juin 07, 2024
As our digital footprints grow, they leave behind a trail of clues about what makes us unique. Each click, search or interaction adds to a massive pool of data that businesses can tap into to understand their customers better.
Shoppers expect their favorite brands to connect with them on a personal level. They want businesses to anticipate their needs, understand their preferences and deliver tailored experiences that reflect their tastes and lifestyles.
In the broadest sense, we describe this expectation as personalization, and when done well it can drive incredible results, turning casual interactions into engaging experiences that drive customer satisfaction and loyal relationships.
Two-thirds of consumers have spent more time and/or money than intended because of an immersive experience, our research shows. When brands tailor their interactions to individual preferences, they can capture a bigger piece of that spending.
This glossary provides a quick introduction to the world of modern personalization. Explore the concepts and strategies that define personalization and are shaping the future of customer experience.
App personalization
App personalization is all about customizing the user experience within a mobile application based on individual user preferences, behavior and other data points.
This can include tailored content, personalized recommendations, customized interfaces, and notifications that are relevant to the user's interests and behaviors.
For example, a music streaming app can use personalization to create a unique experience for each user. Based on listening habits, the app can recommend new songs, artists and playlists that align with the user’s musical tastes. It can also personalize the app’s interface, highlighting favorite genres or recently played tracks. Personalized notifications can also inform users about new releases from their favorite artists or suggest music for specific activities, like working out or relaxing.
An ecommerce app could analyze browsing history, purchase behavior and wish list items to recommend products that match the user's preferences and shopping habits. If a user usually purchases sportswear, the app can highlight new arrivals in athletic clothing and offer exclusive discounts on related items. Personalized push notifications can also alert users to sales events or restocks of previously viewed items, driving higher engagement and sales.
AI-driven personalization
Unlike traditional personalization methods that rely on basic rules and manual segmentation, AI can process complex datasets to uncover patterns, preferences and predictions, enabling businesses to offer truly personalized interactions in real-time.
For example, in the travel industry, AI-driven personalization can analyze a customer's booking history, preferred destinations and travel behaviors to provide tailored recommendations. If a customer frequently books beach vacations, AI can highlight new beach destinations, exclusive resort deals and relevant travel packages.
AI can also adapt to changes in customer behavior much more quickly than traditional methods. If a user starts showing interest in adventure travel, AI can quickly adjust recommendations to include activities like hiking tours, adventure sports and off-the-beaten-path destinations. This real-time personalization ensures customers receive relevant travel suggestions and increases the likelihood of repeat bookings.
Content delivery network (CDN)
A content delivery network (CDN) distributes servers across geographical locations, allowing users to access content quickly and efficiently, regardless of their physical location.
CDNs work by caching content on multiple servers around the world. When a user requests content, the CDN directs the request to the nearest server, minimizing the distance the data has to travel. This speeds up content delivery and reduces the load on the origin server. For businesses, this means faster website performance, which can lead to higher user engagement and conversion rates.
In the context of personalization, CDNs make sure personalized content is delivered seamlessly. Personalized content, such as user-specific recommendations, targeted ads and customized interfaces, requires rapid and reliable delivery to be effective. CDNs help achieve this by distributing personalized content closer to the end user, ensuring minimal delays.
For example, an ecommerce site can use a CDN to deliver personalized product recommendations instantly, enhancing the shopping experience and increasing the likelihood of a purchase.
Customer 360 (C360)
Customer 360 (C360) gives businesses a complete view of their customers by pulling together data from every touchpoint and channel, allowing them to deliver highly personalized and consistent experiences every time they interact with a customer.
Traditionally, companies have struggled with siloed data, where information about customer interactions is scattered across various departments and platforms. This disconnected data makes it difficult to get a unified understanding of the customer, leading to disjointed and frustrating customer experiences.
Customer 360 addresses this issue by integrating data from diverse sources, such as customer relationship management (CRM) systems, social media, email interactions and purchase history. The result is a single, cohesive customer profile that provides a complete view of the individual.
For example, a retail company can use C360 data to identify high-value customers and tailor personalized recommendations or exclusive offers to encourage repeat business. Real-time data integration means customer profiles are constantly updated, allowing businesses to respond quickly to changes in customer behavior. Plus, customer service representatives can quickly access all relevant information, providing faster and more personalized resolutions.
Contextual marketing
Contextual marketing involves delivering personalized content to customers based on their current context, such as location, time of day or current activity. This approach ensures marketing messages are highly relevant to the customer's immediate situation. For instance, a coffee shop might send a discount offer to a customer who's nearby during the morning.
Dynamic content
Dynamic content creates a more personalized and engaging experience for every visitor by adapting web content in real-time based on user behavior, preferences and data.
Imagine visiting an online news site that instantly tailors its homepage to your interests. If you often read about technology, the latest tech news will greet you. If sports are more your thing, top sports stories take center stage. This personalization keeps users engaged and ensures they find content that resonates with them.
Retailers can use dynamic content to recommend relevant products, display personalized offers and highlight items based on a customer’s browsing and purchase history. For example, if you’ve been eyeing running shoes, the site might showcase related accessories like fitness trackers and running socks.
