CHATBOTS AND VOICEBOTS IN THE PHARMACEUTICAL INDUSTRY-OPPORTUNITIES AND ADDED VALUE
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CHATBOTS AND VOICEBOTS IN
THE PHARMACEUTICAL INDUSTRY
– OPPORTUNITIES AND ADDED VALUE
page no. 1
BRAIN STATION
-COMPANY PROFILE
Brain Station 51 in Germany and its sister company Brain Station 23 in Bangladesh are a team
of experienced business IT professionals and professional developers. We are proud to be one
of the most innovative IT leaders in the industry. Through the implementation of leading-edge
technologies, we work on challenging projects to provide comprehensive solutions that meet all of
our clients’ requirements.
We combine the finest technical craftsmanship with elegant and functional design to realise
digital experiences. Our focus is on the pharmaceutical industry with its high requirements for
safety, compliance and pharma-specific regulations and specifications, such as GCP (good clinical
practice), GLP (good laboratory practice) and GMP (good manufacturing practice). In addition,
the emphasis is on CDP (Customer Data Platform), BI (Business Intelligence) and reporting. Brain
Station has been successfully developing tailor-made solutions for the pharmaceutical industry for
many years.
Dr. Matthias Hansch, Managing Director, Brain Station 51
page no. 2
COGNIGY
-COMPANY PROFILE
Cognigy is a global leader in Conversational AI to support customer service automation. Its low-
code platform, Cognigy.AI, enables enterprises to automate contact centres for customer and
employee communications using intelligent voice- and chatbots.
With precise, reliable intent recognition, human-like dialogs and seamless integration into backend
systems, Cognigy.AI creates superior user experiences and helps companies reduce support costs.
Cognigy.AI is available in SaaS and on-premise environments and supports conversations in any
language and on any channel including phone, webchat, SMS and mobile apps.
Cognigy’s worldwide client portfolio includes Daimler, Bosch, Henkel, Lufthansa, Salzburg AG and
many more. For more information, please visit: www.cognigy.com.
page no. 3
SOCIETAL CHANGE
Societal change stemming from digitalisation and new technologies has experienced an
unprecedented level of acceleration due to the Corona pandemic in 2020. All sectors are affected
– both public and private. Retail, F&B, and tourism have been plunged so severely into crises by
lockdowns that many livelihoods are on the verge of ruin. Ideas are desperately being sought to
avert the threat of bankruptcy: The F&B industry is providing delivery services, the retail sector
is attempting to remain available online, and the travel industry is hoping for rebooking instead
of cancellations. In the private sector, home schooling and distance learning are becoming the
buzzwords, but with Germany’s lagging digitalisation, lessons have practically ground to a halt.
Governments are indebting their countries at the expense of future generations in order to be able
to pay compensations to affected industries.
At the same time, a vaccine against Covid-19 is expected to become available in many European
countries and the USA at the end of 2020. A vaccine that has been developed in record time and
passed all levels of testing. Emergency approvals are speeding up the market transition in the UK.
But the whirlwind of the pandemic has resulted in a boom in other industries. Amazon, delivery
services such as the post office, but also other delivery services are experiencing record sales.
Companies that were “born” as platform companies seem to have recognised the signs of the
times and can hardly fill the flood of orders. However, the pandemic is merely a magnifying glass
or a catalyst for what experts (Dueck, 2017) have been predicting for a long time: The disruption of
existing business models through digitalisation. Those who still believe that this “new normal” is only
a temporary phenomenon should know better by know.
page no. 4
CHANGE IN THE PHARMACEUTICAL AND
HEALTHCARE INDUSTRY
Change in the pharmaceutical industry is characterised by two major trends: The HCP (Health Care
Professional) is becoming a consultant and service provider, and the patient is becoming a consumer
and customer. Informed and self-determined patients will no longer see the doctor as an all-knowing
authority who is to be believed merely due to his professional ethics. The self-determined patient will
obtain his information via the internet, i.e., networking groups, medical portals and chat rooms. The
HCP – e.g., a doctor – will be confronted, compared and judged with and by this knowledge. These
“sages in white” will no longer
exist in their current form
in the future, and the health
market is transforming into
a consumer market with the
laws of a market economy,
demand, and supply.
