In the fast-paced and highly competitive world of the pharmaceutical and life sciences industry, companies must harness a vast sea of data to make informed decisions. However, despite an abundance of data—from prescriber and patient information to market and sales data—many teams often struggle to derive actionable insights.
Enter AI-powered analytics, which equips pharma and life sciences companies with a user-friendly means to explore and analyze their data, revolutionizing every facet of their operations.
For sales, brand, and marketing teams to manufacturing, quality, and drug development departments, AI-powered analytics paves the way for these organizations to truly embrace a data-driven approach by using natural language queries and getting answers with automated insights.
Whether it’s for optimizing brand strategies, gaining new commercial insights, navigating complex market access scenarios, delving into payer analytics, ensuring manufacturing quality, or a plethora of other critical areas, implementing AI-driven analytics is a catalyst for transformative change for pharma and life sciences companies.
In This Post
Top use cases for AI analytics for life sciences
Field sales analytics: Track sales metrics changes at the sub-national and territory level, and dramatically improve healthcare professional (HCP) targeting.
Brand insights: Uncover actionable brand insights to improve marketing strategy and drive sales.
Self-service life science analytics: Eliminate organizational bottlenecks and increase analytics agility.
Market access: Inform strategic decision-making and optimize market access.
Payer analytics: Track rebate usage, perform formulary/plan-level performance management, and forecast rebate/cashflow, driving revenue.
Supply chain quality analytics: Enhance pharma manufacturing quality management and efficiency while mitigating risks.
Let’s explore each of these use cases in a little more detail.👇
Field sales analytics
Question: How many calls have we made to T1 targets in the New York City Metropolitan Region this quarter?
Using today’s tools to try to unite disparate data sources, home office and field sales teams find themselves stuck with a “needle in a haystack” challenge when it comes to answering these types of granular questions to identify new sales opportunities, target the right HCPs, and ultimately, put lifesaving drugs into the hands of the right patients.
Instead, AI-powered analytics empowers home office and field sales teams to track field activities, market dynamics, sales drivers and opportunities, and more. They can then make better decisions around sales force sizing, territory alignment, and incentive compensation to cost-effectively gain market share.
By merging sales, marketing, CRM, patient, financial, inventory, and third-party data sources, commercial teams can gain a deeper understanding of HCPs to make more informed, data-driven decisions. They can then better execute on commercial and brand strategies, course-correct faster to minimize risk or capitalize on market opportunities, and use automated machine learning-driven analysis to target HCPs more efficiently.
Answer: Our team has only called 3,021 out of 4,552 targets. Time to get the ball rolling on a more targeted approach to identify doctors with a higher propensity to write our brand, based on high formulary access and low TRx.
Question: What’s up with all the recent growth with our main competitor’s brand? 👀
If that’s a question your brand insights team finds itself struggling to answer easily (and in a timely manner), AI-powered analytics can help.
On the frontlines of understanding key performance data to support strategic decision-making, brand insights teams are often stuck spending lots of time triangulating trends using spreadsheets and numerous dashboards.
With data analytics powered by AI, here’s how teams can get out of static tools and derive actionable brand insights:
- Proactively spot market share insights: Maximize salesforce productivity through machine learning-based HCP targeting.
- Identify brand performance drivers: Use natural language search to uncover factors driving changes in performance across geographies, portfolios, and markets.
- Monitor brand health automatically: Track key customer segments and specialty groups for brand uptake and switching behaviors, leading to more successful launches and faster decision-making for sales and marketing.
Answer: Ah, now we see what’s driving all that growth. Time to assemble a new marketing campaign targeting the region our competitor set its sights on…
Self-service life science analytics
Question: How are prescriptions trending by state for new patients for a certain drug?
Life sciences and pharmaceutical companies need to effectively leverage all of their data to improve their decision-making—a not-so-simple task with this typical process:
✔ They take this question to the data team.
✔ The data team goes to build a dashboard or report.
✔ But depending on their backlog, a week (or two) goes by.
