Artificial Intelligence in eCommerce Personalization

by Bryan Teo

Artificial Intelligence in eCommerce Personalization

Technology is rapidly changing

It’s no secret that technology is evolving at breakneck speeds. This effect has been the most visible in recent years with the increased popularity of Artificial Intelligence (AI) everywhere.

These technologies are projected to change the way the world works, impacting everything from a macro to micro scale. With how much people are talking about it nowadays, AI is a topic that is constantly debated. Some will support AI until they’re in their deathbeds, while others would vow that AI is the end of the human race.

Regardless of which camp you’re in, it’s still important to understand its uses and impact. AI can be used almost anywhere, especially so in eCommerce personalization.

But first, for those that aren’t too familiar, what exactly is AI? And relatedly, what is Machine Learning (ML)?

Artificial intelligence, or AI, is technology that enables computers and machines to simulate human intelligence and problem-solving capabilities. On its own or combined with other technologies (e.g., sensors, geolocation, robotics) AI can perform tasks that would otherwise require human intelligence or intervention. Digital assistants, GPS guidance, autonomous vehicles, and generative AI tools (like Open AI's Chat GPT) are just a few examples of AI in the daily news and our daily lives.

In that realm, machine learning (ML) is a branch of AI. It focuses on using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy.

How is AI being used in eCommerce personalization?

With the flexibility and usability of AI, it should be no surprise that it’s being used in eCommerce personalization as well. Personalization is quickly becoming a necessity for all online shoppers.

According to McKinsey, 71% of consumers expect personalization while 76% of consumers get frustrated when they don’t find it. That’s a very large majority of consumers who are expecting some sort of personalization in their shopping experience.

With how highly sought after it is, the introduction of AI was to help make personalization easier. While personalization in eCommerce is still possible without AI, it relies on grouping customers into “personas” based on shared demographics or interests. This may be an adequate approach, but today’s consumer can sniff out when they’re being marketed to as a persona rather than an individual.

AI-based personalization is much more specific. It uses advanced algorithms to scan volumes of customer data and delivers information to you based on your own specific behavior.

The uses of AI in personalization

There are various examples of how AI is already being used in personalization.

1. Recommendation Systems

An AI-powered recommendation system is an ML algorithm trained to rank or rate products or users. It is designed to predict the ratings a user might give to a specific item and then return those predictions to the user in a ranked list.

This technology is currently being used by many popular companies such as Google, Amazon, and Netflix to increase user engagement with their platforms. The most obvious example here would be with Spotify. Spotify’s platform frequently recommends songs similar to ones you have previously listened to or liked to keep you using their platform to listen to music.

There are many ways to build a recommender system for your business. The ways can vary from algorithmic and formulaic to modeling-centric. These approaches include page rank, collaborative filtering, content-based, and link prediction. However, it is important to note that complexity does not necessarily translate to good performance. Often enough, simple solutions and implementations yield the strongest results.

By leveraging AI, these systems can identify intricate patterns and trends within consumer data. The data includes browsing and purchase history, preferences, and behavior. This allows the AI to generate highly personalized product recommendations.

Integrating AI in product recommendations enhances customer satisfaction and increases sales by presenting users with items that align closely with their tastes and needs. AI algorithms’ continual learning and adaptation capabilities further refine suggestions over time, ensuring a dynamic and effective recommendation system for optimizing the shopping experience.

2. Predictive Analytics

Predictive analytics is the use of data to predict future trends and events. It uses historical data to forecast potential scenarios that can help drive strategic decisions.

To date, predictive analytics has been possible and has been used prior to the development of AI models. The drawback however, is that manual predictive analytics methods can take hours to perform based on a couple of hundred data points.

As such, AI is used as it’s more effective at performing predictive analytics. It is able to collect, organize, and analyze data quickly. AI predictive analytics models will be able to generate a solution based on millions of data points in a matter of minutes.

AI in predictive analytics enables businesses to predict customer behavior, needs, or wants and then precisely tailor offers, products, and messages to each recipient. This can be done across channels and touchpoints.

These messages differ greatly from traditional manual segmentation and personalization tactics. This is because they are based on insights revealed through automated data-driven algorithms, and are continually optimized through AI and ML.

3. Customer Segmentation and Clustering

As we spoke about, segmentation and clustering is a key step in personalization. Customer segmentation helps to simplify your entire audience into a few groups that you can target, manage, and convert.

When we think of traditional customer segmentation, basic demographics come to mind. Location, occupation, gender, and other characteristics define the foundation of our targeting. There’s a key assumption at play here: all people in that demographic act the same. However, we know that’s not the case.

AI-driven customer segmentation is the modern-day solution to overly simplified, traditional segmentation tactics. It considers various variables, including purchasing behavior, online interactions, browsing history, sentiment analysis from social media posts, and more. Essentially, it allows you to target customers with much greater precision.

With AI-powered customer groups, marketers can deliver personalized customer experiences that increase engagement, loyalty, and higher conversion rates. Marketers can thus better understand their audience and drive more impactful marketing campaigns.

