about it, not only will you have a happy customer but an amazing customer experience for any person who arrives in your physical store.
But how can you replicate this in the digital world? How can you make any e-commerce technology like Shopify, Magento, Big Commerce, Oracle Commerce Cloud or VTEX more human-like?
This is a huge challenge for AI developers and data scientists, not only because of the complexity of what human relations or engagement look like but also because replicating this with a machine is one of the most complex problems to solve in AI.
Technologies like NLU (Natural Language Understanding) and NLP (Natural Language Processing) will have a huge impact in solving this problem. Why? Because they can understand first-party data generated in your e-commerce store. Suppose you can capture and analyze the real-time behavioral and contextual data of your e-commerce and process it with advanced AI Algorithms. In that case, you can understand what each individual is doing on your website.
What this means is that ML algorithms can analyze each real-time interaction that is happening in your e-commerce store (clicks, scrolls, cart additions, purchases, geolocation data) and based on that individual data, it predicts what product has a higher likelihood of actually being purchased by each of your customers.
With that prediction, you can provide recommendations in real-time and personalize your
e-commerce store so it adapts to the preferences of each individual. What this means is that each individual navigating your e-commerce store will be experiencing a different site specifically tailored for them. These recommendations can mimic that physical experience we talked about earlier, thanks to the NLU and NLP algorithms. If you can give highly accurate recommendations, your customers will have a higher likelihood of buying.
This may sound a little bit like science-fiction but the fact of the matter is that, currently, many companies are using advanced machine learning algorithms to be more cost-efficient in all of their digital marketing and sales activities. Also, hyper-personalizing with intelligent “human-like” recommendations will help you have a higher CTR (click-through rate) and boost conversions.
These advanced machine learning algorithms also have a unique capacity: they are able to remember your preferences so that when you come back to that specific e-commerce store, you will always receive a brilliant, tailored experience made explicitly for you.
That’s how I see the not-so-distant future; e-commerce and AI working together to make a more human-like experience for all.