How AI in Omnichannel is Supercharging Pharma Growth

Customer expectations have changed significantly over the last decade and even more over the last few years. An abundance of sophisticated highly personalized digital channels and services from companies such as Amazon and others means customers today expect a high level of convenience and personalisation when interacting with businesses. 

This is no less true of healthcare practitioners (HCPs), patients and payers, and the pharmaceutical industry’s slowness to adopt omnichannel business models and marketing and communications strategies has made poor experiences with drug providers all the more frustrating for its customers.

Conversely, the early adopters are standing out from the pack and gaining attention and traction—in addition to seeing increases in revenue, marketing efficiencies, prescriptions, and HCP satisfaction. 

The importance of personalised customer experiences

The facts are clear: customers prefer omnichannel experiences.

According to the a study my McKinsey in 2021, roughly two-thirds of all customer interactions are now digitised. Of those two-thirds, half consist of remote human interactions and half of the use of self-serve digital tools. 

Furthermore, the average number of channels used by customers has increased from five (predominantly email, in-person, phone, website) to ten (with the addition of mobile apps, video conferences, web chats and web searches) in just five years. 

Companies that excel at omnichannel approaches are seeing important benefits including seeing increases of 5-10% in revenue, 10-20% in market efficiency and cost savings, 3-5% in prescribers, and 5-10% in HCP satisfaction among pharmaceutical companies that have implemented an omnichannel approach to customer interactions. 

For omnichannel to be effective, it must be personalised. 

Personalisation here means two things.

1. First, a personalised experience. Going beyond simple multichannel approaches, an omnichannel approach offers a holistic, consistent experience, informed by each  customer’s unique journey which can only be done with Artificial Intelligence due to the large variation in customer journeys across channels.

Consider a practitioner who starts a conversation about a new drug on the phone, and needs more information but doesn’t have time just then. They switch to a self-service tool on the drug company’s website, and upon logging in, find the information waiting for them. To better understand a drug interaction, they drop into a web chat to ask a few questions, and are greeted by somebody who’s already aware of their journey and can offer fast, pertinent, personalised information. The HCP is satisfied with their experience and has all the information they need to start prescribing. 

Over time, and thanks to the use of AI, the system initial predicts which journey is optimal for that customer based on previous customer journeys with similar attributes, and then it learns the practitioner’s preferences and habits and becomes more and more accurate in optimising that journey, while the human representatives are given insights into the best times and channels for contacting the practitioner. 

2. Second, a personalised offering. By understanding practitioners’, patients’ and payers’ preferences, needs, desires, perhaps even those they aren’t aware of themselves, you can offer better-fitting solutions, services and products. It should look like a happy coincidence that the right information is appearing just when you were about to search for it. Have you had that experience yourself? I have, and immediately I have realised – it was driven by prescriptive and cognitive AI.

For practitioners and payers, this might mean understanding treatment preferences, areas of expertise and interest, online affiliations with networks, groups, and other providers, and then recommending therapies, both traditional and digital, that match this profile.

For patients, it could involve understanding their symptoms, drug interactions, and treatment preferences, but also their lived experience as captured through wearables, smartphone applications, and more.

For the best results, a certain synergy between in-person and digital channels is necessary. Sales reps, community managers and others can engage in open-ended, dynamic conversations with practitioners, payers and patients, capturing information and data that may otherwise be missed, and yet is necessary for a holistic, 360º view of one’s customer. 

For personalisation at scale, pharma needs AI. 

This kind of personalisation can only be achieved at scale, for hundreds or thousands of practitioners and patients, with the use of AI.

Collecting, organising, and analysing the data necessary to provide a holistic, personalised experience for practitioners, patients and payers means leveraging AI and machine learning (ML) algorithms, which are particularly adept at distilling meaningful and impactful insights from millions of data points, in a way human being simply cannot. 

Of course, in-person sales reps still have a valuable role to play here, as the above studies show that 67% of individuals still find in-person meetings to be a key indicator of how much a supplier values a relationships, and 59% of customers won’t buy from a supplier unless they’ve met in person at least once. 

And by enabling a lean team of representatives to manage hundreds of customer-centric partnerships, pharma businesses can increase revenue, reduce costs, and improve relationships with customers. 

