“The ability to learn faster than your competitors may be the only sustainable competitive advantage.”
-- Arie de Geus, Former Head of Royal Dutch Shell's Strategic Planning Group

One industry that has been at the forefront during the pandemic, and for obvious reasons, is pharma. The alacrity with which the sector has responded to the challenge at hand, readying COVID-19 vaccines for use in record time, is simply remarkable. A McKinsey research emphasizes how in comparison to the average drug development timeline of ~10 years, a few COVID-19 vaccines have been approved for use in a matter of months since the outbreak. However, it has also triggered a fundamental question. Can this now become a standard practice?

Undeniably, the pandemic has been a wake-up call and there is a need to dramatically shorten timelines to develop newer drug formulations and have those formulations pass clinical trials and reach the market successfully. However, achieving this in an environment characterized by increasing complexities, continuous change, hyper-competition, and stringent regulations is challenging, to say the least.

This calls for discarding redundant and dated strategies and embracing innovative capabilities centered on digital, automation, and analytics. One of the key areas that is experiencing a momentous change is Competitive Intelligence (CI). CI platforms, for all their ability to convert competitive knowledge and data into actionable insights at lightning speeds, have come into sharp focus. According to Fortune Business Insights, the market for such platforms is expected to double before the end of the decade, increasing at a Compound Annual Growth Rate (CAGR) of more than 10 percent.

Innovative Technologies Are Driving and Re-shaping CI

Innovative technologies and agile practices are having a profound impact on CI. These are enabling enterprises to create comprehensive intelligence and forward-looking insights. Some of the key elements that constitute the core of robust and sophisticated CI platforms include:

  • The use of Artificial Intelligence (AI) and Machine Learning (ML) is enabling to collect, analyze, curate, and disseminate information across R&D, regulatory compliance, and sales and marketing. The ability to capture and deliver therapy-specific insights can be highly relevant and actionable for brand managers. AI / ML-driven recommendation engines, coupled with deep domain knowledge, enable tracking and analysis of user behavior and provide intelligent recommendations for relevant content. As a result, brand managers in life sciences companies can get a unique view of the competitive landscape by evaluating marketplaces and drug approval timelines, all along having the option of scalability enabled through AI / ML.

  • The ability to access relevant and contextualized content. User configuration helps increase the actionability of the insights and drive business decisions on time, thereby maximizing impact on strategies like go-to-market, pricing, and market demand, to name a few.

  • Easy Application Programming Interface (API)-led integration of external data sources that supports business reporting on Key Performance Indicators (KPIs) in one consolidated platform. This integration is a very effective functionality for brand managers, and allows access to all relevant intelligence, data, and insights in real-time, within the same interface.

Deploying the Right CI Platform for Your Organization

The efficacy of a CI strategy needs to be measured across several parameters and not just ROI. This implies that for any strategy to work in today's context, it has to account for disruptions and newer result-oriented learnings. The mainstay of a robust CI strategy lies in agility, transparency, and comprehensiveness.

To formulate an effective CI strategy, organizations need to be powered by the right and most comprehensive data and insights. A CI platform will help enable this. However, the critical aspects to factor in as you deploy a CI platform are:

  • Fostering synergies between different functions, therapy areas, and stakeholders for creating a cohesive picture of the market landscape

  • Monitoring factors such as recent innovations, prevalent care models, research patterns, and market competition

  • Keeping a close eye on competitors' clinical trials, Health Economics and Outcomes Research (HEOR), and regulatory information

CI platforms are increasingly becoming table stakes. While there are platforms that score high on user engagement and modularity, there are those that specialize in providing an intelligent data pipeline. On the other hand, holistic solutions provide user configurability as well as intelligent and relevant data through self-learning AI / ML algorithms. They can be nimble and accommodate evolving data by providing insights that lead to decision support and realistic market analysis.

An ideal CI platform will be one that not only yields tangible ROI, but also provides the foresight to stay ahead of the competition and anticipate market developments well in advance – in the form of relevant and automated therapy-specific intelligence and insights.

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