How Hyper Personalization Can Crack the Code for Meaningful CX?
Organizations have realized that true digital transformation cannot be imagined without building dynamic integrations to offer optimal product experiences and meet the demands of the business, making an API-led approach an absolute must.
Headless abilities can vastly facilitate multi-experiential information and digital asset delivery by including channels like mobile apps, smart devices, digital and voice assistants, progressive web apps (PWAs), and single-page applications (SPAs), among others. For enterprises that are serious about providing impeccable omnichannel commerce experiences, automatic dissemination of consistent digital assets has become the number one priority. This insight deep dives into why headless Digital Asset Management (DAM) is fast becoming the default route for disseminating digital assets.
What You’ll Learn From This Insight:
Let’s go back to 2000 and imagine how convenient was buying experience. For those who did choose to shop online, the process was generally more cumbersome and less user-friendly than it is today.
Fast forward to 2023, enterprises now think from a customer-centric rather than a product-centric approach. Now the whole notion has turned to understanding customers' needs, wants, and pain points, and then use the data to smartly personalize products and services. The degree of personalization has gone to the next level to earn more meaningful customer connections. Essentially, we are in the age of hyper personalization.
Hyper personalization is a win-win spot for both businesses and customers, as it leads to more relevant experiences, simplifies choices, eliminates friction, and helps in making engagement effortless for customers.
2. The Evolution of Personalization
The paradigm of personalization has changed in recent years. The ability to create individualized and tailored customer experiences continues to evolve, driven by technological advances and consumer behavior changes. Brands can now leverage existing data and analytics tools to better understand customer preferences and serve them as per their behavior and intent.
Some key milestones in the evolution of personalization include:
The decade from 2000 to 2009
The early 2000s: Personalization began to gain traction with the rise of eCommerce and online shopping. Retailers started to use basic personalization techniques, such as recommending products based on the buyer’s browsing history or buying history.
The mid-2000s: The arrival of social media and the proliferation of user-generated content led to the rise of "people-based" personalization, which applied social data to create more personalized experiences.
The late 2000s: The proliferation of mobile devices and the rise of location-based services enabled organizations to deliver more contextualized and relevant communications and offers to consumers.
The decade from 2010 to 2019
The early 2010s: The growth of data and advances in analytics enabled organizations to use sophisticated algorithms to deliver highly personalized experiences. This included tactics like personalized pricing and personalized product recommendations.
The mid 2010s: The rise of big data and the internet of things (IoT) technologies forced companies to take advantage of data-driven insights never observed before. This led to a new level of personalization by using real-time data from various sources.
The late 2010s: Artificial intelligence and natural language processing made the personalization more conversational and more human-like. Chatbots and virtual assistants are the best examples of personalization.
The current decade: From 2020
The early 2020s: The COVID-19 pandemic accelerates the shift to digital commerce, leading to an increased focus on personalization across multiple touchpoints, such as personalization of virtual events and metaverse shopping experiences.
Personalization will likely become even more sophisticated and integrated into various aspects of consumers' lives. Multiple analytics methods and techniques including Ai-driven predictive, prescriptive, and NLP and graph analytics will come into play to predict individual customer’s wants and needs in real time. According to Gartner, personalization growth was 28.2% in 2021, and the segment is expected to continue with strong growth at a 20.4% CAGR through 2026. We have entered the era of hyper personalization.
3. What is Hyper Personalization?
According to Deloitte, Hyper-personalization is the most advanced way brands can tailor their marketing to individual customers. It’s done by creating custom and targeted experiences through the use of data, analytics, AI, and automation. Through hyper-personalization, companies can send highly contextualized communications to specific customers at the right place and time, and through the right channel.
For brands, hyper-personalization can be a powerful tool to improve customer engagement and boost conversion rates. It gives advanced insights to understand each customer's preferences and behavior and create more personalized and contextual experiences. This can lead to higher customer satisfaction and loyalty, as well as increased revenue. Hyper-personalization can also help brands to build stronger customer relationships, amplify revenue, and improve customer loyalty.
86% of companies report seeing a measurable uptick in business results from hyper-personalization.
|By 2023, smart personalization engines used to recognize customer intent will enable digital businesses to increase their profits by up to 25%.
- According to Gartner
4. Comparison: Personalization vs. Hyper Personalization
Personalization typically involves using basic demographic information, such as a person's name or location, to customize an experience. For example, an e-commerce website might use a customer's browsing history to recommend products they might be interested in. Or a news website might use a reader's location to show them stories relevant to their area.
