Analytics in Life Sciences Improve Life Expectancies

The heavily regulated and IP-driven life sciences industry operates in a state of continual flux. This dynamic nature of the industry throws a diverse mix of powerful challenges and equally promising opportunities at the pharmaceutical executive all the time.

Some of the key challenges facing pharmaceutical companies at the moment are:

  • A patent cliff
  • Dwindling pipelines and late-stage attritions
  • Rising cost of commercialization
  • A host of healthcare cost containment measures adopted by regulators and payers across key markets

To offset the revenue loss triggered by major patent expirations, companies have moved towards innovative therapies—often biologics—for niche indications with high commercial potential.

On the brighter side, changes in demographic structure in developed economies and shifting disease profiles in the developing economies have created large patient populations with chronic/lifestyle diseases. The emerging markets offer new opportunities with a growing population, increased purchasing power and progressively greater access to healthcare supported by the proliferation of private health insurance. To tap opportunities in these emerging markets, drug manufacturers have attempted to look beyond branded pharmaceuticals into generics and biosimilars. Although this relatively low-risk, low-return diversification strategy has helped counterbalance the high-risk, high-payoff strategy of entering the innovative biologics space, it has affected profitability adversely.

To be able to better address the challenges and to optimally realize the available opportunities, the pharmaceutical industry needs to significantly augment its commercialization capabilities. Here is where analytics comes in.

Performance Analytics

The successful execution of a marketing and sales strategy by the field force drives commercial efficiency. Performance analytics is an enabler that ensures companies can realize their objectives. Analytics can help in two important ways: enabling accurate customer segmentation and targeting and tracking performance through brand performance tracking. While segmentation helps companies identify the high-potential customer groups, performance tracking lets them continuously gauge performance against the target segments selected.

The traditional approach to segmentation and targeting leveraged basic statistical techniques such as deciling (of customer groups such as hospitals, pharmacies and prescribers), but more sophisticated techniques for segmentation are now available that integrate the element of probability into past behavior. Apart from segmenting customers at the hospital, pharmacy or physician level, companies can segment at the patient level in order to help develop direct-to-patient communication or to let physicians establish therapeutic connections between a prescription drug and relevant patient profiles (those that are most likely to benefit).

Forecasting

Forecasting is an important exercise with several applications. The objectives range from pre-launch assessment of market potential (while introducing products to new markets), medium-term brand planning for inline products, demand planning for manufacturing operations to the strategic assessment of a molecule’s potential for in-licensing decisions, etc.

  • Time series-based forecasting is done for inline products using historic sales data to arrive at projected sales numbers for the future. A baseline forecast is capable of delivering risk-adjusted net present values of future streams of cash flows. Companies can then refine this baseline forecast with market intelligence in the form of commercial, regulatory and clinical events of strategic importance, e.g. new product launches by competitors, patent expirations, NDA filings, Phase III results of major clinical trials, etc. Such “evented” forecast models invariably incorporate inputs from the product marketing teams to build a consensual forecast.
  • Epidemiology-based market modeling is relevant while considering a new product introduction. An epidemiology-based model attempts to arrive at the revenue potential of a product based on a clear understanding of epidemiological trends, prevalent treatment algorithms, key patient segments, prices of gold standard therapy and potential adoption rates, etc. Epidemiology-based models can help assess peak sales of a novel therapy, a critical parameter in go/no-go decisions for in-licensing efforts. Simulation models can now support all forecasts to enable sensitivity testing of the included variables.

Optimization

Optimization techniques can help informed decision making when there are many constraints executives must consider while trying to achieve an objective.

  • Sales force optimization helps optimize the size and structure of a sales force for a territory based on physician’s prescription potential and propensity to respond. The model delivers a sales force grid by brand and region that ensures optimal reach and call frequency.
  • Promotion mix optimization similarly lets the marketer allocate resources to different promotional channels such as physician contact, professional advertisements, journal advertisements, sampling, etc. to optimize the promotional spend across channels with an assured maximum ROI.

TCS offers a complete suite of analytics offerings spanning solutions such as these–performance analytics, forecasting and optimization–supported with a well-deployed competitive intelligence system (covering activities such as pipeline assessment, situation assessment, competitor profiling and conference intelligence, etc.) and can significantly boost the commercialization capabilities of a pharmaceutical manufacturer. TCS supports two of the largest global pharmaceutical data vendors in a wide range of analytics and reporting activities (including performance analytics, predictive analytics and optimization) and data management.

Author’s Profile

Anthony is responsible for conceptualization and implementation of analytics-powered responses to commercialization challenges in the life sciences domain. His team focuses on delivering continuous analytics (performance analytics, reporting, and dashboarding services), and on-demand analytics (issue-based analysis, forecasting, and optimization & simulation). Anthony has over 8 years of industry experience in diverse roles spanning pharmaceutical marketing & sales, marketing research, healthcare consulting, and commercial analytics delivery. He has held various positions with AstraZeneca, Datamonitor and Tata Consultancy Services.

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2 Responses to “Analytics in Life Sciences Improve Life Expectancies”

  1. usually says:

    a good deal addiitional information with this kind of issue on websites, observe.

  2. Manish says:

    Thanks for Informative and interesting

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