Greg Kihlström advises Fortune 1000 companies on MarTech, CX, and Digital Transformation, and hosts The Agile Brand Podcast.
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The pressure to meet customers’ online shopping experience expectations is higher than ever. This means brands face the challenge of delivering highly personalized experiences to customers while managing vast amounts of data and interactions across multiple channels.
In my work with enterprise retail brands, I have seen firsthand how AI can supercharge the e-commerce experience. Let’s explore three areas where AI can be leveraged effectively.
Search & Discovery
The search and discovery process is a critical aspect of the e-commerce customer journey. AI can significantly enhance this process by analyzing customer data and preferences to deliver advanced personalization. By using AI-based tools that personalize what customers see and the order they see it, retailers can customize both search results and product recommendations. This makes it easier to find things, and it also makes customers feel seen and understood.
When a customer visits a fashion retailer’s online store, for instance, AI can analyze their browsing history, past purchases and even real-time behavior to present a tailored list of products that match their interests. If a customer frequently buys sportswear from the retailer, the search results can prioritize new arrivals in that category.
To implement this effectively, enterprise marketing teams should invest in robust AI-driven search and recommendation engines. Several integrate seamlessly with existing e-commerce and customer data platforms. Ensuring effective feedback loops to continuously optimize based on changing customer behaviors and preferences is also crucial.
This means a more intuitive and satisfying shopping experience for customers. For the enterprise brand, it can lead to higher conversion rates, increased customer satisfaction and, ultimately, greater customer loyalty.
Personalization With Product Descriptions And Images
Personalization extends beyond search results to include the very content that customers engage with. AI can tailor product descriptions and images based on a user’s behavior, context and preferences, creating a more engaging and relevant shopping experience.
Consider that same fashion retailer we looked at earlier, that now chooses to use AI to personalize its product pages. If a customer has shown a preference for eco-friendly products, the system can highlight the sustainable aspects of the items they view in real time. Similarly, images of the products can be selected based on what resonates most with the customer, such as lifestyle photos versus studio shots, or other aspects like location or even featuring other products the customer owns alongside the new product.
Brands should use AI tools that can dynamically generate or adapt content on product pages. This includes personalized text descriptions and selecting the most appealing images for each user. It’s also important to continually test and refine these personalized elements, including multivariate testing across segments, to ensure they resonate with the target audience.
When this comes together well, customers enjoy a more relevant and engaging experience, seeing products presented in a way that appeals directly to them. For the brand, this approach can lead to higher engagement rates, reduced bounce rates and improved overall sales performance. It’s truly a win-win for all.
Predictive Analytics
Using AI to analyze past customer data to predict future behaviors, such as a customer’s propensity to buy, preferred messaging channels and likelihood to churn, is what makes predictive analytics so powerful. Using this approach enables marketers to make data-driven decisions to maximize customer lifetime value while providing customers more of what they want.
An e-commerce company that relies on subscriptions for its core services, for example, could use predictive analytics to determine which customers are likely to cancel their subscriptions. The company could then use this information to help it create an early-warning system of sorts. By understanding the patterns of a customer likely to churn, the company can proactively engage these customers with personalized offers or incentives to retain them.
For enterprise marketing teams to use predictive analytics, it is crucial for them to have access to top-notch data—ideally real-time data—and the appropriate tools for analysis. This entails merging data sources and employing machine-learning models to extract insights that can ideally be automated to address issues before they lead to subscription cancellations. It is also vital to monitor and adjust these models in response to data and evolving customer trends.
Customers benefit from receiving tailored communications that cater to their preferences, ultimately enhancing their satisfaction. Predictive analytics can result in targeted marketing campaigns, improved customer retention rates and increased customer lifetime value for the enterprise brand.
Integrating AI into e-commerce has the potential to revolutionize the customer experience and yield business advantages. The key factor in achieving success lies in integrating AI into existing systems while continually refining strategies based on data-driven insights.
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