5 hot trends about hyper-personalization
Today a good customer journey begins with understanding HCP's intentions, drives, and expectations. And the data on a general population of clients is no longer so scarce: brands have started communicating with their customers in a most direct, interpersonal manner.
As pharma companies strive to offer tailored and sophisticated #cx , pharma has to find new smart ways how they can reach their audience.
What is hyper-personalization?
Hyper-personalization is a marketing approach that creates unique experiences for clients by drawing on previously collected and real-time data. However, one of the best examples of hyper-personalization comes from large organizations that target niche markets with products or services that have high lead values—and thus are willing to invest in this effort. This is why hyper-personalization drowns so much attention from marketers in the pharma and life sciences industries. A hard truth: a customer who feels like a part of something will keep coming back.
So here come the key indicators of successful hyper-personalized communication:
Personalization and hyper-personalization: how to tell the difference?
If you’re new to the topic, you may assume that there is no drastic difference between the two terms. And yet, we can draw a pretty distinctive line in order to explain in which way personalization and hyper-personalization are not the same things.
The first thing you need to know is that the two terms actually imply a different scale of targeting. Personalization means that the marketers will be dealing with groups of customers that are shaped according to common patterns. At the same time, when we are talking about hyper-personalization, the marketer’s goal is to create a very unique, one-of-a-kind experience tailored specifically for a single client.
Also, hyper-personalization as a marketing practice and approach utilizes more advanced practices and technologies, allowing marketers to automate processes, make real-time predictions, use Dynamic Creative Optimization, etc. The engagement of all those technologies helps to provide a better customer experience and still, brings some technical challenges for the marketing team either.
How to implement hyper-personalization on the ground
It is well-understood that all pharma marketers literally crave the personal data of the clients, gathering all client data available out there bit by bit. At the same time, the pharma and life sciences spheres are well known for their firm restrictions considering patient personal data disclosure and personal medical record privacy. All those restrictions create additional obstacles and challenges for marketers who are responsible for collecting data. However, there are many smart ways how marketers can actually collect customer data, which we will discuss further.
When implementing hyper-personalization as an approach and a cornerstone principle of your marketing strategy, you need to go through several preparatory stages. Here we will roughly outline what those are, so you can get a general impression of the introduction of the approach.
Stage 1 - Data collection There are several ways how you can gather relevant user data from the very beginning. Such methods as AI-powered surveys, feedback collection, and questionnaires will help you gather the initial data for creating a rough vision of client properties, behavioral patterns, and any other relevant information.
Stage 2 - Analysis and distribution Before you will be able to address your clients at an individual level, you still need to start by segmenting the users by some of the most well-articulated features. At this stage, the main goal of the marketers is to reveal as many valuable behavioral patterns and recent market trends that influence clients’ decision-making.
Stage 3 - Customer journey development Quite frequently, hyper-personalization can not be fully implemented without some other advanced technologies and practices that are widely used in pharma marketing today. For instance, smart omnichannel engagement helps choose the most relevant channel and time for communication with the customer. The designed hyper-personalized customer journeys are to be tested in the field right after.
Stage 4 - Distribution and evaluation After the implementation of hyper-personalization practices in a test mode, it is vital to measure the impact of the technology on communication with clients, content production, etc. At this stage, you will have to deal a lot with fine-tuning the system and implementing automation practices that will help to come up with better, more relevant content that can be suggested for approval to the content managers.
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5 Ways How you Can Use Hyper-personalization for Your Benefit
Today hyper-personalization is applied in so many fields and can bring so many benefits to businesses, users, marketers, and researchers, it may take quite a lot of time to cover them all. This time we will focus on some of the major ways in which pharma and life sciences companies and their clients can benefit from hyper-personalization technologies in 2023.
Hyper-personalization via offerings and questionnaires
Probably it is the most obvious and yet also a pretty amazing way how you can achieve hyper-personalization with the least investment. Nowadays, customers are caring more and more about the services and products they use, so many customers will actually be ready to give companies fairly, viable feedback in case if they believe that the companies will react to their opinions with concrete actions.
Hyper-personalization as a user feature
In 2023 you no longer have to convince most of the users to share their personal data if you can showcase the benefits of the technology. Services like Spotify suggest music to the user based on the user content, and online clothing vendors offer mailings featuring only those products that match in size and optionally fit any other criteria. So if you can find a way the right way to catch the users’ attention, making emphasis on how they can save their time and resources by cooperating with brands, the customers won’t hesitate to get their hands on the new opportunities.
Hyper-personalized chat-bots
The field of AI-powered chatbot creators has embraced hyper-personalization. Nowadays, such bots widely apply machine learning so they can adapt better to the questions posed, find exclusive, hyper-personalized answers, which are based on the data that was previously collected and analyzed. Naturally, such bots can not fully supplement real HCPs. However, they already contribute greatly to healthcare, patient treatment and patient education.
Hyper-personalization for achieving relevance, engagement, and trust
As was mentioned before, nowadays, clients have greater expectations from brands, their products, and services. This statement is especially true for the pharma and life sciences industries, within which the patients always expect to get the most highly qualified assistance and service. With the help of hyper-personalization, the companies can showcase to the patients how more advanced can be a great benefit in terms of the quality of service they receive.
Hyper-personalization for making viable predictions
There are many unknown variables every time pharma companies are targeting and communicating with patients, potential clients, or HCPs. However, with the help of hyper-personalization and smart data acquisition, you can actually clear up many things.
If you will be able to develop clients’ individual profiles, including every little piece of information that you will be lucky to collect during the interactions with the client. And this time, we once again need to highlight that hyper-personalization is just one of the approaches that should be supported by other modern technologies. For instance, when we are talking about creating a centralized, regularly updated client database, it will be convenient to use an omnichannel approach.
Pharma marketers now have the ability to choose which channels they will use for communication, automate some of their processes, and leverage big amounts of data in order to make predictions about customers' actions and intentions.