Patients want and expect more from pharmaceutical manufacturers, and naturally it is in manufacturers’ best interests to deliver. COVID-19 caused a significant drop in rep access and outpatient visits, while call centers were inundated. Meaning, the pharmaceutical industry must find an alternative to live customer support: chatbots. Chatbot technology can lead to faster response times and reduce customer service costs by 30%.1

Application Areas

Chatbot technology can have true impact in several key areas:

Information sharing: Pharmaceutical websites contain a lot of information, which can be overwhelming to patients. Chatbots streamline this process and educate patients (or HCPs) about health conditions, drug usage, and next steps, all while alleviating patients’ fears of side effects.

Process automation: An AI-powered chatbot integrated into a CRM system can automate required, repetitive tasks, such as scheduling meetings, sending reminders, placing sample orders, etc.

Insight gathering: Chatbots store valuable conversation data which unveils critical insights that can be used for planning marketing campaigns and identifying consumer behavior.

Best Practices for Implementation and Audience Adoption

After identifying where to implement chatbot technology, these best practices are key:

Intent recognition: Chatbots should have the ability to understand context in conversations and guess what the user is requesting, even if phrased unexpectedly.

Continuous learning: Examine conversations on Google, social media, and call centers to determine popular questions and how customers word them. Engage language consultants to make the conversation more humanlike. Use a closed-loop, continuous learning approach to train the bot in answering increasingly complex questions.

Next steps for unrecognized inputs: Chatbots should have the ability to provide next steps to avoid a dead end, either by reframing the question or redirecting to a customer care representative.

Error accommodation: An undo command enables users to continue on the correct path when they incorrectly type something. The bot should recognize misspellings, and either assume the correct word or clarify when needed.

Adverse events: Monitoring conversations for adverse events (AEs) is a top priority. Chatbots should have the ability to detect these in inputs received.

Multichannel support: To provide comprehensive support throughout the patient’s journey, chatbots should be available where the users are (e.g., websites, social media). Deploying chatbots on social media platforms also provides the chatbot with the latter’s customer base.

Historically, the pharma industry has been slow to adopt chatbots; however, COVID-19 has shown the cracks in the ecosystem where chatbots can effectively be used to increase efficiency and convenience. By 2023, healthcare chatbot interactions are expected to reach 2.8 billion,2 representing 10% of all chatbot interactions across key industries, and the market is expected to grow at a compound annual growth rate of 26.29%3 showing an increased commitment from pharma companies toward development of chatbots.

References:

1. https://www.ibm.com/blogs/watson/2017/10/how-chatbots-reduce-customer-service-costs-by-30-percent.

2. https://www.juniperresearch.com/press/press-releases/healthcare-chatbot-interactions-reach-2-8bn-2023.

3. https://www.prnewswire.com/news-releases/global-chatbots-in-healthcare-market-to-reach-498-1-million-by-2029–300916816.html.

  • Croom Lawrence

    Croom Lawrence is Strategy Lead at Merkle Health. Croom is an agency leader in the strategy group at Merkle. He advises U.S. and global marketing organizations on brand leadership, digital transformation, and connected experience design to drive greater customer relevance and business performance.

  • Keya Paul

    Keya Paul works in strategy at Merkle Health. She has more than 8 years of experience in the areas of strategy, market research, data analytics, and digital marketing working with clients across Health, Finance, Insurance, Retail, and Nonprofit.

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