Dynamic Targeting: Revolutionizing Call Planning in Life Sciences

Over the past decade, the life sciences industry has undergone significant changes. Pharmaceutical commercial teams now have access to abundant information, enabling them to be more agile, efficient, and data driven. Despite this rapid evolution, many large organizations have stuck to the same call planning process for the past 25 years.

Initially, sales representatives relied solely on their own knowledge and relationships. However, with the advent of reliable prescriber-level data in the mid-1990s, companies began to explore ways to optimize their sales force targeting and planning. While monthly prescription data and customer master processes were introduced, they provided only limited and often outdated insights.

This outdated approach of “Smart Rep” to call planning has become apparent, necessitating a shift towards more dynamic targeting strategies.

In recent years, advancements in data and analytics have empowered sales forces with omni-channel connectivity, better insights, significantly boosting their effectiveness. Previously reliant on professional experience and relationships, representatives now benefit from access to near-real-time data sources and advanced analytical methods. This has not only enriched their understanding of customers but also provided valuable alerts and suggestions to enhance relationships further.

Today, life sciences companies utilize advanced master data management systems for a comprehensive view of patients and prescribers, integrating data from various sources such as claims, electronic health records, provider profiles, and customer omnichannel activity.

This enables pharmaceutical companies to leverage data, analytics, and technology for dynamic targeting in rapidly evolving markets. Unlike reactive call planning, dynamic targeting is a proactive, customer-centric approach that delivers a competitive edge and drives increased sales, even in challenging circumstances.

Dynamic targeting brings engagement

In today's fast-paced market, agility is crucial for life sciences companies to stay competitive. Failure to adapt to real-time changes can lead to missed opportunities and leave companies ill-prepared for the future.

Dynamic targeting revolutionizes how representatives engage with healthcare professionals. Unlike traditional methods, where reaching out to new doctors or responding to regulatory changes could take months, dynamic targeting enables immediate action. This agility is particularly vital during events like the COVID-19 pandemic, where swift responses are essential to navigating market shifts effectively.

Traditional call planning often results in a significant lag between market changes and company actions, undermining a company's competitive edge. In contrast, leveraging AI-assisted representatives equipped with Machine Learning and Artificial Intelligence allows for real-time access to data and insights. This empowers representatives to engage more meaningfully with healthcare professionals, providing timely and relevant information when it matters most.

Embracing this shift yields tangible benefits, with companies experiencing a 4 to 8% increase in sales, according to ZS research. By embracing dynamic targeting and leveraging advanced technologies, life sciences companies can thrive in today's dynamic market landscape.

Fear of challenge holds back companies

Indeed, the fear of change often holds companies back from embracing new technologies or processes. The prospect of company-wide adoption can seem daunting and risky, particularly when there's an inherent distrust of unfamiliar technology or a desire to maintain the status quo. As a result, some life sciences companies have hesitated to adopt dynamic targeting, opting to stick with traditional call planning methods, despite the challenges this poses for remaining competitive in a dynamic marketplace.

It's understandable that people gravitate towards the familiar. Embracing dynamic targeting would necessitate reps to adapt and undergo retraining. Brand and sales operations managers would need to establish new workflows to ensure timely implementation in the field, rather than relying on quarterly updates. Additionally, data and analytics teams would be tasked with maintaining data cleanliness for more frequent updates.

These changes may seem daunting at first, reminiscent of the initial struggles when new technologies are introduced. Consider the transition from physical maps to GPS navigation. Initially met with skepticism, GPS technology gradually became indispensable, providing real-time information on traffic, accidents, and road closures. Similarly, many companies shifted from costly data warehouses to cloud services like Amazon Web Services, while traditional brick-and-mortar stores embraced digital sales channels.

Despite initial reluctance, the benefits of embracing change often become apparent over time, leading to widespread adoption and improved efficiency.

Making the move to dynamic targeting

Transitioning to dynamic targeting represents a significant shift, but it's not an all-or-nothing endeavor. Reps can still leverage their local knowledge, while cross-functional collaboration remains essential for prioritizing requirements. Utilizing existing analytics approaches and data foundations helps set the stage for prioritization and allocation, but attention must be directed towards automation and optimizing field tools.

