Today, personalized experience is no longer just a plan; it’s a necessity. Around 75% of eCommerce companies have a website personalization program.
Stakeholders responsible for driving digital commerce initiatives must prioritize personalization to create relevant, individualized experiences catering to unidentifiable and identifiable customers. The integration of Generative AI (GenAI) brings a new dimension to personalization, enabling hyper-relevant communication and right product recommendations at scale.
The Connection of Personalization and Business Profitability
Over 60% of consumers will stop buying from brands that use poor personalization tactics. Personalization enriches digital commerce by tailoring interactions to customers’ preferences, behaviors, and needs. Whether through user segmentation, search taxonomy, filters, recommendations, product bundling, geo-targeting, or dynamic pricing, it enhances the customer experience (CX), leading to improved conversion rates and higher average order values (AOV).
90% of leading marketers say personalization significantly contributes to business profitability. It directly contributes to boosted sales, customer retention, and overall profitability by providing convenience, differentiation, relevancy and trust.
The Role of Generative AI in eCommerce Personalization
GenAI is an absolute game-changer, revolutionizing how we personalize content, products, and experiences. According to a Salesforce report, more than two out of three (68%) say generative AI will help them better serve their customers. Leveraging GenAI, eCommerce brands can create highly customized product descriptions, tailor marketing messages to individual preferences, and even design new product details based on customer insights. This unprecedented level of personalization gives brands a powerful competitive edge when harnessed effectively.
Moreover, GenAI can help enhance customer interactions by creating personalized chat responses or virtual assistants that understand and anticipate customer needs in real-time.
GenAI Use Case Scenarios in eCommerce Personalization
1. Personalized Content Generation
- Scenario: A fashion eCommerce platform wants to create personalized marketing content for its diverse customer base. Traditionally, this would require significant manual effort to segment customers and craft tailored messages for each segment.
- GenAI use case: By using GenAI, the platform can automatically generate personalized email content, product descriptions, and marketing messages that reflect the preferences and past behaviors of individual customers. For instance, brands can quickly analyze a customer’s purchase history and generate an email highlighting new arrivals similar to their previous purchases.
- Impact: This approach not only saves time but also increases the relevance and engagement of marketing communications, leading to higher open rates, click-through rates, and conversions.
2. Dynamic Product Recommendations
- Scenario: An online electronics retailer wants to improve its product recommendation engine to offer more relevant suggestions to customers browsing its online store.
- GenAI use case: GenAI can enhance the recommendation engine by analyzing customer interactions, purchase history, and external data sources, and social media activities/behavior. The retailer can offer customers more precise and timely suggestions by generating product recommendations that consider these diverse data points. For example, if a customer frequently searches for gaming laptops and interacts with gaming-related content on social media, GenAI can recommend the latest gaming accessories or related products.
- Impact: Improved recommendations lead to higher customer satisfaction, increased average order value (AOV), and greater customer loyalty, as customers feel understood and valued by the brand.
3. Real-Time Personalized Customer Support
- Scenario: A global travel booking platform wants to enhance its customer support experience by providing personalized assistance in real-time.
- GenAI use case: GenAI can power chatbots and virtual assistants to offer personalized support based on a customer’s browsing history, previous interactions, and even sentiment analysis from call center data. For example, suppose a customer is looking for flights and has previously expressed interest in budget travel. In that case, the virtual assistant can proactively suggest cost-effective options and provide personalized travel tips.
- Impact: Real-time, personalized support improves the overall customer experience (CX), reduces cart abandonment rates, and builds trust, leading to repeat business.
4. Unified Customer Profiles and Predictive Segmentation
- Scenario: A retail company with online and offline sales channels wants to create a unified view of its customers to deliver seamless omnichannel experiences.
- GenAI use case: GenAI can unify disparate data streams from call centers, social media, in-store transactions, and online behavior to create holistic customer profiles. By integrating predictive analytics, GenAI can identify new customer segments based on previously unnoticed patterns and behaviors. For example, you can discover a segment of customers who are highly influenced by social media trends and they are more likely to purchase products featured by influencers.
- Impact: This unified approach allows for more accurate targeting and personalization across all channels, leading to better engagement, increased sales, and a stronger brand-customer relationship.
5. Creative Content Creation
- Scenario: A luxury goods retailer wants to enhance its brand storytelling by creating visually stunning content that resonates with its affluent customer base.
- GenAI use case: GenAI can generate high-quality visual content, such as product images and promotional videos, that align with the brand’s aesthetic and the preferences of its target audience. Thus, you can create a video campaign that features a new product line, using imagery and narrative styles that have proven successful in past campaigns.
- Impact: This approach enhances the brand’s visual appeal and ensures that the content is tailored to the tastes of its audience, driving engagement and brand loyalty.
These are just a few use cases— you can do much more than.
Enabling Technologies for eCommerce Personalization with GenAI
Organizations must invest in several key technologies to achieve effective personalization, with AI and data at the core. These technologies generally include:
- Data Management Platforms (DMPs): Collect, organize, and activate large sets of structured and unstructured data from various sources to create detailed scenarios.
- Customer Data Platforms (CDPs): Centralize customer data from various sources, creating a unified customer profile that can be used to deliver personalized experiences across all channels.
- Personalization Engines: Leverage AI and machine learning to analyze customer data and deliver personalized content, product recommendations, and offers in real-time.
- Search and Product Discovery Tools: By personalizing search results and product recommendations, these tools ensure that customers find the most relevant products quickly and easily.
- Digital Experience Platforms (DXPs): Enable organizations to manage and deliver personalized digital experiences across multiple channels, ensuring consistency and relevance.
- Multichannel Marketing Hubs (MMHs): Allow for personalized marketing campaigns across various digital and non-digital channels, ensuring marketing messages are tailored to individual customer segments.
In conclusion
Personalization and Generative AI are revolutionizing digital commerce by empowering organizations to deliver highly relevant and individualized customer experiences. However, the real challenge for brands lies in seamlessly integrating these technologies into their digital commerce strategies to fully harness their potential. By prioritizing personalization and tapping into the capabilities of GenAI, organizations can achieve substantial gains in customer satisfaction, loyalty, and revenue growth.
How is your organization currently approaching the integration of GenAI, and what challenges or opportunities are you encountering along the way?