✖ Now the data is outdated, and there more questions than answers. 🙁
Instead, using AI-powered self-service analytics, teams can ask their own questions in natural language and then automatically get charts and visualizations based on their questions, highlighting the right insights to explore. Instead of asking the data team for a new report or dashboard, it’s already at their fingertips instead.
Answer: I can see for myself that these prescriptions are hitting it out of the park in the Tri-State Region. Onward and upward!
Question: How is this recent formulary change going to affect our newly released product?
Hurdles like fragmented, complex data (from EHRs, claims, and sales), dynamic stakeholders (patients, healthcare providers, payers, regulators, and policymakers), pricing and reimbursement complexity, and gathering real-world evidence make it challenging for market access teams to identify changes, opportunities, and challenges within the market.
Once they have their data, market access teams must then conduct multidimensional analysis of clinical efficacy, cost-effectiveness, patient outcomes, and healthcare system impact. Often faced with budget constraints, teams require sophisticated analytical models and frameworks to balance and align the needs and requirements of stakeholders.
With advanced analytics using the power of AI, market access teams gain real-time insights to inform strategic decision-making. Here’s how:
- Get automated insights from internal and syndicated data sources so you can quickly highlight the impact of access changes across payers, informing contract discussions and negotiations.
- Conduct automatic formulary monitoring to proactively receive alerts, freeing up time and reducing risk.
- Combine performance and formulary data, identifying favorable access segments with growth opportunities.
Answer: Not great. We’ll need to develop some new analytical models to really demonstrate this product’s value and cost-effectiveness. 🤑
Question: Which rebate dollars should we spend time disputing?
A combination of complex reimbursement models, a lack of data standardization, and the time-consuming task of analyzing and extracting meaningful insights out of available data (e.g., drug usage, claims, medical records, prescriptions, and patient demographics), can make answering these types of questions challenging.
Using an AI-powered analytics solution to streamline these capabilities into one source, payer and rebate analytics teams gain the following advantages:
- Track rebate usage in real time, using ML-powered alerts that provide constant visibility into shifts in rebate usage and trend-based insights into areas with high variance.
- Automate the manual task of formulary and plan-level performance monitoring and proactively get alerts to free up your time and reduce risk.
- Use historical data and trend-based insights to predict when rebate requests will come in (and when TPAs/PBMs usually submit larger-than-usual rebate resubmissions).
Answer: Now that we can see market share performance across all of our payers, let’s tackle these disputes.
Supply chain quality analytics
Question: “What’s driving my supply chain quality issues for a certain product—across several tiers of suppliers—and how can we fix this to improve product quality and reduce defects?”
With stringent regulatory and compliance requirements, product complexity, traceability issues, data integration and interoperability considerations, it can be challenging for pharmaceutical firms to maintain supply chain quality, especially when there are time sensitivities.
However, with AI-powered diagnostic analytics, firms can conduct root cause analyses of issues impacting high cycle times in order to more effectively change controls, improve capacity planning, and mitigate future manufacturing risk. In addition, with ML- and AI-based automated trend spotting and clustering, they can more easily minimize variation in ordering patterns and supply usage.
Teams can also use point-and-click ML modeling to predict critical workflow delays that are causing issues with quality processes, as well as make smarter, data-driven decisions for manufacturing management by gaining more holistic views across all manufacturing data sources.
When they can easily visualize and interact with vast amounts of data across multiple systems, supply chain analytics teams can solve all of their granular questions with the click of a button.
Answer: We see a major change in lead time at our warehouse in Raleigh from this morning. Let’s investigate the issue.
Want to learn more about AI-driven decision intelligence?
Top pharmaceutical companies like Novartis and AbbVie are using Tellius to improve patient outcomes, get answers more quickly, find deep insights more easily, and make better decisions using all of their data—the cornerstone of AI-driven decision intelligence.
Learn more about using decision intelligence to take your analytics to the next level in the life sciences sector—get your copy of The Tellius Guide to AI Analytics for Life Sciences.👇