Customers, who used to be
patients, now need to be
convinced and competed
for. The HCP’s competitors
are no longer lone clinics
in the neighbourhood or in
the nearest town, but can
be accessed worldwide and
are able to build up trusting
relationships across physical
distances through information, advice and references, something which was previously considered
inconceivable (telemedicine, on-demand services). The relaxation of the ban on remote treatment in
Germany is already a strong indication of the shift towards video consultations. For one, in Germany,
the Telematics Infrastructure (TI) and the Digital Healthcare Act (DVG), supported by the federal
government and gematik GmbH, are gaining momentum. Specific digital health applications (DiGA),
such as the electronic patient record (ePA), must be made available from 2021 onwards. The reason
for this is that providers of healthcare services and medical products are currently also developing
and making available interfaces for data exchange, for example based on the near-standard FHIR
(Fast Healthcare Interoperability) (in the B2B context). On the B2C market of “health platforms”, it
is Apple and Google in particular which stand out. Recently, the Federal Cartel Office approved the
establishment of a digital health platform by Poenix and Noventi (see press release from the Federal
Cartel Office dated 21/12/2020).
In addition, users of mobile devices are willing to make their data available for scientific purposes. In
the future, medical care, which is currently fragmented, will above all be digitised and personalised,
which is diametrically opposed to the current approach to medical care (Seebach, Nils, et al., 2021,
p.37).
Even pharmaceutical companies will be confronted with a new competitive structure, as powerful
players who today still stand for a completely different business model will enter the market (Amazon
intends to enter the healthcare sector: https://lnkd.in/dHuZpdH). The balance of power between the
players threatens to tilt in favour of the patient or consumer.
page no. 5
For the pharmaceutical industry, it is important to remain vigilant and observe the changes on the
market. The internet – as a synonym for digitalisation – will serve as a marketplace in the future.
Sales activities, product promotion and the collection of information will become virtualised and
lead to radical change. The novel, digitalised marketing of medicines, also in combination with
mandatory prescriptions, will revolutionise the entire market. In this context, the following aspects
are particularly salient for the pharmaceutical industry (adapted from Fischer, Dagmar et al., 2013,
p.274 – p.275):
1. Expansion of the internet 4. Assisting HCPs with the 7. Price as a market
as a channel for sales and collection of information instrument (generics /
purchasing substitutes)
2. Direct addressing of target 5. Building service offerings 8. Establishment as a
groups focused on the pharmaceutical premium brand / brand
product messages
3. Profitable pricing 6. Focusing on the brand, not
structure individual products
MOTIVATION
The pharmaceuticals market is (still) a special market with its unique challenges, particularly
where communication is concerned. In particular, the following play a role here (adapted
from Fischer, Dagmar et al., 2013, p.276 – 277):
Complexity and information Decision-making Decision-making
overload risks situation
From the perspective of information processing, the first point falls into the well-known category
of “big data”. Enormous quantities of data are generated on pharmaceutical products, their
production, active ingredients and side effects. Ongoing research findings yield new insights, which
in turn need to be communicated. Patients, but also HCPs, are on the brink of being overwhelmed
and must be provided with exactly the right information at the right time from the perspective of
the pharmaceutical company (Saghaei, Abbas, 2017, p.7). Unlike on the consumer goods market,
pharmaceutical products serve to alleviate suffering and diseases, or even to eliminate them. On a
classic consumer market, the main goals of consumption are of a joyful nature, such as enjoyment
and increasing zest for life.
Bringing a pharmaceutical product onto the market and establishing it generally entails enormous
entrepreneurial risk. The high expenditures for research and patents that expire after a certain
period of time can lead to high losses if the pharmaceutical preparation is unable to establish itself
on the market. Uncertainty concerning side effects and long-term consequences cannot always
be completely eliminated at the time of market approval. The risk is often greater than for many
products from the consumer goods industry. The decision-making risk for the market entry of a
product in the pharmaceutical sector must therefore be given special consideration in order to
minimise failure at market entry.
page no. 6
Despite a highly regulated pharmaceutical market (Medicines Act (AMG), licensing requirements,
etc.), people are free to choose their physician, chemist/pharmacy, health insurer, and may also opt
for generics and substitutes (Seebach, Nils et al., 2021, p.35).
