What Is Hyper-Personalization?
Hyper-personalization is the next evolution of tailored customer experience. By using a combination of real-time data, artificial intelligence (AI), and machine learning (ML), it delivers hyper-personalized experiences to individual users at scale.
Unlike basic personalization — which might include addressing a customer by name, or making product recommendations based on their purchase history — hyper-personalization uses predictive analytics to create highly tailored and bespoke experiences for individual customers based on their behavior and preferences.By combining browsing behaviors, past actions, location, time, and other data points from CRM, support, and marketing into a unified customer view, hyper personalization creates context-aware interactions. This results in unique customer experiences, such as dynamic website content that is adapted in real-time, or offering relevant support before the customer even asks for it.
Benefits of Hyper-Personalization
As customers continue to become digital-first, they’re increasingly expecting seamless, relevant interactions at every touchpoint. Hyper-personalization makes that possible at scale as part of broader digital transformation efforts. Harnessing deeper customer insights boosts engagement, satisfaction, and helps brands retain customers.
Key benefits include:
Higher Conversion Rates
By understanding individual customer preferences and behaviors, businesses can offer more relevant experiences and anticipate customer needs to provide the right offers, recommendations, and solutions at the right time.
Delivering data-driven experiences and interactions that resonate will lead to increased engagement and purchases.
Proactive Customer Service and Support
Predictive analytics allow brands to analyze patterns, trends, and anomalies in customer data to predict future behaviors or preferences. They can then use this intelligence to create relevant and dynamic experiences to meet customer needs and address challenges or issues before they’re expressed. This, in turn, helps drive higher levels of customer satisfaction.
Improved Customer Loyalty
Highly tailored experiences that address specific individual customer needs can build trust and foster deeper emotional connections. When customers feel understood, they’re more likely to engage, return, and stay loyal.
Challenges of Hyper-Personalization
Although the promise of hyper-personalization and enhanced customer experiences is becoming more widely recognized, the journey to uncovering its full value isn’t without difficulties.
Chief among these is the significant investment required in both technology and data, which is the driving force of successful hyper-personalized interactions.
Some of the main challenges organizations face when it comes to hyper-personalization include:
Data Privacy Concerns
Effective hyper-personalization strategies depend on customer data, but customers are becoming worried about how their data is being collected, processed, stored, and used.
Growing awareness of data privacy and security, and the evolution of stricter regulations like GDPR and CCPA, mean brands must be transparent and open with customers about data collection and management processes. They must also explain how they are using data to improve the customer experience, as well as provide the choice to opt out. At the same time, brands need to ensure compliance with privacy laws.
This is why ethical AI must be a priority for any brand looking to capitalize on hyper personalization technologies. Companies need to proactively adopt practices and techniques to make sure AI systems are being implemented in an ethical and responsible way. This will help maintain trust, fairness, and accountability.
Data Quality Issues
Ensuring the accuracy, completeness, and consistency of customer data from various sources is essential to get hyper-personalization right. Incomplete, inaccurate, or outdated data can severely impact hyper-personalization efforts and turn experiences into negative ones that erode trust in the brand.
Brands must act decisively and invest in a robust data governance framework to ensure the integrity of their data. With this foundation in place, brands can bring together customer data from various sources into a single, unified platform, and harness advanced and predictive analytics for actionable insights.
Over-Personalization Resulting in Customer Fatigue
Customers are besieged by more content and choices than ever before. This sea of content – even if tailored – can quickly become overwhelming. Likewise, receiving too many targeted communications or interactions through emails, ads, and more can make it seem like customers are being ‘watched’ or ‘listened to’ and put them off.
Companies must again clearly communicate with customers about how data is being used and give customers options to adjust their personalization preferences. They must also have a clear hyper-personalization strategy in place to balance personalization with relevance and ensure that they are sending the right messages at the right time.
Hyper-Personalization vs Personalization: What’s the Difference?
Hyper-personalization is the next iteration of personalization as a business strategy. It moves beyond basic personalization efforts to ensure that each interaction and experience is highly tailored and relevant to each individual customer for the best experience possible.
