Deep Dive into Unusual Sales Data Trends with Anomaly Detection
Some of the benefits of using AI for anomaly detection:
Pharma commercial sales data processing is complex due to the dynamic nature of the data from multiple external and internal sources. Routine data processes face challenges from changes in product definitions (e.g. NDC codes), affiliation hierarchies, geographic regions, address changes from HCPs, alignments updates, product reinstatements, Sales rep changes and format changes from data vendors.
With each sales data refresh, commercial teams face the challenge of understanding the underlying reasons for the shifts in the sales. Uncovering these issues requires Business and IT teams to spend hours or days diving into the data, comparing it with previous data and then developing explanations for the changes.
An automated approach to identifying changes and their contributing factors can save significant effort and enable proactive communication, thereby enhancing operational efficiency.
AI based Anomaly detection is a technique for finding unusual patterns in data that deviate from the expected behavior. AI, particularly machine learning and deep learning algorithms, can be very useful in building anomaly detection systems by learning from historical data and recognize what’s normal and abnormal.
- Automates identification: AI can analyze vast amounts of data quickly and efficiently, automating the process of finding anomalies that might be missed by humans.
- Adapts to changing patterns:
AI models can learn and adapt over time as new data becomes available. This allows them to identify anomalies even when the underlying patterns in the data change.
- Detects subtle anomalies:
AI models can detect complex patterns and subtle anomalies that might be difficult for humans to identify.
For Pharma commercial data, anomaly detection can be highly valuable in understanding sales trends or break in the trends, further providing deeper insights into market dynamics.
- Sudden Drops / Surge in Sales:
Anomaly detection can pinpoint unexpected dips or surge in sales. This could be attributed to competition activity, Patient preferences, Physician prescription pattern, large accounts dropping sales, formulary changes at Payers or market saturation or seasonality, prompting further investigation.
- Geographic Sales Anomalies: It can help identify unusual sales patterns in specific regions. This could indicate regional disease outbreaks, product distribution issues, or counterfeiting activities.
- Deviations from Typical Ordering Patterns:
It can be very instrumental in learning about the usual ordering patterns of different accounts/healthcare providers. Significant deviations, like a sudden shift towards a specific drug class or a drastic increase in overall orders, could warrant investigation.
- Unusual Call Patterns:
AI can monitor the call data of sales reps and flag anomalies like a sudden decrease in calls to specific doctors or a shift in calls data. This could indicate a lack of focus on the account.
Building an automated anomaly processes within the regular data processing helps highlighting key contributing factors such as seasonal trends or demographic shifts, Payer changes in formulary, Physician prescription patterns, large account sales variances, competition switch patterns, sales rep call activity, further helping you make data-driven decisions to address the underlying issues.
How to setup anomaly detection in regular sales data processing
Setting up Anomaly detection in AWS would involve using built in machine learning capabilities to automatically detect and highlight anomalies in the data.
- Data Preparation – Anomaly detection relies on high-quality data. Ensure your commercial data processes deliver accurate, complete, and properly formatted structured data for optimal results..
- Create an Analysis – Choose the dataset you want to analyze by connecting to a data source i.e. commercial data Lake or data warehouse for the relevant dimensions and facts containing sales, rep, HCP/HCO, territories, Payer datasets.
- Build visual charts – Chose the visual types that support anomaly detection such as line charts, bar, heat maps or Insights.
- Enable Anomaly detection – Once the visual charts are setup, enable anomaly detection by clicking on💡ML-powered Insights -> set up anomaly detection.
- Customize the Anomaly parameters – In the new Visual click Customize Insights -> Computations, select Anomaly detection and configure the variables to find anomalies for and the top contributors.
- QuickSight will automatically start analyzing the data and identify any anomalies. The anomalies are typically marked with different colors or shapes on the chart.
- Review and Interpret Results – Review the anomalies highlighted in your visuals. Each anomaly will provide details such as the expected value range and the actual value that deviated. You can drill down into the data to further investigate the causes of these anomalies.
Some of the benefits of using Quicksight for Anomaly detection:
- Real-Time Insights:
QuickSight provides real-time anomaly detection, allowing you to promptly identify and address unusual patterns in your data, such as sudden spikes or drops in sales, prescription volumes, or marketing expenditures. You can set up alerts to notify relevant stakeholders immediately when anomalies are detected, facilitating quick decision-making and response. - Ease of Use:
QuickSight’s interface makes it accessible for users without deep technical expertise. The platform automatically analyzes your data to detect anomalies, reducing the need for manual monitoring and analysis. - Comprehensive Data Integration:
QuickSight can connect to various data sources, including Amazon S3, Redshift, RDS, and more. You can integrate and enrich your commercial data with additional datasets, such as market trends or customer demographics, for more insightful anomaly detection. - Scalability and Performance:
Built on AWS, QuickSight can handle large volumes of data and scale with your organization’s needs, ensuring consistent performance even as your data grows. - Enhanced Decision-Making:
QuickSight provides detailed explanations for detected anomalies, helping you understand the underlying reasons and take informed actions.
Overall, Commercial organizations can gain valuable insights, improve sales efficiency, and mitigate potential risks by identifying unusual patterns in data thereby improving decision-making across a wide range of applications.
Feel free to reach out to info@chryselys.com for more information.
Author
Sanjeev Sardana
Co-Founder & Principal
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