Unlike the consumer goods market, pharmaceutical products are bought when this becomes
necessary due to an illness or other health-related circumstances. It is less of an experiential world,
such as in the consumer goods industry, which encourages emotional purchasing decisions when
shopping. Medicines and other pharmaceutical products are used out of necessity or even due to
an emergency situation, and rarely when the target group happens to be looking such a product or
because it is currently being advertised. Furthermore, the healthcare market has so far developed
into a seller’s market, whereas the consumer goods market is more of a buyer’s market (Seebach,
Nils, et al., 2021, p.45).
Lifestyle products, wellness preparations, dietary supplements and drugstore items are more
characteristic of a classic consumer goods market; in particular, products in the OTC (over the
counter) segment are suitable for being advertised in a manner similar to products in the consumer
goods industry.
Which conclusions can be drawn from this? Marketing in the pharmaceutical industry must take
into account the special requirements of the market and implement corresponding measures
alongside modernisation that are in line with the changes. Approaching customers, determining
the target group, and the provision of information must be strategically astute, future-oriented, and
technology-based in times of big data, data analysis (BI) and personalisation.
The following diagram serves to schematically illustrate various measures in reach measurement
and the influence (impact) on the HCP. Online marketing and personalised communication not only
lead to a higher degree of maturity in digitalisation, but in particular also increase reach and influence
so that the success of marketing measures is significantly improved, the customer’s (patient, HCP)
wishes and needs are truly understood from the perspective of the pharmaceutical company, and
the market entry of new pharmaceutical products are based on a solid (digital) data foundation.
Classical marketing measures alone are not helpful here, simply due to the massive amounts of data,
evaluation options, speed and quality requirements, as well as the security aspects.
page no. 7
What are the next steps in your company?
Fig.: Maturity model of digital marketing (Source: AcrossNavigator 2015).
In the following, particular emphasis will be placed on the aspect of communication. Modern,
technology-based communication in pharmaceutical marketing is supportive in nature and fulfils
the need to filter out important and correct data from huge quantities of data and making it
available to the patient, the doctor or other HCPs in a usable format. In addition, modern solutions
such as conversational AI ensure a constant exchange of information with the stakeholders in the
pharmaceutical market, who wish to remain informed regardless of the time and place. The provision
of high-quality information and the establishment of trust in the pharmaceutical markets constitute
central aspects in this regard.
page no. 8
CHATBOTS IN B2C AND B2B
Everyone knows them: “Alexa”, “Cortana”, “Google Assistant” and many others. Digital voice assistants
that are a simple and quick way to access information, but also to execute commands. These voice-
controlled virtual identities have already found their way into our everyday lives. Natural language
input and processing is an essential building block and driver of research on artificial intelligence
(Buchkremer 2020, p.29). NLP and NLG are closely related terms here, and stand for Natural Language
Processing and Natural Language Generation. In the background, text analysis plays a decisive role for
these bots; whereby the processing of questions is referred to as “Natural Language Processing” and
the generation of speech and texts is called “Natural Language Generation”. Today’s bots are syntax-
oriented rather than semantics-oriented. In the future, voice assistants will know how to interpret
other components in addition to semantics, such as humour and irony (Buchkremer 2020, p.30). In
the B2B segment, professional solutions such as conversational AI need to establish themselves
to ensure customer access and loyalty. The triad for future success is therefore: customer access,
creating added value for the customer, and establishing customer loyalty (Seebach, Nils et al., 2020,
p.48).
Expert interview with Sebastian Glock, Senior Technology Evangelist
at Cognigy, on the possibilities of automation in customer service.
Brain Station: Why are chat- and voicebots being used?
