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Data Secrets to A Successful Drug Launch

Ankur Gupta, Sr. Product Marketing Manager, Reltio

Value from pharma should be measured in terms of clinical outcomes, patient satisfaction, and cost reduction. Using data, pharma companies can enhance value for patients along the entire lifecycle of a drug, from drug discovery to commercialization to end of exclusivity.

From the perspective of business strategy, value delivery can be seen as a three-step process as illustrated by David Ormesher, CEO of closerlook, in his PharmExec.com post.

  • Value Creation (discovery)

  • Value Capture (commercialization)

  • Value Extraction (end of exclusivity)

Discovery Phase: Value Creation via Data

It is important to capture unique customer insight to inform drug innovation. The drug should be relevant (to an urgent disease burden) as well as differentiated (relative to alternate therapies). These two factors will largely determine market access, provider endorsement and patient acceptance for a new drug. However, departmental silos between medical affairs and commercial side of the business, and lack of access to quality data lead to incomplete understanding of competition and the market.

A Self-Learning Data Platform goes beyond a traditional master data management (MDM) offering and brings together patient, provider, payer, and plan data from internal, third party, and public sources to cleanse, match, merge, un-merge, and relate in real time. Platform’s multi-domain data organization capability helps perform deeper analysis to better understand the needs of patients, providers, payers, and relationships among these players. A Self-Learning Data Platform breaks down silos among medical affairs, marketing, business intelligence and manufacturing, and helps develop a common understanding of customer data and market insight across all departments.

Research indicates that 81% of future drug sales performance is determined by actions taken during clinical development and early commercialization phase. It’s even more critical for a pre-commercial pharma which is planning to bring its first drug to the market. Early adoption of a Self-Learning Data Platform helps a pre-commercial pharma develop future-proof commercial infrastructure and put up business processes to launch their first drug with safety, efficacy, and desired formulary placement in place. Read the pre-commercial pharma success stories about how they successfully launched their first drug with the help of a Self-Learning Data Platform.

Commercialization Phase: Value Capture via Data

A new product’s commercial performance during the first six months after FDA approval is often considered a very important indicator for how the product will do over the course of its patent life. During Value Capture or commercialization phase, the purpose of data is to build trust and respect via data-driven personalization and engagement. However, pharma companies are unable to recognize prescribers and patients consistently across multiple channels and touchpoints. They often fail to increase content speed to market in their customers’ preferred channel. This leads to negative Net Promoter Score (NPS), increased defection to competitors, and loss of revenue and market share.

The more you know about your customers – the physicians who can write the product – and what they care about, the more you’re able to build an effective campaign around a new product. What you need – an out-of-the-box, data-driven affiliation management application, with built-in MDM, for managing all relationships within and across HCOs and HCPs to support commercial operations, identify the right key opinion leaders (KOLs), and understand their influence.

A Self-Learning Data Platform helps you organize launch as a micro-battle (See the Infographic “Make Your Drug Launch Truly Take Off”, Bain Insights, September, 2017), gather continuous front-line feedback from sales reps before, during and after the launch, and make rapid adjustments as needed to the launch strategy. It helps you make quick decisions on messaging, targeting and marketing investments. Such platform powers reliable advanced analytics by enabling master data profiles and graph relationships to be seamlessly combined with real-time interactions and analyzed in Spark. For example, when a new drug is launched, it helps track sales performance compared to projections so that you can adjust strategies whenever needed.

Read the success story of a French multinational pharmaceutical company that built Customer 360 on top of a Self-learning Data Platform to support their account-centric field operations and personalized engagement.

Loss of Exclusivity Phase: Value Extraction via Data

At the point when a drug loses its patent protection, its price typically drops quickly as generic competitors enter the market. During this phase, there is often enormous pricing pressure from competitive products and health insurers. In addition to these external pressures, there is also internal competition for attention and resources, usually from a promising new product.

The business strategy during Value Extraction is to increase efficiency via operational excellence. The main cost now is sales and marketing. This is where digital can play a very strategic role. Digital sales and marketing through non-personal promotion can become an effective substitute for sales rep promotion. By replacing expensive personnel costs with lower cost digital channels, we can reduce overhead costs but still maintain market share.

Read the success story of one of the oldest and largest global pharma that consolidated customer profile across all business functions to improve customer experience across all digital touchpoints, and better engage high-value customers.

Successful pharma companies use data as a competitive weapon to develop new sources of differentiation, focus on building superior customer experiences and treat drug launches as a micro-battle. How did your last launch perform vs. expectations, and what were the reasons for under-performance or over-performance? Which interactions matter most for your target physicians, and do you provide a superior customer experience? What are the three largest internal challenges your launch team faces, and what would it take to eliminate them?

Read more Pharma Commercial Success Stories


Why Master Data Management and Machine Learning Go Hand in Hand

Ramon Chen, Chief Product Officer, Reltio

Reltio’s inclusion in the The Forrester Wave™: Machine Learning Data Catalogs Q2 2018, by Michele Goetz with Gene Leganza, Elizabeth Hoberman, and Kara Hartig, Forrester Research, June 2018, sparked (pun intended) several questions. Such as why was Reltio included, how did we receive such strong marks, and why were we the only Master Data Management (MDM) vendor in the Wave?

The simple answer is that the Wave’s qualification criteria includes several key areas in which Reltio is naturally strong. As the only Master Data Management platform recognized in the Wave we believe our core MDM capabilities contributed to our strong showing. In fact, Reltio had already been included, together with 23 other excellent companies, in Forrester’s Now Tech: Machine Learning Data Catalogs, Q1 2018 report preceding the Wave.