Hyper-personalization goes beyond traditional personalization by considering multiple data points such as demographics, purchase history, and behavior patterns when delivering content or services. This enables organizations to create highly targeted messages that are more likely to resonate with their target audience on a deeper level. This can include things like browsing history, purchase history, social media activity, and even biometric data.
An example of this would be a music streaming service that uses a listener's listening history and preferences to create a custom playlist that they will likely enjoy or an eCommerce website that uses the purchase history, browsing history, and location to personalize the offers, contents, and layout to increase conversion rate. Hyper personalization can also provide the benefits of ‘private personalization’.
According to Deloitte, virtually every business leader (97%) agrees that brands have to harness data, analytics and AI to create hyper-personalized experiences.
5. How To Leverage Hyper Personalization for Business Growth?
i) Reinvent Experience
- Engage consumers in personalized moments, 24x7
- Real-time customer segmentation
- Behavioral recommendations and omnichannel optimization
- Make real-time adjustments based on a customer's current interactions and behavior
ii) Amplify Revenue
- Detailed product targeting with predictive intelligence
- Individualized or dynamic offerings
- Real time promotions and offers
- Apply mass personalized on certain types of customers
iii) Optimize Cost
- Serve better to cost-sensitive customers
- Optimize marketing campaigns cost with data-driven customer intelligence
- Decrease customer acquisition and retention costs
- Enhance productivity and reduce resource cost
6. Benefits of Hyper Personalization in Digital Commerce
Hyper-personalization in digital commerce refers to the practice of using advanced technologies and data analysis to create highly customized and targeted marketing campaigns and shopping experiences for individual customers. Some of the potential benefits of hyper-personalization include:
- Contextualized engagement: Allow brands to create targeted marketing messages and product recommendations that are more likely to resonate with individual customers in real time. So, it can lead to increased engagement and conversion rates.
- Adaptive experiences: Apply context, data, and information to identify buying patterns, preferences, and behaviors for constructing person or role-based experiences.
- Improved customer loyalty: Enable businesses to build stronger relationships with their customers by providing personalized experiences, leading to increased loyalty and repeat business.
- Predict customer desires: Get a better understanding of the customer intent and predict their wants and needs to increase conversion using the predictive power of existing customer data.
- Increased revenue: Lead to increased conversion rates and customer loyalty, which can ultimately result in increased revenue for the business.
The implementation of hyper-personalization in digital commerce is not a trivial task. Digital commerce brands should strategically invest in hyper personalization capabilities as it demands advanced analytics methods, AI-driven predictive technologies, graph analytics, NLP and techniques including product and content recommendations.
7. How to Apply Hyper Personalization Across the Customer Journey?
The goal of hyper-personalization is to create a unique and tailored experience for each individual customer, based on their preferences, behavior, and demographics. Hyper-personalization can be applied throughout the entire customer journey, from the initial engagement to post-purchase follow-up.
This can be achieved through a variety of means, such as:
Delivering customized content, such as product recommendations, email campaigns, or website experiences, based on a customer's interests and behavior.
Segmenting customers based on their behavior, preferences, and demographics allows you to create targeted and relevant experiences for different groups of customers.
Reaching out to customers through their preferred channels in real-time, such as such as personalized product recommendations with instant discount offers.
Personalized products and pricing
Creating customized products or services based on a customer's preferences. Offering different prices or deals to different customers based on their behavior or demographics.
Using predictive modeling, machine learning, and artificial intelligence can help anticipate a customer's needs and preferences and create individualized experiences.
Integrating hyper-personalization across multiple channels, such as connected devices and online & offline channels to create a seamless and relevant experience across all touchpoints.
But it is important to note that hyper-personalization should be done with the customer's consent and care should be taken to protect their privacy and security.
8. Best Real-World Examples of Hyper Personalization
- Netflix: Netflix uses data on viewing habits, search history, and ratings to recommend TV shows and movies to individual users. This helps to ensure that each user sees a personalized selection of content that is tailored to their interests.
- Amazon: Amazon uses data on purchase history, browsing history, and product ratings to recommend products to individual users. This helps to ensure that each user sees a personalized selection of products that are tailored to their interests.
- Spotify: Spotify uses data on listening habits and preferences to create personalized playlists for users. This helps to ensure that each user hears a selection of songs that are tailored to their interests and preferences.