Sebastian Glock: With well thought-out applications and smart implementation, companies
improve customer service by being available to customers at any time and in
numerous languages, while also helping to solve problems. Furthermore, this
reduces costs. According to a study by IBM, companies around the world spend
more than 1.3 trillion US dollars handling 256 billion calls from their customers
every year. In other words, each customer service call costs an average of 30
US dollars. Even healthcare / pharmaceutical / chemical companies are looking
for ways to provide good service at a lower cost, around the clock. Availability,
even outside business hours, is extremely important – particularly for sensitive
topics such as healthcare. When in doubt, customers want to receive sound
information concerning their individual issues, even at night. Ideally, they would
be able to talk to someone who is well-versed in the subject and possesses all
the relevant background knowledge. This is what conversational AI can make
possible today.
However, such customer service bots should not be lumped together with
assistants like Alexa, Siri or Google Home. Naturally, they make using one’s
voice as a user interface acceptable to a large section of the population. But the
page no. 9
applications differ greatly. One is entertainment and a solution of convenience
for the home. The other is a serious dialogue for solving problems with
customers.
Brain Station: What would such a solution look like?
Sebastian Glock: As customers, we expect problems to be solved 1. quickly, 2. competently, 3. at
any time of the day, and 4. across all channels where possible. These are four
hurdles that customer service needs to overcome. Many simple chatbots are
based on rudimentary software that can only provide ready-made answers to
questions worded in an exact fashion. They work using pre-written FAQs or
rules with keywords. They offer no real added value compared to information
from the website or in an app.
A smart solution is obtained through the combination of AI-based speech
comprehension and the bots being connected to back-end systems. Such
a smart, networked bot can then access an enormous wealth of knowledge
about products, services or even personal information on the customer from
the customer database in real time, and is not simply limited to a catalogue
of questions and answers. This is read access; it gives the bot a great deal of
knowledge.
However, the smart bot or virtual assistant also has written access to the back-
end, i.e., it can, for example, change an address or an order quantity, create an
appointment, cancel a booking, schedule a call back, etc. Our platform Cognigy.
AI has already been used for this purpose in many instances. One could also
say that, at first, bots were able to provide general information, and somewhat
later on, more customised information. Today’s bots can autonomously initiate
complex business processes. Just like a real employee in a call or contact
centre.
Fig.: Easy chat flow design.
page no. 10One could also say that, at first, bots were able to provide general information,
and somewhat later on, more customised information. Today’s bots can
autonomously initiate complex business processes. Just like a real employee in
a call or contact centre.
Brain Station: To what extent can bots automate customer service?
Sebastian Glock: There will always be people managing communications and processes
in customer service. Particularly in the highly regulated and sensitive
pharmaceutical sector, humans are needed to provide customised assistance
in the event problems arise. But at the end of the day, a lot of things involve
standard business processes. Bots are good helpers and are becoming
increasingly capable. In our projects, they can now independently respond to
30 to 60 per cent of all enquiries. This greatly increases the effectiveness of
customer service and improves the customer experience for the user.
For one, however, the platform has to be set up and the response and business
logic behind the bots needs to be configured. And this is exactly where partners
such as Brain Station 51 come in. They will always be required for cases or
problems where decisions have to be made, for example. But a bot can receive
enquiries outside business hours, process them, and forward them to the right
agent in the contact centre, who then takes care of them the next morning.
Customers are left with a good feeling and the agent is able to work more
effectively.
Serving important B2B customers should also continue to be possible face to
face with a dedicated contact person. However, if the business customer simply
wants to change an order, enquire about a delivery date, or receive specific
product information, the virtual agent can also be of assistance – immediately
and around the clock.
Often, a bot is connected upstream in order to directly forward enquiries to the
right agent. Or it might operate entirely in the background. For example, it could
be listening to a conversation and providing the human in customer service with
information on the customer history or detailed specifications on the product
which is currently the subject of the conversation. It makes the human smarter
and improves the quality of his responses. The customer receives qualified
responses more quickly. Called agent assist solutions, they are in high demand
at our company.
page no. 11Brain Station: What is the technology behind a smart bot?
Sebastian Glock: A smart bot can remember things and carry out dialogues like a human being.
