Embracing hyperpersonalisation in the pharma industry

Senior Manager, Customer Transformation, PwC Switzerland

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Hyperpersonalisation harnesses AI and big data to transform linear customer journeys into dynamic experiences across multiple channels. It’s already being implemented in industries such as retail and entertainment. But how can companies in pharma and life sciences also use hyperpersonalisation to operate more efficiently and effectively, cut costs and boost profitability?

In my last post, I left you with three questions to ponder: What stage are you at in terms of omnichannel engagement and hyperpersonalisation? What pain points could it help you address? What are your aspirations? If you’re ready to go further down the rabbit hole, here, as promised, is a deeper dive into what hyperpersonalisation is all about.

Personalisation in interactions with customers has been around for quite a long time. But even personalised journeys tend to be fairly linear, taking the customer along a straightforward path from attraction, engagement and conversion to service and retention, often via a single channel. Hyperpersonalisation transforms this linear journey into dynamic, real-time, contextual experiences tailored to individual customer preferences and delivered responsively via multiple channels. It’s all possible thanks to AI and data analytics.

Let’s look at some examples of how personalisation is evolving into hyperpersonalisation through a more sophisticated approach to data, segmentation, technology, timing and the channels used.

Personalised experiences draw on basic information such as the customer’s name and purchase history. Hyperpersonalisation goes much further, harnessing behavioural, contextual and predictive data in real time. So instead of getting an email saying “Hi Grace, here’s 10% off your next purchase,” while browsing shoes our customer receives a push notification saying “20% off these shoes – perfect for today’s weather.”

Personalisation groups customers into broad segments based on demographics or past behaviour. For example, females aged between 25 and 40 are shown handbags. Hyperpersonalisation goes further, creating segments of one based on micro-moments and unique preferences. A frequent traveller, for instance, might receive recommendations for carry-on bags tailored to their favourite airline’s size requirements.

Personalisation relies on CRM, marketing automation and rule-based automation. Hyperpersonalisation builds on AI, machine learning and predictive analytics. So instead of using a static purchase history to generate a basic recommendation along the lines of “Customers who bought this also bought...,” a hyperpersonalised, AI-powered platform analyses browsing patterns and cart additions in real time to suggest products tailored to the user’s current shopping behaviour.

While a personalised system delivers content or offers at a pre-set time, for example via scheduled email campaigns, a hyperpersonalised platform responds dynamically in real time based on the actions of customers. So rather than sending a notification every Monday morning to remind users to log their weekly workouts, a fitness app might detect a gap in workout activity during the user’s usual exercise time and send a motivational message.

A conventional personalised experience is provided via a single channel, for example an email campaign. A hyperpersonalised experience, by contrast, is delivered across platforms. So instead of receiving a credit card promotion for dining discounts emailed to all cardholders, for example, the customer is given a tailored offer for dining rewards via email, sees it highlighted in their app’s rewards section and is notified on WhatsApp about nearby restaurants eligible for cashback.

Personalisation vs. Hyper-Personalisation

Personalisation

Basic customer information like name, purchase history

Customer gets an email saying, "Hi Grace, here's 10% off your next purchase."

Hyper-Personalisation

Behavioral, contextual, predictive data in real time

“Grace” browses shoes receives a push notification with "20% off these shoes – perfect for today’s weather!"

Personalisation

Groups customers into broad segments based on demographics or past behavior

Female customers aged 25-40 are shown handbags

Hyper-Personalisation

Individuals based on micro-moments, unique preferences

A frequent traveler sees recommendations for carry-on bags tailored to their favorite airline’s size specifications

Personalisation

CRM, marketing automation, rule-based automation

A retail website uses a basic recommendation engine that suggests "Customers who bought this also bought..." based on static purchase history

Hyper-Personalisation

Leverages AI, machine learning, and predictive analytics

AI-powered platform analyses browsing patterns and cart additions in real time to suggest products tailored to the user's current shopping behavior

Personalisation

Delivers content or offers at a pre-set time (e.g., scheduled email campaigns)

A fitness app sends a notification every Monday morning to remind users to log their weekly workouts.

Hyper-Personalisation

Responds dynamically in real time based on customer actions

The app detects a gap in workout activity during the user’s usual exercise time and sends a motivational message

Personalisation

Single-channel experiences (e.g., email campaigns)

Customer receives a credit card promotion for dining discounts that sent via email to all cardholders

Hyper-Personalisation

Omnichannel experience across platforms

A customer gets a tailored offer for dining rewards via email, sees it highlighted in their app’s rewards section, notified on WhatsApp about nearby restaurants eligible for cashback

It’s easy to see how a hyperpersonalised approach might be adopted in an industry such as retail or streaming entertainment. But what about pharma? How is hyperpersonalisation supposed to work under our tight data privacy and consent policies? Is the investment justified? Does our organisation have the right mindset to try, test and learn? Let’s briefly look at three real-life cases to see what’s already being done in the industry despite these apparent challenges.

A top player in pharma and diagnostics has used AI to seamlessly automate content creation and accelerate the content review and approval process. The result has been improved personalisation creating highly relevant, consistent content. The company has seen a 20% reduction in costs, a 3x improvement in customer engagement, a 40% increase in content reuse and a 50% acceleration in speed to market.

A global pharma company has used hyperpersonalisation to transform patient care and improve patient adherence and outcomes. An online platform was introduced recently offering disease management with a telehealth program to connect users (patients) with healthcare professionals. It helps them to find a doctor, navigate the healthcare system, potentially lower their costs and get medications delivered directly to the home.

A global biotech company has redefined customer segmentation to be more personalised in engagement and make internal processes more efficient. It now has a scalable framework across geographies and TAs as well as a standardised CRM approach for harmonised customer data. The company can now do tailored messaging based on behavioural dynamics and can engage customers personally on the basis of their channel and context preferences.

Conclusion

Do you feel inspired? Despite the apparent constraints, these examples prove that pharma and biotech companies are already finding imaginative and effective ways to enhance the experience of their customers – and ultimately their profitability. Watch this space for my next post on how to make hyperpersonalisation happen and reach out if you want to discuss the matter further with me.