Customer experience: the not-so-secret key to success and why AI is essential

Most businesses understand that customer experience (Cx) is critical to success. Happy customers are loyal customers, after all.

And there’s a wealth of data and innumerable success stories to support this. Take any super-successful, fast-growing company today, and you’ll find the focus is firmly on customer experience. Uber has replaced taxis; AirBnB is outstripping hotels; Tesla’s ahead of most other car companies; and Amazon… Well, Amazon’s got just about every other retail store on Earth scrambling to compete.

The companies listed above owe their Cx success to two key factors.

First, they have made the experience dramatically simpler for customers. Need a car? Push a button. Forgot the milk? At your door with a drone in 5 minutes. An apartment in downtown New York on a small-business budget? Just a few taps away. Everything, from user interface to internal process, has been shaped with the customer, rather than the product, in mind.

Second, they approach customer experience in a holistic fashion. The myriad business units and various touch points customers encounter on a day-to-day basis have been united into a single, simple, enjoyable customer experience.

Meeting these targets is not easy. It often requires fundamental changes to the way businesses interact with customers and each other. This is particularly true of the pharmaceutical industry, which has been slow to adopt a customer-centric model, preferring product-driven strategies to maintain profit margins.

Customer experience in pharma

Customer experience in pharma has often played second fiddle to product-driven strategies.

Historically, this worked well for pharmaceutical companies, which were able to leverage their sheer size and unique capabilities in research and production. In fact, even today, a majority of pharma CEOs believe that new products are the most important factor for revenue growth, despite the fact that returns from R & D are lagging.

But according to Florent Edouard Global Head of Commercial Excellence at Grünenthal, this model “has alienated us, our customers, the patients, the public, and regulating authorities.” By focusing on products rather than practitioners and what they need to perform their jobs, pharma companies are missing opportunities to provide a better overall experience for practitioners, i.e., customers.

“Nurses and physicians still go online searching for the information they need,” continues Edouard. “That is exactly where we need to offer our services. … We can help them deliver better care by providing what they want to consume, rather than spoon-feeding them what we want them to consume.”

Another central confusion in pharmaceutical marketing comes from the debate over being patient-centric or customer-centric and the question of who exactly the customer is.

But pharmaceutical companies needn’t choose between serving practitioners, patients and even payers as customers.

Certainly, practitioners need support when it comes to gathering mission-critical information on a daily basis, and making their jobs easier and ensuring their experience is holistic and enjoyable will go a long way in meeting the two key targets—customer-centricity and holistic customer experiences—defined above.

But patients also interact with drug companies, and there’s a lot to be said for making patient interactions easier—and more frequent.

For one, it will alleviate pressure on practitioners and pharmacists, who have to field many questions that pharmaceutical companies could easily answer in their place. Second, it helps familiarise patients with the company that’s actually producing the drug and having such a profound impact on their life.

Capturing the positive attention of patients as customers can help ensure brand loyalty. This is especially true for those with chronic diseases like diabetes and cancer, where positive patient-customer experiences have lasting medical, personal, and commercial consequences.

So how can this be done at scale? How can a pharma company meet and surpass the needs of the hundreds of thousands of practitioners and patients it interacts with?

The impact of AI on customer experience in pharma

Savvy businesses—pharmaceutical companies included—know that there’s no way to fulfil the needs of all those customers without the help of artificial intelligence (AI). It alone can organise, analyse, and draw insights from the millions of data-points available to pharmaceutical companies.

Customers today expect highly personalised solutions and experiences that put their needs (as patients, practitioners, etc.) front and centre. Some pharma companies are already taking the lead.

Take, for example, Novo Nordisk’s work in diabetes. Its AI-powered pens automatically track insulin dosing data and history and can securely communicate this data to a smartphone app. In addition, the company has paired with other insulin trackers in a non-exclusive data exchange and used this information to create a platform ecosystem that gives people with diabetes the “freedom to decide which solution works best for them.”

To be clear: Novo Nordisk hasn’t abandoned the sale of drugs. Instead, it has adopted a strategy known as “owning a disease” and interwoven its drugs into an AI-powered ecosystem that puts the diabetes customer—be it practitioner or patient—at the heart of the experience. And, incidentally, no matter what product the customer uses, they’re using Novo Nordisk’s platform.