The technology behind it is called conversational AI. At its core is Natural
Language Understanding, i.e., AI-based “understanding” of human language.
Understanding here means that the input does not have to be exact in nature;
instead, a wide range of input variants can be recognised. For this purpose, a
bot is trained in advance. Once such groundwork has been carried out, the AI can
quickly be scaled to other languages and markets. This is immensely important
for our healthcare clients, most of whom are internationally active.
Fig.: Intelligent-Service-Ecosystem.
But the AI alone is not enough for this. You also need to be able to manage and
adapt the bot responses easily and clearly in any language, design structured
dialogues, connect back-end systems, and implement bots in channels. In the
past, this was a complicated affair and could only be realised with a great deal
of IT know-how and programming.
Fig.: Plug and play backend connection.
page no. 12However, today’s technology is so far along that even persons without a technical background can
independently create and refine the control mechanisms behind the dialogues and the logic behind
the virtual assistants.
REQUIREMENTS FOR CONVERSATIONAL
AI IN THE HEALTH SECTOR
Conversational AI or virtual agents assist human users with their concerns via a dialogue interface.
This goes beyond mere conversation by executing tasks issued in the form of voice or text commands
for the benefit of the user, and subsequently presenting the results (Stucki, Toni et al., 2020, p.4).
The implementation of a conversation strategy with chatbots requires the creation of a classic IT
project, or integration into such an IT project. Once the technical area of application for the chatbot
and specifications have been defined, the technology for implementation is selected. Special
quality assurance measures apply to artificial intelligence projects. Furthermore, all regulatory
requirements (GCP: Good clinical practices) must be fulfilled.
The requirements for the chatbot should be defined with as much precision as possible in order to
consider the aspects described above. The following diagram shows an example and excerpt of how
the requirements can be visualised in practice via a “solution design”.
page no. 13Fig.: Excerpt of conversational concept with HCP using conversational AI: solution design
(custom project).
page no. 14Another real-world example from the United International University in Dhaka, Bangladesh, shows
a healthcare chatbot system called “Disha” (signpost). It is a system that assists the user with
recognising diseases and maintaining a healthy lifestyle.
Fig.: System diagram for “Disha” from Dhaka (Rahman, Md. Moshiur et al., 2019).
page no. 15A chatbot that processes a massive amount of data which is used to detect commonly known
diseases. In order to capture various possibilities in textual input, a Named Entity Recognition (NER)
algorithm is used. The user can enter individual symptoms, although at least three symptoms must
be entered for diseases to be recognised. The inputting of values such as blood pressure, editing of
master data, and reminder features such as reminding the user daily to take their medication have
also been implemented. The system is based on a generic machine learning algorithm. For making
further improvements, a deep learning (DL) algorithm will subsequently be employed (Rahman, Md.
Moshiur et al., 2019).
On the technical and conceptual side, it is generally important to define the corresponding
environment and requirements. In this context, particular attention should be paid to
• Tower definition and architecture (runtime environment, build and deployment process,
code repository, volumetric)
• Solution design (data model, intentions, GDPR)
• APIs, access to existing resources, interfaces with core systems
• Entry points (entry points for communication)
• Communication for various chatbots (chatbot architecture)
• Data basis, algorithm for learning (knowledge base and learning algorithms)
• Quality assurance & testing approach
• Operational concept
The list above is not exhaustive and should be considered as an excerpt.
“Intentions” here refers to the intention of the user – in this case the HCP – to access the chatbot,
submit his queries, and request the corresponding information. This might include:
• Subscription / newsletter (regular provision of information).
• Sample request (samples of medicines)
• Search for dosing information / medication management (information on dosage)
• Self-anamnesis
• Reminding and notifying (reminder feature with notification)
• HCP / patient education (continuing education for doctors and patients)
• Self-service
The intentions must be defined in a process chart and agreed upon with the party responsible for
the project. In this case, it is important to map both the positive and negative cases so that the
chat bot can competently map every situation. Messages such as “an error has occurred” must not
appear to prevent the loss of trust in the virtual identity. Bear in mind that the chatbot operates
in a manner more closely resembling an abstract algorithm. It is practically a representative and
helper from within the pharmaceutical company – a digital professional (much like a digital HCP).