To achieve constant reallocation akin to GPS navigation, companies deploy analytical engines that analyze data and provide guidance to reps. This engine integrates inputs from various sources such as brand segments, resource allocation, and field feedback, adapting dynamically to market conditions and local insights.

Upon implementation, dynamic targeting enhances call-plan adherence and execution by providing a comprehensive 360-degree view of customers, integrating profile, promotion, patient dynamics, and payer access.

To transition effectively, pharma companies must:

Manage internal change: Navigate organizational changes effectively, enabling reps to adopt agile territory management while ensuring continuous call plan updates by sales ops and brand leadership.
Integrate new data gradually: Start with reliable data sources, gradually expanding insights as trust in the data grows, and gradually moving towards omnichannel coordination.
Increase insights and analytics capabilities: Begin with rule-based insights and progress to AI-enabled predictive and prescriptive insights, optimizing customer interactions and suggesting next-best actions.
Embrace user experience: Simplify technology and user interfaces based on user research to facilitate seamless adoption, enhancing insights adoption rates significantly.
Progress to full customer-centric execution: Orchestrate automated next-best actions to bridge the gap between planning and execution, transforming into a true integrated, customer-centric sales and marketing organization.

Encouraging change and innovation is vital for life sciences companies to thrive. Maintaining traditional call planning processes risks missing opportunities in today's dynamic market. Embracing dynamic targeting is not just a technological evolution; it's a necessity for staying competitive and achieving true customer-centricity in sales and marketing operations.

New Trends in Generative AI for 2024: Wh...

What’s ahead for generative AI in 2024? While we don’t foresee true breakthroughs this year, like GPT-4, we do expect steady improvements—and it will be fun to see how innovation starts to harmonize and scale. So, what should you expect? Let’s dive in.Retrieval-augmented generation (RAG) will mature to become mainstream and power most enterprise use cases. Large language models (LLMs) are approximate databases that mimic patterns in text rather than knowing anything. Developers work around this problem by using retrieval-augmented generation to combine search with LLMs to generate answers. This technique reduces hallucinations and enables users to verify answers through citations embedded in the model’s answers.Most current proof-of-concepts use simple retrievers, but these are based on cosine similarity, and they often fall short for several use cases. While advanced retrievers exist, there are too many narrow retrievers right now. We expect a consolidation across these retrievers and then the development of orchestrators to pick the right combination of retrievers automatically based on the use case. This step will eventually drive companies to replace enterprise search apps with RAG-based enterprise answers apps.Multimodality will be a new frontier, evolving chatbots to assistants that can see, listen and talk back. Multimodal learning has become necessary to improve the effectiveness of AI and thus has emerged as a new frontier. We expect a significant focus on multimodality from all generative AI model developers in 2024. With steady improvements, multimodal performance on benchmarks like MMMU, a test designed to assess college-level understanding of a range of tasks, will likely come closer to 80% (current benchmarks are 59.6%). Remember that “fake” Gemini Ultra demo? Those capabilities will become real. We’ll start to see more versatile and engaging interactions and a wider range of tasks that chatbots can effectively perform, including how they understand and respond to users.10B open-source models will perform at par with GPT-4, enabling ubiquitous local deployment. Smallish models like Mixtral, Solar and Phi-2 have been punching way above their weight. So far, reinforcement learning from human feedback has been the primary limitation for the open-source software (OSS) community because collecting data for fine-tuning is expensive. However, self-play and sample-efficient fine-tuning techniques have led to smaller OSS models performing at par with proprietary models. Once reached, this milestone will make generative AI models ubiquitous because developers can deploy them on any local device, owing to their small size.We’ll see a wave of wearables powered by LLMs. Most of them will fail. While the software space is crowded, the hardware space is still predominantly mobile phones, so we expect significant interest in imagining and developing new generative AI-based devices ahead. However, these will face severe headwinds on privacy, security, safety and subpar user experiences (UX). UX designers will require several iterations to strike gold—if they have any to find.Enterprises won’t use OpenAI’s app store for GPTs. We anticipate that the GPT marketplace will be hosted in OpenAI cloud in SaaS mode, just like GPT models. This step is a spin-off of the plugins that OpenAI shut down recently. However, we don’t think enterprises will widely use these GPTs, primarily due to data security concerns (see number 7 for what they will do). Consumers will need help navigating the marketplace to buy GPTs (like their experiences with Alexa Skills).Domain-specific reasoning and planning AI will trigger yet another wave of enterprise adoption. Generative AI models are still far from achieving reasoning and planning capabilities, and we don’t see a path that will lead them to that anytime soon. However, companies will be interested in how generative AI models could blend with traditional planning and simulation software and learn domain-specific reasoning and planning capabilities with self-play.Enterprises will start leveraging OSS models and develop custom LLMs with their data. So far, most enterprise adoption has been for proprietary models, like OpenAI and AWS Bedrock, but their adoption has been stunted primarily because of data security and privacy concerns. Once the OSS models cross the GPT-4 threshold, we expect a significant shift toward OSS models. This step will drive finetuning with in-house data to develop custom models.Enterprises will start realizing productivity gains from generative AI. Here, insights teams will transform data analytics with generative AI-based copilots. We expect turn-around times for analytics questions to be five times faster than they are today. Content generation will heavily leverage LLMs, with 75% of new content based on AI-generated drafts. In customer service, AI agents will become the first “person” a customer talks to, routing only complex queries to human agents.Existential risk voices will quiet, and the focus will shift to regulation. The EU’s AI Act will pass, and institutions like NIST and OECD will develop standards around risks involved in generative AI models and standards for data used for training these models. Overall, policymakers and regulators will favor content creators more than model developers.Alignment will continue to be a tough nut to crack, as the work to align models to human expectations needs breakthroughs. New companies and jobs will emerge around teaching LLMs for alignment, including how to curate and create data for alignment. Until there are better answers, we expect companies to live with the costs of ensuring people who use models in the workplace recognize how they may be biased, untruthful or potentially harmful.