That report outlined key 3 characteristics of Machine Learning Data Catalogs (MLDC):

1. Interpret, define, classify, link, and optimize the use of disparate data sources

Reltio is used by companies globally to define logical business schemas, capture and discover relationships through the Reltio Self-Learning Graph, suggest ongoing improvements, and to organize and bring siloed data together across the enterprise to meet their business objectives. This continuous reliable data foundation feeds better operational execution, predictive analytics, and sets them up to evolve towards a self-learning enterprise.

2. Reconcile policies across data use

Reltio Cloud’s built-in data security and privacy, regulatory, life-cycle, and data quality policies coexist to adapt data for multiple uses through a powerful audit trail, and role based access to data. This is critical in the face of evolving compliance measures such as GDPR. Flexibility and agility to ensure that you can track not just where the data originated, but how it’s being used and the outcomes it generates, is a critical component of any forward looking ML strategy.

3. Democratize data to the edge of business

Reltio is particularly well suited to meet this requirement through frontline business user facing data-driven applications and workflow and collaboration capabilities that come OOTB with Reltio Cloud. It allows teams to submit comments, suggestions, filter and easily segment information through a UI that’s as easy to use as Facebook and LinkedIn.

Data science teams are then able to use Reltio IQ, with Apache Spark to run their algorithms without the pain associated with cleaning and onboarding data in separate environments. This is increasingly important as enterprises deploy machine learning systems, with data scientists requiring relevant, curated data sources to train algorithms to improve results.

As this video illustrates, the true value comes from being able to synchronize ML-algorithm derived IQ scores back into master profiles as aggregate attributes. Making them available for segmentation by marketing, sales, and even data stewards and compliance teams. Teams can then continuously reconcile results to recommendations in a closed loop to self-learn and improve outcomes.

We are tremendously proud and honored to have been included in the MLDC Wave as it reflects our core belief that machine learning cannot be used in isolation from the overall data organization and management needs of the business.

Whatever your desired outcome, MDM forms the backbone of high quality, reliable data which allows ML to thrive.

ML in turn provides unique capabilities to improve and increase the efficiency of data quality, and enterprise data organization operations. Like the graphic I selected for this post, they go hand in hand, and are interconnected across all points of the data continuum and life cycle.

Patient 360: Molecule to Market

Ankur Gupta, Sr. Product Marketing Manager, Reltio

The rise of the Chief Patient Officer and the “P–suite” emphasizes a commitment to a culture around patient-centricity across life sciences companies. Patients are becoming increasingly demanding and taking greater control of their own healthcare decisions. They expect all relevant parties like pharma, providers, and payers to collaborate and recommend the best treatment options.

It is essential for a pharma company to know their patient throughout the drug discovery, development, and commercialization process. Every department across a pharma company can contribute toward and benefit from complete patient understanding. Some of the use cases are:

1. Patient-centric Drug Discovery and Development

Recruiting and retaining the right patients, and capturing all interaction and transaction events during clinical trials are vital to continuously develop new diagnostics and treatments. Patient-centric clinical operations lead to improved clinical trial outcomes, reduced patient exposures to drug adverse events, and faster drug discovery.

Today, reliable data, relevant insights and recommended actions via machine learning can be combined into one, single cloud application, delivering analytical intelligence and operational execution. Such cloud based Patient 360 data-driven application helps pharma companies derive meaningful patterns from an ever-expanding volume of patient health data and incorporate those insights into the drug development processes. 


A Patient 360 application built upon a self-learning data platform delivers reliable, and up-to-date 360-degree views of patients, and their relationships with providers, healthcare organizations, caregivers, payers, plans, products and places, driving seamless omnichannel patient experience and improved health outcomes.


2. Personalized Corporate and Marketing Communications

Pharma companies are increasingly seeing more value in reaching out patients more personally and directly to improve patient loyalty and brand recognition. They want to execute direct-to consumer (DTC) drug advertising campaigns, deliver educational insights (such as medical information and pharmacovigilance) to inform patient decision-making and behaviours, and encourage patients to contribute their medical data to help advance medical knowledge.

A true Patient 360 data-driven application helps with prospect identification, capture, synchronization to CRM, and segmentation and targeting of existing customers and prospects in various life-stages. As part of the patient centric approach, brand-focused marketing is juxtaposed with the creation of content that supports a patient’s journey through disease progression. In addition, the Self-Learning Graph helps solve the problem of “householding” by grouping patients into family units by uncovering relationships. This patient-centric approach helps pharma companies to gain “profitable share” in competitive markets by informing their ‘pricing and contracting’ strategy and identifying treatable patients. 

3. Superior Patient Experience with Full Compliance in Place

Pharma companies can add far more value to patients by executing adherence programs such as tracking drug usage and benefits. Likewise, they can run affordability programs to help patients stay on therapy (e.g. by creating apps to educate patients or by reminding them about medications). However, to drive such initiatives, one needs to collect and use large amounts of sensitive health-related data of patients. A modern data organization platform helps you respect and protect patient HIPAA and data security concerns. In addition, it helps you be GDPR compliant and allows patients to provide granular consent for sharing their data.

The data forms a key part of the insight needed to create better products and services, better engagement, adherence, and relationships with patients. Changing business models, expectations of “patient of one” and newer regulations will accelerate the evolution of pharma and healthcare. The transition will not be easy, but building a reliable Patient 360 with ability to pivot around pharma, provider, and payer is the first step towards patient-centricity.