Feature
The Gist
- AI powerhouse. AI for personalization enhances individualized ecommerce experiences.
- Tech advantage. Machine learning dynamically adapts prices, boosting consumer appeal.
- Customer support. AI-enabled chatbots provide personalized, emotionally intelligent assistance.
Attention ecommerce brands: The days of blanketing consumers with vaguely relevant ads are over.
Seven out of 10 consumers now expect brands to personalize ads and product recommendations, and 76% get frustrated when this doesn’t happen, according to McKinsey research.
In response, nine out of 10 businesses, including Coca-Cola, Netflix and Sephora, are investing in the practice of using artificial intelligence (AI) for personalization to give consumers a one-to-one experience, or something close to it.
In a nutshell, personalization in ecommerce uses data to show customers products and deals tailored just for them. Instead of asking shoppers to sift through a list of products, personalization uses a customer’s purchase history and browsing behavior with the brand to suggest the most likely item that person would buy.
To return the favor, 78% of consumers are likely to make repeat purchases from companies that personalize, according to the same McKinsey report mentioned above.
Yet personalization will only boost customer satisfaction, brand loyalty and sales if it’s executed precisely. And to do that requires culling insights from droves of customer data that humans simply cannot process and analyze manually.
And this is where artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) come into play for ecommerce brands.
AI for Personalization in Ecommerce
Personalization in ecommerce is still possible without AI, but it relies on grouping customers into “personas” based on shared demographics or interests. While this is an adequate approach, 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, using advanced algorithms to scan volumes of customer data and deliver information to you based on your own specific behavior.
“AI’s ability to process data in real-time and adapt on the fly to create personalized experiences is a key advantage for ecommerce brands,” said Kristin Smith, managing director and retail commerce lead at Deloitte Digital. “It also helps that AI isn’t prone to human mistakes and can work 24/7.”
With advanced personalization now expected by the majority of consumers, ecommerce brands have a variety of ways to utilize AI to deliver tailored shopping experiences. Here are three of them.
Related Article: AI’s Role in Digital and Retail Personalization, Part 2: Changing Rules of Engagement
1. Product Recommendations for the Individual
One of the clearest examples of using AI for personalization are the tailored product recommendations we see in emails or when logging on to our favorite ecommerce brand’s web site.
Here, complex machine learning algorithms mine your previous purchases, cart adds, product reviews, and product interactions, and generate personalized product recommendations in real time.
This customer data becomes the basis for training an algorithm that continues to learn and improve on the accuracy of recommendations as it receives new data.
Example to Emulate: Netflix
Netflix is a recommendation trailblazer. The streaming giant’s recommendation engine, called NRE (Netflix Recommendation Engine), uses algorithms to analyze data from each member’s viewing history and generates hyperpersonalized movie and TV show recommendations.
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Related Article: AI’s Role in Digital and Retail Personalization, Part 1: The Big Picture
2. Automated Dynamic Pricing
Constantly adjusting product prices is a necessary but time-consuming task. By incorporating machine learning into pricing, ecommerce brands can automatically adjust prices in real time based on their own manufacturing costs, competitor’s prices, market demand and seasonality.
AI-based dynamic pricing benefits consumers by:
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Monitoring the competition and adjusting prices to ensure customers get a fair price.
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Offering real-time personalized discounts based on a customer’s behavior. For instance, if a person continually shows interest in a product, a dynamic pricing algorithm could entice that person with a time-limited discount.
Example to Emulate: Amazon
Amazon is the king of AI-based dynamic pricing. The ecommerce giant uses machine learning to update the prices of millions of products several times every day. Its repricing algorithm factors in product demand, stock availability and customer behavior. This allows Amazon to consistently offer the most competitive prices.
3. Personalized Customer Support via AI-Powered Chatbots
Using NLP and sentiment analysis, today’s chatbots understand not just text but also the emotion behind customer support requests.
When you combine sentiment, access to customer data and speedy responses, it’s easy to see why chatbots are now a personalization tool. Today’s chatbots can greet customers by name, recommend products and discounts based on purchase and browsing data, and even help customers complete online purchases.
Example to Emulate: Sephora
Most ecommerce chatbots can handle rudimentary customer inquiries, but the more innovative chatbots also serve as shopping assistants.
Cosmetics retailer Sephora is a prime example. Sephora’s website chatbot answers questions about returns and exchanges. But it’s also a virtual assistant that asks customers questions about their skin tone and makeup preferences and then gives tailored recommendations.
The Big AI Personalization Challenge: Relevant Data
The benefits of using AI for personalization are clear, but the success of your strategy hinges on your data.
Kristin Smith of Deloitte recommends that ecommerce brands ask themselves the following questions regarding customer data:
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What is the quality and source of the data your brand is trying to use?
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Does the brand have permission to collect and use the data they have?
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How actionable and granular is the data?
“Many organizations have customer data only at a high level,” Smith said. “But high-level, demographic data does not always translate to actionable insights for personalization.”
In addition to having the skilled staff in place to implement and maintain AI tools, the entire marketing and data team should always ensure that the data the AI algorithms are using is unbiased and specific enough to actually help the customer connect with your brand and buy from you consistently.
“There will be a rabbit hole of ideas for data points AI can collect for personalization,” said Derric Haynie, head of demand generation at Pipe17 and co-founder of Ecommerce Tech.
“Maybe you're going to test new products based on previous purchase history. Or test personalized emails based on when customers last visited the site. There's a lot to personalize, and the nature of personalization is recognizing each person has a different customer journey, and catering to it.”
