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5 Use Cases for Cost-Cutting Life Sciences Analytics

In times of economic uncertainty, such as those posed by the far-reaching impacts of the novel coronavirus pandemic, many life sciences companies are left on uneasy footing. Years prior, life sciences businesses would have been faced with a diverse array of tough decisions, including layoffs, product delays, or ultimately shuttering operations.

Today, however, businesses can tap into the capabilities and power of advanced analytics and digital transformation to not only boost the bottom-line but help a variety of departments in the business operate faster, cleaner, and more accurately. Below are just a few examples of how life sciences companies can leverage advanced analytics to better understand business trends and narrow in on areas to cut costs when it comes to their life sciences analytics.

Cost-Cutting in Life Sciences Analytics #1 – Cross-Channel Optimization

Personal communication with health care providers (HCPs) continues to prove to be valuable in sales for life sciences companies. The difficulty in cost-cutting across sales organizations is the ever-increasing pool of HCPs to target for business development, but with a finite sales team. This is where cross-channel engagement comes into play. Segmenting HCPs based on experience and interaction with a life sciences company allows for insight into how to best communicate with targeted groups of HCPs, optimizing interactions and supplementing with digital engagement.

Take this example of a leading biopharmaceutical company leveraging a clustering model to better understand how to communicate with their HCP population. The client used the provided insights to build a cross-channel communication strategy to better reach HCPs on a tactical level. Physician segmentation for cross-channel optimization can provide life science companies the framework to use low-cost digital assets with their existing sales team, avoiding increasing costs of supplementing.

Cost-Cutting in Life Sciences Analytics #2 – Creating Robust, Accurate Forecasts

A company’s historical data is a powerful input to predict future behavior, and better yet, comes at no cost. Although recessions and other industry disruptions may be beyond control, life sciences companies can hone in on their supply chain as an actionable area, specifically through forecasting. A forecasting model uses data-backed assumptions to control for macro changes (e.g. the financial landscape) in tandem with a company’s typical behavior. Companies can use a multitude of data sources (e.g. social media data, review data, and IoT data) to better understand drivers of demand. Robust data, in addition to a better understanding of the data, can provide greater visibility to events over time, allowing companies to avoid over- or understocking to minimize disparities in supply chain that can be costly.

Additionally, predictive models can be used to gauge the influence of competitor drugs. Understanding external factors like the competitive landscape is key to accurately planning a company’s own demand and working to minimize revenue loss based on competitor market entry. See this example of a pharmaceutical company understanding how a competitor drug would affect their current HCP/patient landscape.

Cost-Cutting #3 – Predicting Quality Interruptions/ Quality Specs

Gathering quality data as a product moves through its life cycle can build a better understanding of quality KPIs. When enough data is gathered to enable predictive capabilities, advanced analytics can enable earlier detection of issues in the production process. Cost grows throughout the manufacturing life cycle, so the earliest possible detection of quality issues can lead to major savings. Equipment predictive maintenance can optimize manufacturing up times, automate spare parts ordering, and use IoT data to predict equipment slowdowns or failures. These things allow a company to get ahead of the problem, protecting the business against liability claims while also reducing scrap, recalls, and associated costs.
In a macro sense, quality predictions can avoid costly recalls and qualitative hits to a company’s brand perception, events that could be incredibly tough for a company to handle in today’s climate.

Cost-Cutting #4 – Clinical Operations Improvements

The impact of advanced analytics for life sciences analytics in clinical operations is potentially transformative from faster, lower-cost trials to higher data quality. In this digital world, companies should strive for a strong data backbone and robust architecture, as this will improve business transparency and business agility, generate new insights, and reduce costs of IT and operations. The data architecture should include integrated data, supporting and new technologies, and value generating features.

By identifying and aggregating the smallest variations in performance, data can now provide executives with new capabilities to manage clinical trials and transform them. Across clinical trials, even small improvements in various areas, such as protocol optimization, trial forecasting, and risk based and real time monitoring, can add up to huge cost savings.

Cost-Cutting #5 – Advancing R&D Efforts

Especially in the bio pharmaceutical space, the use of analytics for life sciences analytics and digital capabilities combined with targeted therapies is helping to reduce the cost of drug development and accelerate cycle times to get treatments to patients faster. Many are shifting to cloud platforms to reduce data storage and processing costs and to boost R&D efforts. This allows companies to reach insights faster, demonstrate efficacy, and eliminate failures faster, ultimately reducing the overall cost of operations.

By leveraging molecular and clinical data, predictive modeling can also help identify new potential candidate molecules and improve the success rate of drug development. Here is a great example of a company using high-throughput systems to run more assays to efficiently evaluate new molecules or compounds.

The above examples demonstrate the tangible, diverse interactions that life sciences companies can have with advanced analytics in life sciences analytics, creatively supplementing sales strategies without additional costs and aiding in avoiding costly product supply and quality issues. In navigating today’s uncertain economic times, the incorporation of segmentation and predictive capabilities bring modernization to traditional business operations, allowing companies to best prepare themselves for the uncertainties that stem from changes in the economy.

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Contributions by Dayna Larson and Meghan McCullough

Tags: Advanced Analytics, Analytics