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What is hyper-personalization?

Explore how this strategy help identify customer needs and keeps brands ahead of their competitors by leveraging data and AI

CHAPTER 1

The evolution of personalization

Many people have gone through the experience of going into a local shop, like a coffee house where you’re a regular and the barista recognizes you and knows your favorite drink before you even order it. It’s also not uncommon to find other vendors who can easily recall which products their regular customers typically prefer.

These merchant-to-customer relationships are good business. Being able to establish a personalized connection fosters loyalty.

On a digital level, and to scale it up, hyper-personalization is an even more advanced marketing tactic, leveraging real-time behavioral data, advanced analytics, and artificial intelligence to tailor products, services, and experiences according to customers’ aspirations and needs.

That’s also good business.

It’s a matter of matching customers with their preferences, and hyper-personalization can achieve this with the right techniques paired with technology. Hyper-personalization creates fine-tuned, customized, and targeted experiences through data, analytics, artificial intelligence, and automation.

This strategy goes beyond the typical personalization strategies of the recent past. Today’s marketplace is evolving, and businesses must harness cutting-edge technologies to know what customers desire and stay ahead of a growing field of competitors.

Before any type of modern personalization was introduced, the process of identifying customers was clunky and time-consuming. Segmented customer data was collected, but it was spread across outdated data-entry systems and limited to point-of-sale or call centers. It could take weeks or longer to process the data and identify customer behavior trends.

It was still a step above mass-media campaigns in which advertisements went out with little more than general demographics information based on the type of platform it would be appearing on, such as TV, print, radio, etc.

But by combining advanced data collection along with AI-powered technology, a B2B or B2C brand can now analyze a customer’s historic interactions and create a multichannel setup for more effective engagement and industry-specific insights — often in real-time.

Chapter 2

How hyper-personalization can work

Hyper-personalization is most effective when brands have a thorough understanding of their customers. A brand using hyper-personalization tools can find a customer in its database and send contextualized messaging at the optimal time and place as an act of product targeting.

As an example: A shopper is looking for a certain pair of shoes but only browses online at a certain time of day while on a break without buying. A business set up with hyper-personalization apps can deploy algorithms to track and analyze the data the shopper has left via cookie or other means and customize a campaign to send a push notification to pitch a discount that’s specific to that particular shopper.

In a Deloitte analysis led by marketing and AI practice leader Bilal Jaffery, researchers outlined a nine-step hyper-personalization playbook throughout a customer’s journey. (It’s similar to what the shoe shopper experienced in the above example.). Not all hyper-personalization campaigns are similar, but regard the following as a general outline:

  • Advertising: The customer starts the engagement by shopping online. The retailer targets the customer with unique ads that render relevant products or customer information with welcoming language and useful directions.
  • Landing pages: The retailer with the right hyper-personalization tools can launch a customized campaign using data that details where customers are located, past visits, geographic data, and their preferences, including related products.
  • Recommendation engines: The algorithm detects certain data points and presents content, product, or service recommendations tailored specifically to the customer.
  • Omnichannel customer service: Databases and AI recognize and connect customers from both online and offline shopping channels.
  • Service chatbots: Through more data harvesting, conversational AI technology learns customer behavior and delivers personalized services.
  • Dynamic pricing and offers: Retailers reel in or — more appropriately — identify a customer and present them with changes in the offer, promotion, or price.
  • Pre-populated applications: To speed the process, existing customer data can be used to pre-populate required documents, processes, or applications.
  • Real-time product notifications: Customers make the purchase, then they are updated on shipments, promotions, or re-orders based on their purchase history.
  • Loyalty programs and re-engagement: The original transaction creates historical data. Going forward, the customers’ purchases, segmentation, and data can determine contextualized offers and messages from the retailer.

It sounds complex. But on our own devices, we’ve seen how simple searches on retailers and streaming services can activate algorithms that trigger recommendations via email or push notifications.

Sitecore also has a top-10 list of “personalization tactics for quick wins.

These kinds of personalized and targeted transactions allow organizations to improve the customer experience with meaningful engagement that deepens relationships and builds brand loyalty.

Chapter 3

Why you need hyper-personalization

Businesses deploy it, but hyper-personalization is customer-driven. Customer preferences drive sales campaigns.

It’s all about making customers become repeat customers. The pandemic contributed to the growth of hyper-personalization as customers turned to e-commerce options instead of in-person shopping. Deloitte found that companies such as “Amazon, Facebook, and Google are leading the charge through their use of rich customer databases and personalized recommendation solutions.”

Not tapping into hyper-personalization can be costly for businesses.

A Gartner study found that brands risk losing 38% of their existing customer base because of poor personalization efforts. By ignoring personalization, the study determined that businesses also risk higher fallout rates throughout the consumer funnel, creating a domino effect of failure. This stumble triggers lower returns on advertising investment, reduced customer loyalty, fewer impulse purchases, and higher product returns.

Context is a big key to an effective hyper-personalization strategy. The lack of context can leave a customer feeling the brand did not understand their needs, leading to frustration and unhappy customers.

The availability of data also concerns customers — for the right reasons. Cybersecurity tools exist to limit some extractions of data. But their concerns can be mitigated. Studies of customers have determined that:

  • 90% say they are happy to share data if it enhances their purchasing experience.
  • 84% say that being treated as a unique person is critical to winning their loyalty.
  • 80% prefer brands that offer personalized experiences.
  • 77% will pay a premium to brands that deliver personalized experiences.
  • 72% only respond to personalized messaging and ignore anything that they perceive is generic.

Chapter 4

Using customer data for hyper-personalization

Hyper-personalization also can mobilize data from multiple sources — social media, consumer trends, mobile browsing, or purchase history, even data from IoT devices.

So, we know the means of getting the data. How about the outcome?

According to the Deloitte analysis, Amazon and Netflix achieved high customer conversions and retention, which translates into increased revenue. To optimize revenue on a hyper-customization scale, brands also should consider the best practices of electronic bill presentation and payment (EBPP).

What apps are the most effective tools for a hyper-personalization strategy? According to a strategies survey by Ascend2, predictive analytics, user experience, content creation/curation, search/social marketing, email marketing, digital/display advertising, and open-question chatbots are the most effective AI-powered applications.

The right cloud-based technologies are also important, including embracing an omnichannel strategy. This means deploying cloud applications with effective database management capabilities that offer contextual relevant data, constant availability, real-time access, global access, and scalability.

Sitecore AI offers solutions that use machine learning to personalize customers’ online experiences, increasing the power of Sitecore Experience Platform™ and Sitecore Content Hub™ to deliver personalization at scale. Sitecore’s Customer Data Platform can capture, unify, and activate omnichannel customer data. It can create profiles from historical, transactional, or behavioral data for optimal visibility across unique customer journeys.

Sitecore AI solutions analyze customer behavior, learn where each person is in their shopping journey, and determine the best content to continuously optimize their experience.

Chapter 5

How Sitecore can help

Remember the barista example? Yes, it was Starbucks, and it has turned to a hyper-personalized in-app experience with real-time offers based on preferences, activity, and past purchases, leading to increased revenue and transactions.

From baristas who deliver personal service to brands that do on a mass scale, hyper-personalization can work on so many levels.

To learn more about how Sitecore can help you start hyper-personalizing, reach out to one of our experts today.

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