How pharma’s making use of AI and omnichannel

Early adopters are already making good use of AI and omnichannel, with encouraging results. 

One global pharma company based in Germany saw significantly increased engagement (7-15%) in a biologic using an AI-powered approach that involved analysing sales and HCP data and developing a novel predictive method for identifying HCPs who were prescribing immunology therapies, gain a new understanding of treatment decisions, and uncover new segments with an interest in immunology biologics for their patients. This translated into similar increases in their sales. Once developed, the model was applied to additional regions across Europe. 

A second example comes from a multinational pharmaceutical company in Japan. Their omnichannel featured a robust analytics component connected to datasets from market-research surveys, CRM, and 3rd-party data, culminating in a holistic view of HCPs featuring more than 100 elements. This enabled the company to identify approximately 8,000 HCPs with a high likelihood of prescribing at launch, and roughly 2,400 classed as ‘early adopters.’ The model also provided key insights, like the need for at least two in-person, explanatory visits per month for initiation. These efforts resulted in a 7% increase in patient coverage. 

There are some challenges…

Of course, the kind of transformation necessary to adopt a fully omnichannel approach to customer experience comes with certain challenges. These include:

● Data limitations. The real challenge here is overcoming the common misconception among pharma companies that they have insufficient data to begin implementing an omnichannel approach. While quality and quantity of data are, of course, key factors in training AI and ML algorithms, most pharma companies have more than enough data to begin trialling omnichannel in certain therapies, markets or regions. 

To move forward and continue improving quality and quantity, pharma companies should focus on collecting as much information as possible on sales, HCP and patient interactions and characteristics, and evolving market data. 

● Legal and compliance. Data is the backbone of the omnichannel customer experience, which aims to provide seamless experience for customers, whether they’re doing a face-to-face detail, or browsing on a device, all whilst making them accurate, personalised offers. A frictionless experience means customers choose you over competitors who fail to offer the same experience. GDPR complicates this. Achieving omnichannel relies on systems storing and accessing vast amounts of big data that is amassed as customers interact with them. Companies have spent years collecting this data, and now have to retrospectively assure their methods meet new standards. The key areas are that companies of 250 plus employees must appoint an independent data protection officer to ensure that the data standards are met and customers must give active consent for businesses to use their data for marketing or profiling.  In general, profiling is not approved if the processing of personal data takes place solely automatically, and the decision of the data subject based thereon has a legal effect or substantially affects it in a similar manner. However, in spite of this broad interpretation, personalised marketing and advertising, for example, is not covered by this prohibition* because it has no legal effect and does not significantly affect the person concerned in any other way. This is very useful for omnichannel systems as essentially it is personalised marketing. (*Source: Schürmann, Rosenthal, Dreyer law firm, Berlin). However, despite this, it still requires the obligatory consent pop-up.

● Talent. It’s true, data scientists, engineers and designers and transformation officers are scarcer in the pharmaceutical space than in other domains. But there are long-term solutions to this that are well worth pharma’s investments. These include starting with a small team whose successes can be later taught and replicated elsewhere, and investing in training and development programs to either upskill in-house talent or provide support for data scientists and engineers who lack healthcare knowledge. Highly collaborative, agile teams of non-industry data scientists working closely with industry experts is also a viable alternative. 

● Adoption. One of the most common challenges businesses face when implementing an omnichannel approach is resistance from within. Omnichannel often requires significant cultural and processual changes, which cannot happen without all stakeholders being on board. 

To achieve this, businesses must provide training to explain why the change is being made and how it will improve the company, its relationship with clients, and the lives of its employees. A change manager or even an entire department is a worthwhile investment for businesses as large as a pharmaceutical company. I explore this in greater detail in my article, How to create a digital transformation in a business as big as a pharmaceutical company.

…but also some big rewards

An omnichannel business and communications model, as we’ve seen, is well worth the investment when done properly. It opens the door to more sophisticated explanatory analyses of markets, more accurate and insightful predictions on customer behaviour, emphasises an approach to customer experience that’s holistic and personalised, and enables sales and marketing teams to be more agile and more effective. 

Pharma companies are already seeing concrete results from adopting an omnichannel approach, including increased customer engagement, prescribing, patient and practitioner loyalty, and overall revenue uplift.