Pharmaceutical companies struggle with a complex and, often, poorly managed partner, customer and distribution network. It’s not surprising, given the makeup of most large pharma companies. Large, often disconnected product portfolios are built through discovery — both internally and externally with academia and biotech partners — and global clinical trials, using a network of clinical research organizations, investigators, other experts and patients. Suppliers, distributors and often contract manufacturers are all integral to making and supplying products. And at the customer level, companies work with healthcare practitioners, pharmacists, payers and patients.
This web of partners and customers is growing in complexity — both logistically and from a compliance point of view. Yet this way of doing business remains the same. Communication and requests are conducted via email and through call centers without a connected and intelligent way of routing work and queries. Service level agreements are often poorly developed, and governance processes are often inconsistent across the distribution and customer ecosystem.
While different parts of the pharmaceutical business are deploying digital technologies, an opportunity exists to transform the customer and partner value chain with progressive digital tools and platforms. Customer service and support centers are now implementing artificial intelligence (AI) by analyzing both structured and unstructured data and also leveraging natural language processing (NLP) for omnichannel engagement models with the customers. But how is this actually achieved?
A single source of truth
Synchronizing systems around the customer for a customer-centric approach begins with bringing together data from disparate sources and creating a single view of the truth through a common data model. In this way, companies have a big-picture view of every customer service request, including the distribution chain.
Once the data is in place, the next step to improving customer engagement and ensuring regulatory compliance is to embed the common platform with digital tools and technologies. Combining NLP with AI, machine learning and workflow automation enables increased customer engagement with sound governance and improved compliance.
How do these digital technologies improve engagement and compliance?
Customer service support and the operations space have evolved over the years from a manual, labor-intensive and software-centered business model to a more dynamic multichannel customer engagement business model. This new model facilitates omnichannel engagement with the customer using multiple devices. AI- and machine learning-powered chatbots are being leveraged for quick response management, and digital capabilities are tightly integrated with intelligent workflow management tools.
For example, today a customer can raise a service request through an email, phone call or a text message, or even talk to a live chat agent. Digitally enabled customer service engagement centers can now seamlessly bring in the request from different channels into one homogenous customer engagement platform for action. From there, the request is processed using digital tools to identify the intent of the case or request — who it is aimed at, what the objective is – and to create groups in which to classify the case based on importance. This is achieved by using NLP to create an entity score, match this score with a subgroup and route it to the right place to ensure proper follow-up.
The customer may then choose to follow up with a phone call or through a chatbot or online feedback form. This is where an AI capability (or the more traditional customer service agent) should be able to view all of the various communication forms and frame the response accordingly.
To achieve this, AI and ML tools learn from previous interactions, continuously improving on the quality of responses. The AI learning also needs to extend to compliance, adherence to SLA guidelines, as well as any regulatory restrictions on what can and cannot be shared. For example, if a customer asks for the available stock of a particular drug, the pharma company is not allowed to address that question according to U.S. government regulations. So, the response needs to be framed appropriately. The rules will be different in each country, so the AI/ML-enabled automated response app should be able to learn and adapt accordingly.
Preconfigured responses based on the type of the request are then configured using data science and AI/ML techniques. AI and ML capabilities also help to determine the urgency or sensitivity of a case, and how best to ensure that compliance requirements are met within the timelines and SLA metrics.
In addition, analytics will play a key role in verifying, validating and improving customer service. Predictive models can be used to strengthen the human response team by understanding peak cycles, such as a new drug launch, natural disaster areas and so on.
By taking a progressive digital approach to managing the communication network with partners and customers, companies can mitigate many problems while improving customer engagement. This is enabled by having a single view of the customer, using robust data analytics capabilities thanks to AI and ML — to predict risk and compliance needs, and ensuring that the company is always ready for regulatory inspections and has the necessary information at hand. With the emergence of AI and ML techniques, it has become easier to achieve customer engagement needs with more enriched analytics and insights, thus allowing enterprises to not only automate customer engagements but also excel in customer experience.