Marketers can send personalized emails that adjust in real-time, showing products or offers based on what a recipient has viewed or purchased. An abandoned cart email can include the exact items left behind, along with personalized recommendations and special deals to encourage a purchase.
Customer data platform (CDP)
A customer data platform (CDP) is a technology solution that collects and unifies customer data from multiple sources to create a single, comprehensive view of each customer. This data can then be used to deliver personalized experiences across various touchpoints. For example, a CDP can integrate data from online and offline purchases, email interactions and social media activity to create a personalized marketing campaign.
Hyper-personalization
Hyper-personalization takes personalization to the next level by using artificial intelligence (AI) and real-time data to deliver highly relevant experiences, content and products to customers. It involves analyzing not just past behaviors but also real-time data to create a deeply personalized experience. For example, a fitness app might provide personalized workout plans based on real-time data from a user's wearable device.
Omnichannel personalization
Customers expect a unified experience, and any disconnect between channels can lead to frustration and disengagement. Omnichannel personalization bridges these gaps by integrating data and insights from all touchpoints, creating a seamless customer journey. Whether a customer starts their interaction on a mobile app, continues it on a website, and completes it in a physical store, the experience should feel continuous and personalized.
For example, imagine a customer browsing products on a retailer's website, adding a few items to their cart, but leaving without making a purchase. Later, they receive an email with personalized recommendations and a reminder about the items left in their cart. The next day, they visit the store, where a sales associate, using insights from the customer’s online activity, offers assistance and perhaps an incentive to complete the purchase. This seamless transition across channels enhances the customer experience and makes it more likely they'll complete their purchase and remain loyal to the brand.
Personalization engines
Historically, businesses have relied on broad customer segmentation to inform their marketing strategy. While effective to a degree, this method often falls short in addressing the unique preferences and behaviors of individual customers.
Personalization engines transform this approach by using AI and machine learning to analyze data such as browsing history, purchase patterns and social media interactions. This enables businesses to move beyond generic messaging and deliver relevant content that speaks directly to the individual.
For example, an ecommerce platform can use a personalization engine to examine a customer’s browsing and purchase history before recommending products that align with their tastes and needs. If a customer often purchases athletic wear, the engine might highlight new arrivals in sports apparel or exclusive deals on fitness gear.
Personalization engines can also adapt content as new data becomes available. This is particularly valuable in dynamic environments such as news websites or streaming services, where user preferences can shift rapidly. For instance, a streaming service can use a personalization engine to suggest shows and movies based on a user’s viewing habits, mood or even the time of day. If a user tends to watch comedies in the evening, the service can prioritize comedy recommendations during that time.
User segmentation
User segmentation allows businesses to break down their audience into smaller, more manageable groups, each with its own set of characteristics and needs. This enables more precise and relevant marketing efforts, which increases the likelihood of engagement and conversion.
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Behavioral segmentation is valuable in understanding and predicting customer actions. By analyzing how customers interact with a brand — such as which pages they visit, how long they stay and what they click on — businesses can create segments that reflect different stages of the customer journey. For example, customers who regularly visit product pages but don’t make a purchase might be included in a segment for retargeting campaigns, offering them special promotions or additional information to encourage a sale.
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Demographic segmentation, which groups customers based on age, gender, income and other demographic factors, allows businesses to craft messages that resonate with specific life stages and interests. For example, a travel company could create distinct marketing campaigns for young professionals, families and retirees, each highlighting travel packages and destinations that appeal to those specific groups.
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Psychographic segmentation, which considers lifestyle, values and interests, enables businesses to create highly targeted campaigns that align with their customers' passions and beliefs. For example, a fitness brand might segment its audience into groups such as yoga enthusiasts, marathon runners and casual gym-goers.
For example, an online retailer might segment its customers based on purchase behavior, identifying groups such as frequent buyers, occasional shoppers and first-time visitors. Each segment can then receive tailored messaging and offers that speak directly to their purchasing patterns. Frequent buyers might be targeted with loyalty programs and exclusive discounts, while first-time visitors could receive welcome offers and introductory product recommendations.
Zero-party data
Unlike first-party data, which is collected passively through customer interactions, zero-party data is explicitly provided by customers. Zero-party data can include preferences, feedback, profile information and more to offer deeper and more accurate insights into the desires and expectations of customers.
For businesses, working with zero-party data means gaining direct access to customer intentions and preferences without the ambiguity that often comes with inferred data. Since customers willingly share this data, they're more likely to trust the brand and feel comfortable engaging with it.
For instance, a travel company can use zero-party data to understand customers' preferred destinations, travel dates and budget constraints. Armed with this information, they can offer personalized travel packages, exclusive deals and timely promotions that cater to the specific needs of each customer.
Zero-party data also helps businesses manage the complexities of data privacy regulations. Since this data is willingly provided by customers, it typically aligns well with data-protection laws and reduces the risk of compliance issues.
Understanding these terms is key to using personalization effectively in your business. As personalization continues to evolve, staying informed about these concepts will help you create more meaningful and engaging experiences for your customers.