The key differences are:
| Personalization | Hyper-personalization | |
| Technology | Basic data analysis and segmentation software | AI (including generative AI), machine learning, real-time data analytics |
| Data Depth | Basic data like browsing and purchase history, demographics | In-depth data including real-time behavior, location, and social media activity |
| Level of Customization | Segmenting email lists based on demographics, using a customer’s first name in the email body or subject line, and recommending products based on a past purchase. | Recommending a product in real-time based on a customer’s current browsing activity and page views, location, time of the day and even the weather. |
Industry Applications of Hyper-Personalization
Hyper-personalization is becoming ever-more prevalent in industries such as travel, retail, entertainment, healthcare, and banking, where organizations rely on building strong customer relationships to drive loyalty and retention.
Customer-centricity is vital, and hyper-personalization lies at the heart of bringing the goal to life. AI-powered hyper-personalization in retail, for instance, allows retailers to deliver a frictionless experience both online and in-store.
With advanced analytics, they can gain insights into customer sentiment and behavior. That, in turn, can be used to create tailored recommendations and exclusive offers.
In travel, data-driven insights can provide customized itineraries, while in banking and financial services, this could be a tailored investment portfolio or insurance plan.
Additional examples of industry applications of hyper-personalization are:
Omnichannel Customer Service
Omnichannel service and support refers to creating a unified experience across all channels, including physical branches, online, and mobile. Part of that means transitioning between different touchpoints smoothly without losing context – and data integration enables this.
An example of this in retail would be a customer browsing for a new TV on the retailer’s website, and then finishing the purchase in-store – with the sales assistant already equipped with the customer’s online browsing history and able to provide additional information on its specs and features, as well as answer any questions the individual may have.
Intelligent Chatbots
Customers today expect brands to provide real-time support. Live chat on websites and apps has become an important way for companies to provide this at scale. Advanced chatbots using capabilities like Natural Language Processing (NLP) and machine learning can answer questions, solve common issues, make product recommendations, and help with purchases.
Intelligent automation can also help hyper-personalization efforts across sectors. It optimizes processes around all customer touchpoints and interactions. AI, machine learning, and cognitive technologies provide brands and their contact center operations with up-to-date information and options for problem resolution. It also helps customer-facing agents build relationships, creating a smoother, more efficient customer journey across all touchpoints.
An example in healthcare would be chatbots accessible through messaging apps that act as personal health assistants of sorts, providing medication reminders, tracking health metrics, and locating healthcare providers.
Predictive Analytics
With advanced analytics, companies can record and analyze every customer interaction by using transaction records, customer contact histories, ad click-throughs, product hierarchies, and CSAT scores to gain deep insights into customer sentiments and engagement.
Predictive analytics also allow brands to track patterns, trends, and anomalies to forecast future outcomes and make suggestions and recommendations accordingly.
An example in the media and entertainment sector would be a streaming service personalizing show or movie recommendations based on past viewing, as well as how users interact with content – including how long a subscriber watches for, and sections where they chose to rewind or fast-forward.
How Sutherland Helps Enable Hyper-Personalization
Hyper-personalization is going to become a critical strategic differentiator for brands that thrive in the future. There are a growing number of examples of enterprises across industries providing unique, highly tailored experiences that consistently exceed their customers’ expectations. These include:
- A leading restaurant point-of-sale (POS) provider leveraging intelligent automation to provide tailored customer support operations and faster problem identification and resolution for a customer satisfaction (CSAT) score of 4.93 out of 5.
- A premier over-the-top (OTT) streaming service in India implementing Sutherland’s AI-powered knowledge management platform, SmartLeap® HelpTree, to drive a more personalized, accurate, and efficient customer experience for a 14% uplift in customer delight.
- A top Middle Eastern conglomerate integrating advanced tools such as Sutherland Connect, Conversational AI, and Sutherland Engage to understand customer preferences and hyperpersonalize offerings for a 25% improvement in the overall customer experience.
