AI Marketing solution for Pharmaceutical Company
Problem statement.
The traditional approaches to customer engagement don’t work anymore, leaving the sales representatives teams struggling to establish meaningful connections and drive conversions with the customers.
Challenges.
Select the right communication channel.
Identify the ideal moment to reach out to the customers. Misalignment in timing leads to missed opportunities and reduced conversion rates.
Provide a content that addresses the specific pain points and needs of each individual customer.
From Inception to Product-Market Fit.
The journey - Bringing the idea to life
Stage 1: Inception and Ideation Market Research and Analysis:
Utilized tools like market surveys, competitor analysis to identify the market gap. Identify a problem, personas.
Cross-Functional Workshops:
Conducted brainstorming sessions involving teams from marketing, engineering, and data science to generate ideas.
Stage 2: Concept Development. Prototyping Tools:
Employed prototyping tool and wire-framing software to visualise the svisualiseelected concept.
User Journey Mapping and user stories:
Created journey maps for different personas to define clear product objectives and functionalities. Created user stories based on the user journey. Created opportunity solution tree, to track assumptions and experiments.
Stage 4: Testing and Iteration. Customer interview and initial feedback:
Collected initial sales rep feedback to adjust the AI model settings.
Data Analytics:
Utilized data analytics platforms to analyze user behavior and engagement metrics.
Stage 3: Prototyping and Development. AI Model Training:
Leveraging AWS's robust suite of AI and machine learning services, worked with team of engineers and data scientist to architect an intelligent system that crafts tailored marketing suggestions for sales representatives.
Agile Development:
Adopted an Agile development methodology (Scrum) ensuring iterative development and quick adjustments.
Stage 5: Product-Market Fit KPI Tracking:
Set up robust tracking systems using tools like Tableau and PowerBI.
Scaling Tools:
Used cloud-based services like AWS to facilitate the scaling of the product.
Use of interaction data:
Customer’s reactions on new suggestions are recorded and used for retraining a model, what allows to make suggestion more tailored after each iteration.
Challenges and solutions on the journey.
Resource Allocation: Ensured effective allocation of resources by prioritising MVP features and addressing budget constraints.
Data Privacy and Compliance: Ensured the product adhered to data privacy regulations by implementing strict data handling and encryption practices.
Technical Challenges : Faced technical hurdles in AI model development. Collaborated closely with data scientists to overcome algorithmic complexities.
Data quality: Data quality is a foundation for correct work of AI models. Brought the culture of understanding that data quality is shared responsibility by proactively checking and maintaining data quality.
AI Model Bias: Continuously monitored AI model outputs to identify and rectify biases, ensuring fair and ethical recommendations.
Final product.
As a team, we introduced a product that provides the ability to translate invaluable marketing insights blended with historical data about previous interaction into tangible, actionable features seamlessly woven into our AI solution.
This product entails a collaborative approach with Marketing teams to comprehensively grasp their distinct campaign objectives and prerequisites. The final product revolutionises how sales teams engage with customers, ensuring that the right message reaches the right person through the most suitable channel and at precisely the right time.