Errors and shortcomings (customer experience) are liable to be personalised and transferred to the
pharmaceutical company (Stucki, Toni, et. al, p.27-28). This form of social intent can be intercepted
by separate bots, which then work together with the specialist bots (chatbot architecture).
Branding (brand experience) via this digital channel is not only of key importance, but must be
managed with great care and precision. The following diagram serves to show the scope and current
status of chatbots in the healthcare sector in terms of their field of application (Mladan, Jovanovic
et al., 2019, p.7):
page no. 16Fig.: Current status of chatbots in the health sector with regard to the analytical framework (L – Low, M –
Medium, H – High), (Mladan, Jovanovic et al., 2019).
A SWOT analysis helps with the fundamental design of the chatbot for the chosen use case. Applying
the SWOT analysis to chatbots in the health sector means that strengths and weaknesses directly
pertain to the chatbot itself or are considered as internal features (characteristics). Opportunities,
possibilities, and threats are external effects that complicate or even prevent the realisation of
chatbots. Sample questions in the fields of the SWOT analysis for supporting a decision are listed
here.
page no. 17Strengths Weaknesses
• What is unique about our chatbot? • What needs to be prevented during
• What capabilities do the chatbots real-world implementation?
implemented possess? • Is the knowledge base of the
• What are the advantages / benefits system sufficient?
of the system? • What improvements to the chatbot
• What are the greatest benefits of system are necessary?
the chatbot and what will they be in • What are the disadvantages of the
the future? chatbot?
Opportunities Threats
• Which external changes bring • What negative aspects currently
opportunities? exist in the health sector?
• What are the current trends in the • Is political instability having an
health sector? impact?
• What is lacking in the health • Is a change in consumer behaviour
sector? Can the chatbot fill this expected?
gap? • What are the hurdles expected in
• Changes the implementation of the system?
• Are there standards, regulations,
laws, or new ordinances which
negatively influence the use of
chatbots?
Table: Questions for the SWOT analysis (translated from Denecke, Kerstin et al., p.78).
Overall, it can be said that chatbots in the healthcare sector are highly adept at controlling, guiding,
and supporting the patient pathway. Chatbots will become truly successful when a connection to
health systems (e.g., ePA, electronic patient record) and other stakeholders such as health insurance
companies can be established. This will enable a holistic view of the health data and make it possible
to derive targeted diagnoses, therapies and treatments from it. In this context, trust in the technology
and how it is handled, along with the security of the patient data, are of utmost importance for a
chatbot to be a success.
Communication with a chatbot, on the other hand, can be very exhausting if the system has problems
comprehending the user or requires too much interaction. To avoid this, it is advisable to establish
a comprehensive and trustworthy information base which the chatbot uses to perform its work.
For this purpose, the chatbot system can be supplemented with a self-learning system (machine
learning). In order to make a chatbot appear intelligent, care must be taken to ensure corresponding
variation in its vocabulary for one and the same intent. However, this makes the chatbot relatively
complex and may lead to problems. On the other hand, the main focus can be on the stability of a
chatbot. In this case, the user has little control over the conversation. Instead, the chatbot more or
less takes over the flow of the conversation.
page no. 18If we look at the healthcare market as a whole, there is a lack of robust and easy-to-use chatbots and
applications that are able to capture the HCP’s or patient’s interest over the long term and inspire
them with new experiences and real added value. The political regulation of the healthcare market
as such is a challenging topic. Many regulations and rules need to be taken into account, such as
data protection, requirements for medical devices at the national and EU levels, ordinances, and
stipulations by health ministries. Integration with other healthcare systems will be indispensable in
the long term.
Used correctly, chatbots can contribute significantly to the digitalisation of the healthcare market.
