Nowadays, buyers aren’t usually satisfied with simple transactions anymore. They expect businesses to provide more than good products and services, such as an engaging experience that leaves a positive impression.
Organizations must prioritize improving customer engagement to stay relevant in a highly competitive business landscape. Fortunately, rapidly emerging approaches are enabling brands to build lasting relationships with their clients, one of which is personalization.
According to Segment, 49% of consumers are likely to support a brand after a personalized shopping experience. Salesforce’s 2022 report has also found that 56% of buyers expect personalization in all offers. In the same report, 86% believe good customer experience is just as important as the company’s product or service quality.
These figures emphasize the importance of delighting buyers at all touchpoints. However, businesses with a considerable clientele may need help implementing and managing personalization strategies at such a scale. Fortunately, a developing innovation presents itself as a practical solution: artificial intelligence.
Artificial Intelligence in E-commerce
Artificial intelligence (AI) is a system that simulates human intelligence to perform tasks typically requiring manual input while making predictions and decisions without being programmed to do so. These capabilities make eCommerce personalization easier. AI leverages automation to simplify customer data collection and analysis.
AI technology is quickly becoming a valuable tool in boosting engagement. Businesses with data handling problems can use AI to collect and analyze data, enabling them to implement scalable personalization strategies. In turn, clients can receive more tailored and engaging content that elevates their experience with a brand.
Companies can also benefit from AI-powered tools. Study shows that personalized eCommerce experiences may boost revenue by up to 15%, emphasizing the importance of harnessing customer data to elevate operations.
Ultimately, artificial intelligence raises the bar for targeted campaigns. As this technology continues to take the world by storm, leveraging its capabilities quickly becomes critical to staying relevant and competitive in the business landscape and within customers’ minds.
7 Benefits of Artificial Intelligence in E-commerce Personalization
AI is proving to be a highly beneficial asset in improving engagement. McKinsey’s The State of AI in 2022 has found that 50% of companies have adopted AI, a significant increase from 20% just five years prior. But what makes AI-powered strategies so popular among businesses? Here are some advantages they offer.
1. Data collection
Data is essential in eCommerce as it permits personalization and improved customer services (CS). However, organizations with a vast clientele may find this task time-consuming when manually performed.
Artificial intelligence enables automation systems to simplify data collection. Specialized algorithms can track customer behavior on an eCommerce website, amassing data ranging from product searches to time spent onsite. In turn, businesses can build a profile of their clients’ preferences to aid tailored marketing and content.
The system will then use collected data to predict future behavior, such as what products buyers are likely to purchase, which is helpful for targeted advertising and recommendations. AI can also use data to create a customer journey map. This feature provides insights into their touchpoints to determine areas for improvement.
2. Customer segmentation
Segmentation involves grouping clients based on similarities, like demographic information and interests, to learn more about them. In effect, companies can identify and address customers’ specific pain points to improve their experience with the brand. Doing so also helps develop marketing campaigns. It enables general targeting toward segments instead of individual clients, which is usually time-consuming.
AI-powered segmentation tools can streamline this process. Algorithms can build and maintain buyer profiles by tracking a segment’s journey from first contact to purchase, providing valuable insight into all touchpoints. This analysis enables hyper-targeted campaigns that tap the right people at the proper time and with the appropriate messages.
Implementing targeted advertising is vital to engagement. Irrelevant ads may evoke negative feedback from those who encounter them. Statista has found that non-personalized ads upset 51% of adults, meaning businesses need to improve their advertising strategies to connect and appeal to clients better.
Like the previous points, artificial intelligence improves targeted advertising campaigns by collecting and analyzing buyer data. In turn, businesses can identify which approach works best with which segment.
Moreover, AI-powered budget management can also minimize advertising costs. It optimizes the resources spent on each ad set and predicts a realistic budget for each platform. For example, a brand sets its daily ad budget to $250. The algorithm will evaluate an ad set’s historical performance to determine whether to increase or decrease its budget, reducing wasted resources.
Providing instant feedback to inquiries and comments is essential to maximize engagement. However, doing so may be challenging if there are more client messages than CS representatives can accommodate. This issue may become more amplified when the staff is off work or on leave.
A popular solution is to implement chatbots. These automated messaging systems interact with users on a company’s website or social platforms to answer questions and recommend products. They can do so anytime and anywhere, with little to no waiting time on the customers’ end. In effect, the business can constantly interact with leads and foster robust engagement.
AI can further enhance chatbots by leveraging customer data. For instance, algorithms can use the information to optimize interactions and tailor responses to specific client segments. Chatbots may then suggest personalized recommendations and promotions while providing tailored answers.
5. Product recommendations
Shoppers typically see personalized product recommendations whenever they visit an online business, which is significantly beneficial for eCommerce. Organizations leveraging this system tend to gain a 31% boost in eCommerce revenues.
AI-powered product recommendations help buyers find the product they’re looking for based on their search and transaction histories and not on their query terms alone. In effect, these recommendations tend to be more accurate to what customers want to find. AI also allows brands to introduce new, enticing products that better match their client’s needs based on searches and online behavior.
AI can also use product recommendations to enrich data analytics. Studying how customers interact and respond to suggestions helps the algorithm better understand client interests and the factors influencing their purchasing decisions.
6. Product categorization
Categories can further improve product recommendations since they narrow the customer’s search, making shopping easier and more refined. For example, smartphones and laptops can fall under the “gadget” category since they are electronic devices. However, manually segregating products this way is labor-intensive, especially for brands with extensive product lines.
Fortunately, artificial intelligence makes product categorization simpler and more reliable. The algorithm uses Natural Language Processing (NLP) to analyze product descriptions and names, enabling it to classify them into predetermined categories. AI can even analyze product images to complement NLP analyses.
In turn, eCommerce managers can focus less on tagging or segregation and more on core business operations.
7. Customer reviews
Businesses must consider feedback and product reviews to strategize and grow their operations based on customer demand. But doing so may exhaust those with a considerably large clientele. As a result, some valuable input may go unnoticed.
Companies can leverage AI to analyze and evaluate feedback using customer sentiment analysis. This approach automatically evaluates people’s feelings and impressions based on their language use to determine how they feel about products and services.
The system classifies responses as “positive” and “negative,” assigning them to specific categories for human interpretation. CS reps may then determine which classifications are more popular.
For instance, if there’s an overwhelmingly negative response to the category labeled “shipping time,” it implies that customers are experiencing slow deliveries. The business can then respond by making necessary improvements to its logistics operations.
Boost Customer Engagement with Artificial Intelligence
AI is rapidly revolutionizing how brands interact with and engage buyers. Chatbots, automation, and segmentation enable them to deliver highly personalized services, which can directly address every client’s distinct needs without the difficulty of manually doing so. These eCommerce approaches effectively engage customers and leave lasting impressions.
This technology continues to overhaul the eCommerce landscape. As such, there may come a time when artificial intelligence fully supplements eCommerce personalization strategies worldwide. When that happens, companies need to leverage AI-powered strategies to stay competitive and build more meaningful customer relationships.