- L'Oreal: L'Oreal uses data on skin type, age, and concerns to create personalized skincare routines for customers. This helps to ensure that each customer has a routine that is tailored to their specific needs.
- Booking.com: Booking.com uses data on past bookings, browsing history, and customer preferences to recommend personalized hotel and vacation rental options to individual users.
These are just a few examples, but now more and more brands are applying hyper-personalization to improve their marketing and sales efforts.
9. Factors to Consider in Hyper Personalization Implementation
Hyper personalization, which involves tailoring products, services, and experiences to the individual level, can present several challenges. Some of these include:
- Data collection and management: Requires a large amount of data on individual customers, including their preferences, behavior, and demographics. Collecting and managing this data can be a significant challenge, especially for companies not used to working with large amounts of data.
- Data quality: The success of hyper personalization relies heavily on the quality of the data. Poor data quality can lead to inaccurate personalization, which can lead to a poor user experience and can damage the relationship with the customer.
- Data privacy: Needs a significant amount of data about individuals, which can raise privacy concerns. Companies need to be transparent about what data they are collecting and how it will be used, and ensure that they comply with data protection laws.
- Segmentation issues: Requires a deep understanding of customer segments and the ability to segment customers in a way that is both accurate and actionable. This can be difficult to achieve, especially for companies that do not have a deep understanding of their customer base.
- Scalability: Hyper personalization can be difficult to scale, as it entails a large amount of data and computational power to create accurate and customized experiences for each individual.
- Ethical considerations: Hyper-personalization can lead to unintended consequences, such as discrimination or manipulation. It is important to be aware of ethical considerations, so always put in place safeguards to mitigate them.
10. What are the Key Enablers of Hyper Personalization?
Enabling hyper personalization requires two key technologies — data management and architecture.
The Role of Data
One of the critical reasons why hyper-personalization alone is not enough is that it relies heavily on having accurate and comprehensive data about customers. In fact, data is the driving force in hyper personalization by providing insights into individual preferences, behavior, and demographics.
Machine learning algorithms can also be used to analyze data and make predictions about what a particular individual is likely to be interested in, further improving personalization.
Product data can help in hyper personalization by providing detailed information about a product or service that can be used to tailor specific recommendations or offers to individual customers. This can include product features, pricing, customer reviews, and demographic data.
By analyzing this data, businesses can better understand the preferences and needs of their customers and use this information to create personalized experiences tailored to each individual. This can include targeted marketing campaigns, personalized product recommendations, and customized offers and discounts.
Master data or data that is considered to be of the highest quality and importance for a specific organization can help in hyper personalization by providing a detailed understanding of customer preferences and behavior.
This information can then be used to create highly targeted and personalized marketing campaigns, product recommendations, and customer interactions. For example, if a customer has previously purchased a particular product or shown interest in a specific category, the organization can use that information to make personalized product recommendations or send targeted promotions.
Master data can also be used to segment customers into different groups based on their preferences and behavior, allowing for even more tailored and personalized interactions.
The Role of Technology and Architecture
The appropriate technology and architecture are essential for enabling hyper-personalization. This includes things like data management platforms, customer data platforms, marketing automation software, and analytics tools that can be used to collect, process, and analyze customer data in real time.
Some of the key ones include:
- Data Management Platforms (DMPs): These platforms collect, store, and analyze data from various sources to create a comprehensive view of each customer.
- Customer Relationship Management (CRM) Systems: These systems are used to manage customer interactions and data throughout the customer lifecycle.
- Artificial Intelligence (AI) and Machine Learning (ML): These technologies are used to analyze customer data and predict customer behavior.
- Personalization Engines: These systems use AI and ML to make real-time decisions about the content and experiences delivered to each customer.
- Advanced analytics: To extract insights from data and make better predictions.
- Cloud infrastructure: To handle the scalability and flexibility needs of hyper-personalization.
- A/B testing: To validate and optimize the effectiveness of the personalization.
The specific tools and technologies used will depend on the requirements of the organization.
In conclusion, hyper-personalization is a practical approach for businesses looking to drive growth by creating highly personalized and relevant experiences for individual customers. But it can also be challenging to implement and require a collective effort. Nevertheless, enterprises that successfully implemented hyper-personalization strategies have realized encouraging results in sales expansion, customer loyalty, and overall business growth.