4. Dynamic Pricing

Pricing is a laborious task to upkeep. We live in a new age of pricing, and that requires a new set of tools.

Persistent cost inflation, lingering supply chain volatility, ongoing shifts in consumer spending, and intensifying price competition. These are but some of the factors that have created a new level of complexity in pricing. This is more than retailers can manage using traditional retail price-setting tools and methods. As a result, retailers are now implementing AI-powered solutions and dynamic pricing models.

AI solutions have enabled retailers to transform the complexity of their markets from an obstacle into a valuable resource. Some merchants have increased gross profit by 5% to 10%, while also sustainably increasing revenue and improving customer value perception.

Through the use of AI, retailers can hence set optimal prices for each product and store. They are able to respond dynamically to both internal and external changes, while maintaining alignment with a clear customer-centric pricing strategy.

5. Chatbots and Virtual Assistants

As an eCommerce merchant, it is becoming increasingly important to keep consumers engaged. You have your own store, your social media, and all the other digital touchpoints you’ve established. Keeping up with conversations at different stages of a customer journey is only getting tougher by the day.

This is where eCommerce chatbots step in.

Chatbots have become one of the top eCommerce trends. The global chatbot market is expected to be worth $15.5 billion by 2028. This growth will be led by consumer demands for self-service and 24/7 customer support.

There are many reasons for this. The first being that customers want to be able to speak to businesses. Since a lot of shopping has gone online, there is a lack of touch and feel of a product before making a purchase. However, people still want to be able to talk to brands before making a purchase. A chatbot helps your business do that. Talk to customers at a time and place they choose, and assist them in making purchases or in addressing their anxiety.

Another reason is with multichannel sales becoming the norm. It is the only way for eCommerce businesses to keep up with consumers and meet their demands on a platform of their choice. Businesses thus need to use chatbots to keep up with customer conversations across channels.

To keep up with the digital demand, another solution would be to hire a team for customer experience. However, more team members is not an option if your business is optimizing for your costs and budgets. Additionally, consumers now tend to demand instant replies. This makes it tough for a team to handle the new-age consumer. That’s where chatbots come in.

Chatbots are easy to scale, handling thousands of queries a day, at a much lesser cost than hiring as many live agents to do the same.

The challenges of implementing AI in eCommerce personalization

The list of benefits in using AI for personalization is boundless. And as with all things, there are 2 sides to a coin.

1. Data privacy concerns

Though AI-powered automation and analysis can help you monitor regulations and compliance, how AI-powered systems use data also complicates compliance.

There have been large-scale global efforts in the past decade to strengthen data privacy laws. 71% of countries have data privacy legislation, and businesses are struggling with data security challenges.

Data legislation passed in recent years affects how you’re required to handle customer data. This has implications for any AI initiatives your business introduces. The EU’s General Data Protection Regulation (GDPR) law is one of the most stringent data privacy regulations. Not only that, various states in the US have or plan to pass more regulations.

This means that it’s only going to get harder for businesses to feed data to their AI-powered systems. What data is used, how the data is procured and processed, and more are going to be key talking points moving forward. It is hence important for you to understand the regulations and landscape in place, especially in your own region.

2. Integration with existing systems

As a business you probably already have systems and workflows in place. For some, it might already be pretty technologically advanced, while for others it may be simpler. Regardless of which, implementing new AI-powered systems into your existing systems will take work.

As many as 90% of organizations remarked that there is difficulty integrating AI with other systems. Almost universal, 98% of IT leaders also report facing challenges regarding digital transformation. The key drivers are the persistence of data silos at 81% and the fragility of tightly coupled and highly dependent systems at 72%.

3. Training and up-skilling employees

Back in 2019, the Organization for Economic Co-operation and Development made a bold forecast. Within 15 to 20 years, it predicted, new automation technologies were likely to eliminate 14% of the world’s jobs and radically transform another 32%. Those were sobering numbers, which didn’t even factor in ChatGPT and the new wave of generative AI that has taken the market by storm.

Today, advances in technology are changing the demand for skills at an accelerated pace. As workers carry out their daily tasks, many may well discover that AI and other new technologies have so significantly altered the nature of what they do that in effect they’re working in completely new fields.

Due to this, businesses need to increase their focus on not only up-skilling but re-skilling employees as well. Companies have a critical role to play in addressing this challenge, and it’s in their best interests to get going on it in a serious way right now.

So if your business is looking to integrate new AI technologies, it is important to consider the perspectives of the people operating them. It’s easy to look at the potential benefits of a system and push for its integration. However, the operational factor is a key factor to be considered. Businesses need to ensure that their employees are well equipped to be using these systems, or at least have plans in place to teach them how to do so.

AI is the gift that just won’t stop giving. It has changed the world in numerous ways, and will continue to do so in years to come. It’s important to understand these changes, and stay ahead of the curve.

Interested in learning how to procure data that won’t put your business in danger of breaking data privacy regulations? Learn more about zero-party data, or book your demo with Fairing today.


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