“Owning a disease” is one useful model for creating a cohesive and holistic customer experience. And it makes a lot of sense for both patients and practitioners, who think of and tend to experience disease as a deeply personal, continuous journey, rather than a series of distinct “customer steps.”

There are many other models and ways AI can be embedded into the pharma value chain, including sales and marketing and patient adherence. AI enables businesses to create holistic, dynamic, personalised customer experiences at scale, which is something that humans alone simply cannot do.

Challenges (and solutions) of implementing artificial intelligence in Cx

There are a few key challenges to be met when creating an AI-powered, customer-centric product and service.

Data

Both quality and quantity of data is important. The kind of insights necessary to create a truly customer-centric experience come from a large set of high-quality customer data. Algorithms need to be trained with at least two to three years of historical data. A common challenge comes from mergers and acquisitions, where prior data is either of a different format or is simply unavailable.

While little can be done about lost customer data from the past, businesses looking to revolutionise their customer experiences with artificial intelligence should begin collecting high-quality data as soon as possible and ensure that M&A is keenly aware of its importance.

Skills and knowledge

The intersection of pharma and data science is relatively new. Strong talent with relevant experience is hard to come by, and delays in hiring and training can slow ambitions considerably.

Companies should think carefully about what kind of investment they want to make and how that will translate into human capital. They should work closely with dedicated digital transformation firms to create practical, meaningful, achievable roadmaps.

Business value

Many large organisations, pharma companies included, struggle to prove real business value on artificial intelligence projects and generate the kind of important returns they’ve been hoping for. This often happens when companies either buy an AI tool or launch an AI project without taking the time to properly plan for its implementation. Planning an effective AI strategy is an essential first step to solving challenges.

Privacy and security

Regulators and policies are finally catching up with data and its widespread collection, manipulation, and use. HIPAA, the GDPR, and the California Privacy Rights Act continue to evolve, and so businesses must keep a finger firmly on the pulse of regulatory requirements when it comes to data. This is true not only for ethical reasons, but also to protect companies against litigation and poor public image.

This means investing in training, dedicated tools, and whatever legal counsel is necessary to ensure your artificial intelligence projects are compliant.

Ethics and transparency

There’s no shortage of examples where machine learning (ML) has produced biassed results because it was fed biassed data or written in a biassed manner. While the results can be unsettling, even chilling, in all industries, they are especially so where people’s health is concerned.

To combat bias, firms must educate stakeholders on the dangers of AI bias; create or leverage existing resources to mitigate its effects when deploying AI; ensure human oversight is present as necessary; ensure the data used to train algorithms is relevant to the target population; and, most importantly, commit fully to producing, using, and championing bias-aware, inclusive AI solutions.

Why AI is essential for Cx in pharma

Pharma companies may be asking themselves, how much of a difference can it really make?

Quite a lot, actually.

According to a survey by McKinsey, prescribers who are satisfied with the patient prescription journey (defined as “interactions with patients from diagnosis and prescription to monitoring and follow-up”) are 70% more likely to prescribe a drug in the future. And when positive interactions with pharmaco representatives are added, the amount rises to 270% more likely to prescribe.

In pharma and beyond, focusing on a holistic customer experience, rather than individual touchpoints, is associated with anywhere from a 50% to 115% increase in customer satisfaction, and up to 15% more revenue.

Data and analytics plays a big part in this. The ability to collect data, build platforms and tools, and identify, analyse and action opportunities in patient and practitioner journeys is key—and this can most effectively, efficiently and cheaply be done with AI.

Conclusion

Customer expectations have evolved. Thanks to companies like Uber, Amazon, and Netflix, people’s daily lives are filled with personalised, dynamic, holistic customer experiences. To understand, predict, meet and exceed these expectations, pharmaceutical companies must make use of artificial intelligence, as it alone can analyse and offer insights on customer experience from the hundreds of thousands of data points pharmaceutical companies are (or should be) collecting.

While there are challenges, they can be met with proper guidance and planning, and a successful AI strategy for Cx can increase customer engagement and profit significantly.

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