Hyper-Personalization – What It Is and How to Use It
Posted: Wed Dec 18, 2024 7:17 am
For some time now, digital marketers have known that personalization drives positive results. It’s the key to creating a great customer experience. And it’s also the main way for a brand to forge and develop relationships with its customers.
But the days when adding a customer’s name to an email subject line was enough of a personalization effort have come and gone. Today’s customers expect more – and they’ll gladly go to your competitors to get it. Unless you can give them what they crave, instead.
And that’s where the latest evolution of marketing personalization comes in. It’s called hyper-personalization, and it involves the use of vast troves of customer data and sophisticated artificial intelligence (AI) to deliver relevant content and messaging to each individual customer.
It’s the secret sauce that’s driving some of buy telegram number list today’s best-known brands to new heights. And you can use it too if you know how. To help, here’s everything you need to know about hyper-personalization. We’ll cover what it is, how it works, and why it’s different from conventional personalization. Then we’ll go over four major hyper-personalized marketing strategies and some examples of them in action.
If that sounds good to you, let’s dive in.
What is hyper-personalization?
In a nutshell, hyper-personalization is a combination of marketing and data analytics. It’s a process whereby marketers combine real-time customer data from multiple touchpoints and channels to create an ultra-detailed, per-customer marketing strategy. The data makes it possible to deploy customized advertising, products, services, and offers for each customer. And all of it’s fine-tuned for relevance to maximize consumer response.
How does it work?
Needless to say, hyper-personalization takes a lot of work. It’s only possible thanks to the latest developments in AI technology, which uses machine learning and deep learning to analyze real-time data about customers, looking for patterns that could indicate what each customer is looking for. That kind of deeper understanding of a customer’s intentions allows a brand to be in the right place at the right time – delivering the right products and services at the perfect point in the customer journey.
How is it different from conventional personalization?
Conventional personalization is different from hyper-personalization in a few critical ways. The first is that it doesn’t involve attempting to understand the individual preferences of each customer. It only serves to create the appearance that marketing messages are aimed at the individual.
That’s why conventional personalization typically doesn’t involve the use of any data beyond the customer’s name, purchase history, and location. It might, for example, include an email with the customer’s name in its subject and heading that advertises items related to a product they’ve purchased previously. But that same email (except for the name) would go out to every customer that purchased that product; the personalization is little more than an illusion.
A hyper-personalized version of that same email might take into account that the specific customer had recently searched for items in the same product category. It could be triggered by them posting a review of the original product on social media.
Or it could be based on predictive analytics that examines the customer’s location data before and after their original purchase. Then the offer they’d receive would be timed to catch them in a particular place, going beyond simple pre-determined customer segmentation.
Developing a hyper-personalization strategy
Under the hood, most AI platforms built to enable marketing hyper-personalization to work in a similar way. What’s important is how the marketer uses them to create hyper-personalization strategies. And there are four steps involved:
1. Real time data and long-term data collection
The first step in the process of developing a hyper-personalization strategy is to begin collecting relevant data about your customers. And for hyper-personalization, there’s no such thing as too much data. But in general, the types of data required fall into two categories: quantitative and qualitative data.
Quantitative data includes all information about how the individual interacts with the business. That means website activity data, personal and transactional information, social media contacts with the business, and previous interactions with customer service they may have had. Some of these data sources may be collected in real-time, and others from historical data sources. Together these paint an ongoing picture of their relationship with the business.
Qualitative data, on the other hand, relates to the customers’ motivations and feelings. To collect it, most businesses rely on online surveys, product reviews, and post-interaction questionnaires. They may also purchase consumer data sets from 3rd-party data providers to fill in any gaps in their own data.
Examples of quantitative and qualitative data.
Quantitative vs qualitative data. Image: GeoPoll
2. Customer segmentation using customer data
Although true hyper-personalization addresses each customer individually, the process does still require the creation of customer segments (for messaging at the group level). So the next step is to create those customer segments, preferably creating the smallest groupings possible.
But the days when adding a customer’s name to an email subject line was enough of a personalization effort have come and gone. Today’s customers expect more – and they’ll gladly go to your competitors to get it. Unless you can give them what they crave, instead.
And that’s where the latest evolution of marketing personalization comes in. It’s called hyper-personalization, and it involves the use of vast troves of customer data and sophisticated artificial intelligence (AI) to deliver relevant content and messaging to each individual customer.
It’s the secret sauce that’s driving some of buy telegram number list today’s best-known brands to new heights. And you can use it too if you know how. To help, here’s everything you need to know about hyper-personalization. We’ll cover what it is, how it works, and why it’s different from conventional personalization. Then we’ll go over four major hyper-personalized marketing strategies and some examples of them in action.
If that sounds good to you, let’s dive in.
What is hyper-personalization?
In a nutshell, hyper-personalization is a combination of marketing and data analytics. It’s a process whereby marketers combine real-time customer data from multiple touchpoints and channels to create an ultra-detailed, per-customer marketing strategy. The data makes it possible to deploy customized advertising, products, services, and offers for each customer. And all of it’s fine-tuned for relevance to maximize consumer response.
How does it work?
Needless to say, hyper-personalization takes a lot of work. It’s only possible thanks to the latest developments in AI technology, which uses machine learning and deep learning to analyze real-time data about customers, looking for patterns that could indicate what each customer is looking for. That kind of deeper understanding of a customer’s intentions allows a brand to be in the right place at the right time – delivering the right products and services at the perfect point in the customer journey.
How is it different from conventional personalization?
Conventional personalization is different from hyper-personalization in a few critical ways. The first is that it doesn’t involve attempting to understand the individual preferences of each customer. It only serves to create the appearance that marketing messages are aimed at the individual.
That’s why conventional personalization typically doesn’t involve the use of any data beyond the customer’s name, purchase history, and location. It might, for example, include an email with the customer’s name in its subject and heading that advertises items related to a product they’ve purchased previously. But that same email (except for the name) would go out to every customer that purchased that product; the personalization is little more than an illusion.
A hyper-personalized version of that same email might take into account that the specific customer had recently searched for items in the same product category. It could be triggered by them posting a review of the original product on social media.
Or it could be based on predictive analytics that examines the customer’s location data before and after their original purchase. Then the offer they’d receive would be timed to catch them in a particular place, going beyond simple pre-determined customer segmentation.
Developing a hyper-personalization strategy
Under the hood, most AI platforms built to enable marketing hyper-personalization to work in a similar way. What’s important is how the marketer uses them to create hyper-personalization strategies. And there are four steps involved:
1. Real time data and long-term data collection
The first step in the process of developing a hyper-personalization strategy is to begin collecting relevant data about your customers. And for hyper-personalization, there’s no such thing as too much data. But in general, the types of data required fall into two categories: quantitative and qualitative data.
Quantitative data includes all information about how the individual interacts with the business. That means website activity data, personal and transactional information, social media contacts with the business, and previous interactions with customer service they may have had. Some of these data sources may be collected in real-time, and others from historical data sources. Together these paint an ongoing picture of their relationship with the business.
Qualitative data, on the other hand, relates to the customers’ motivations and feelings. To collect it, most businesses rely on online surveys, product reviews, and post-interaction questionnaires. They may also purchase consumer data sets from 3rd-party data providers to fill in any gaps in their own data.
Examples of quantitative and qualitative data.
Quantitative vs qualitative data. Image: GeoPoll
2. Customer segmentation using customer data
Although true hyper-personalization addresses each customer individually, the process does still require the creation of customer segments (for messaging at the group level). So the next step is to create those customer segments, preferably creating the smallest groupings possible.