Making the Right Choice: Cloud vs Hybrid...

Introduction:In today's digital age, cloud computing has become an integral part of business operations. The ability to store and access data and applications over the internet has revolutionized the way companies operate. However, with the emergence of hybrid cloud solutions, businesses now have to make a choice between traditional cloud computing and hybrid models. In this blog post, we will explore the benefits and considerations of both options to help companies make the right choice for their needs.Understanding Cloud Computing:Cloud computing refers to the delivery of computing services over the internet. It allows businesses to access and store data and applications on remote servers instead of relying on local infrastructure. The benefits of cloud computing are numerous. Firstly, it offers scalability, allowing businesses to easily scale their resources up or down based on their needs. This flexibility is particularly beneficial for growing companies or those with fluctuating demands. Additionally, cloud computing is cost-effective, as it eliminates the need for expensive hardware and infrastructure investments. Popular cloud service providers include Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform.Exploring Hybrid Cloud Solutions:Hybrid cloud solutions combine the benefits of both public and private clouds. They allow businesses to have a mix of on-premises infrastructure and cloud-based services. This approach provides enhanced security, as sensitive data can be stored on private servers while non-sensitive data can be stored on public cloud platforms. Hybrid cloud solutions also offer greater control over data, allowing businesses to comply with industry regulations and maintain data sovereignty. Furthermore, customization options are available, enabling businesses to tailor their cloud infrastructure to their specific needs.Factors to Consider when Choosing between Cloud and Hybrid:When deciding between cloud and hybrid solutions, businesses must consider several factors. Firstly, data sensitivity plays a crucial role. If a company deals with highly sensitive data, such as personal or financial information, a hybrid solution may be more suitable as it allows for greater control and security. Compliance requirements are another important consideration. Certain industries, such as healthcare or finance, have strict regulations regarding data storage and privacy. In such cases, a hybrid solution can ensure compliance while still benefiting from cloud services. Budget and scalability needs should also be taken into account. While cloud solutions are generally more cost-effective, businesses with predictable workloads and long-term plans may find a hybrid model more suitable.Conclusion:In conclusion, the choice between cloud and hybrid solutions is a crucial decision that businesses must make carefully. By considering factors such as data sensitivity, compliance requirements, budget, and scalability needs, companies can make an informed choice that aligns with their goals and objectives. Seeking expert advice and conducting thorough research is essential in making the right decision. Ultimately, the goal should be to leverage the benefits of cloud computing while addressing any specific needs or challenges that the business may face.