Here, it is advisable to initially focus on the simplicity and robustness of the chatbot in order to build
implicit trust on the part of the user. But in order to take concrete steps, it is necessary to identify
the right use case. As a digital assistant, e.g. for obtaining information, monitoring the state of
health, for making appointments and establishing doctor-patient communication, or also for issuing
warnings when health parameters are exceeded, the chatbot is able to generate numerous useful
services on a long-term basis (Denecke, Kerstin et al., p. 83).
page no. 19REFERENCES FROM THE
PHARMACEUTICAL INDUSTRY
Bangladesh College of
Physicians & Surgeons
page no. 20CONCLUSION AND OUTLOOK
Changed consumer behaviour and the evolution of the HCP into a service provider in healthcare
require modern, targeted – and above all individualised – marketing approaches. The target group is
no longer really a group, but instead an individual with specific needs for product information and a
wide spectrum of other information, such as research findings, studies and events. Pharmaceutical
companies which have a strategic understanding of how to utilise technological advancements
to serve their customers and consumers with news and information at the right place and at the
right time clearly have strategic competitive advantages. Conversational AI can be the right key
technology in this case for utilising virtual assistants to implement the targeted gathering of
information in near real time and be available to the HCP in a personalised fashion around the clock.
In the future, the characteristics of consumer goods markets will also dominate the market for
pharmaceutical products. Pharmaceutical companies must not be blind to these developments, and
will need to implement corresponding measures for value creation as early as in sales and marketing
measures. The patient, now a consumer on the pharmaceutical market, who has educated himself
through obtaining information, behaves economically and with self-responsibility. In particular,
this can be observed in wellness and lifestyle products, but also for dietary supplements and other
drugstore products, and for chronic and preventive treatments. However, this does not mean that
pharmaceutical companies should concentrate entirely on the marketing side of pharmaceutical
products, but instead on the brand in order to establish a sort of “omnipresence” so as to permanently
be on the minds of patients (consumers). Such brand establishment requires a highly strategic
approach and technological implementation expertise in systems used in the pharmaceutical
industry (IQVIA, Sitecore, AEM, Google Analytics, VeevaCRM, and many others). Special statutory
requirements for the pharmaceutical industry, for example from the Federal Ministry of Health
(BMG) or the Medical Association (Ärztekammer), must also be taken into account. At the European
level, for example, it is the EMA (European Medicines Agency).
Modern means of communication such as conversational AI can maintain constant contact with
consumers worldwide in the form of chat- and voicebots. In this context, classic marketing strategies
alone, such as booths at trade fairs and pharmaceutical representatives, are outdated. Instead, the
future of pharmaceutical marketing requires a combination of measures from the classical world
and the latest measures from this new world. If this combination works, pharmaceutical companies
will be able to establish themselves as brands, equipping them for the challenges of the future.
page no. 21Brain Station combines proven know-how specific to the pharmaceutical industry from numerous
successfully completed IT projects with the necessary methodological, procedural, and regulatory
competencies. This includes compliance with GCP, GLP, GMP and other national regulatory
requirements of various countries.
Quality, Time, Budget Structured, Agile, Efficient
Local & company
Cultural empathy
understanding
CUSTOMER PROJECT MANAGER DEVELOPERS
BRAIN STATION BRAIN STATION
FIxed Price + DAX/MDAX-Experienced PMs + Internal Project Success
Fig.: Brain Station delivery model.
Brain Station’s internationally established delivery model yields high-quality project results for
clients in the pharmaceutical industry and guarantees the realisation of chatbots
We look forward to hearing from you.
BRAIN STATION 51
Office: +49 4403 6999839
Cell: +49 162 2773146
Email: info@brainstation-51.com
Address: Rebhuhnweg 4, 26160 Bad Zwischenahn, Germany
Keywords
HCP, NLP/NLG, AI, conversational AI, pharmacy, artificial intelligence, virtual identities, change,
strategy, online marketing, digital transformation, consumer goods market, pharmaceutical market,
customer access, deep learning, knowledge base, healthcare systems.
page no. 22LITERATURE
Buchkremer, Rüdiger, Heupel, Thomas, Koch, Oliver (Artificial Intelligence, 2020); Künstliche Intel-
ligenz in Wirtschaft & Gesellschaft: Auswirkungen, Herausforderungen & Handlungsempfehlungen
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