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NLP Solution For Pharmaceutical Marketing

Azati developed a unique AI and Computer Vision-based solution for a pharmaceutical company to build assessment reports based on questionnaires and insights from medical practitioners to conduct advanced marketing strategies.

CUSTOMER

Health is the highest value, but unfortunately, only a few think so. Drug manufacturers, pharmacies, doctors have to make a lot of effort to promote the idea of ​​health to create a desire to be healthy. You can effectively promote health only if you have a good understanding of people and their motivation. These are the problems that pharmaceutical marketing is working on.

Pharmaceutical products are pretty sophisticated for understanding by non-experts in Healthcare, so conventional marketing is hardly applicable to them. Therefore direct pharmaceutical marketing is more appropriate for such aims. When simple marketing is used when small earnings are needed, pharmaceutical companies are looking for ways to make much more profit and prefer advanced marketing.

Our customer is an entrepreneur with extensive experience in the marketing business who turned to us with a unique idea to help pharmaceutical companies increase the level of trust of ordinary buyers who know little about the drug’s composition.

Objective

Pharmaceutical marketing, in this case, is not just the sharing of information in different sources but also the quality and compliance with the declared one. And in healthcare, it is sometimes difficult for an ordinary person to understand which pharmaceutical composition will be better or more appropriate. Hence, people often buy “popular” medications that they hear about.

The customer turned to us with a unique idea, which had not yet been on the market. The essential purpose was to facilitate the search and comparison of the required product with the recommendations of doctors based on questionnaires and insights from professionals.

Our task was to develop an MVP using Artificial Intelligence and Machine Learning technologies to build assessment reports for pharmaceutical companies. Azati’s team studied models and tools for solving ML tasks, including speech-to-text, text mining, finding similar phrases and mismatches using NLP.

We also learned data visualization tools to present the resulting reports in an understandable form.

Challenges

01. Challenge

One of the main issues became the language barrier between the leading AI engineer and the English-speaking customer. However, this problem was solved quickly by involving a part-time specialist who spoke English fluently.

While the first was solving applied problems, the second one participated in regular communication with the client and explained the technical side of the problem and the approaches we offer to reduce possible risks of a particular solution.

02. Challenge

Since the task was new and sophisticated for our team, we faced the inefficiency of the available tools at some developmental stages.

At the customer’s initiative, we invite a third-party consultant – Doctor of Science and an expert in his specialty. He recommended several ways that we worked on and thus came out of the creative impasse.

03. Challenge

The generated reports had to be structured and easily readable by any user. We developed a custom system to build a clear report to evaluate the found phrases, classify them, and sort them according to the calculated ratings.

Process

Before getting this project, our AI team went through a test task among a dozen other IT firms, which consisted of two stages. The client described the problem and asked us to draw up our solution, with the terms and description of chosen approaches and technologies. We successfully overcame both stages and started the project.

The management process was built according to Agile methodology. We did not have a clear description and idea of ​​how the developed algorithm for evaluating common phrases should work. We selected solutions and approaches together with our customer. Azati team offered technical means for text analysis, existing models, and their accuracy. And the client, in turn, decided on how closely the potential result meets the project’s goals and is suitable for marketing analysis.

Development process step by step:

Stage 1.

It was necessary to decide how to bring all the input data to a standard format with which the AI ​​algorithm can work. We had to solve speech-to-text recognition issues, model training for specific terms, solve the nuances of punctuation and capitalization of subtasks within the framework of NLP.

Stage 2.

We solved problems directly related to NLP by analyzing input text data, finding common phrases, segmenting phrases by topics, assessing sentiment analysis, and calculating scores for found phrases.

Stage 3.

On this stage we solved the problems of grouping the obtained data into thematic reports that highlight one or another aspect of analyzing the respondents’ answers.

Stage 4.

At the final stage, we considered the means for visualizing the received reports and finding a simple and intuitive tool for sharing and demonstrating the results and main conclusions to potential customers.

Solution

As a result, we have developed an ML model that can analyze data from medical questionnaires, find insights, and build reports for the end-user within the existing pharmaceutical domain.

We have built an ML model that can automate the following tasks for marketing analysis:

  • Analyze questionnaires from a group of doctors, and identify similar answers;
  • Compare and correlate the aspects voiced by doctors with the offers in the marketing strategy of pharmaceutical companies;
  • Generate reports from which users can see what doctors-practitioners are talking about and what they value regarding the specified pharmaceutical product (effectiveness, safety, usage, etc.) and how the marketing strategy for this product can be improved.

Technologies

 
 
 
 

SCREENSHOTS

Results

During the development process, Azati team used Google services and open source products.

Azati have achieved the following results:

#1
 

Integrated Google ASR service effectively solves the speech-to-text issue. It handles general vocabulary, but our pharmaceutical domain needed specific terms to be recognized. The problem was solved by boosting keywords (there is such a tool in Google).

#2
 

We examined various NLP tools to find common phrases in users’ reviews and found the optimal one. Therefore, it became clear how to preprocess data and what datasets to generate for training the model.

#3
 

Our team developed a system for “weight” measuring of the found phrases and their relationship to each other. As a result, the concept of calculating was developed taking into account the indicators of sentiment analysis and topics classification.

#4
 

The last task was to find a simple and accessible interface for ordinary, not very tech-savvy users. After researching several solutions (paid and open-source), we settled on Metabase as a means of reporting data.

Now

We successfully developed and presented a solid MVP which fully meets all requirements. Today, the customer is actively looking for potential leads to build them similar reports and evaluate the effectiveness of their marketing strategy in the pharmaceutical market.