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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

Data-driven Apps will Power the Next Generation of Pharma Marketing

This week, I was invited to present in front of a distinguished group of pharma marketing professionals at the recent Digital Pharma West conference held in San Francisco.

It’s clear that there continues to be significant interest by marketers to use data to help improve campaigns and outcomes. My presentation detailed how Pharma marketing is undergoing a tremendous change with new stakeholder and marketplace dynamics, with a more sophisticated consumer, and interconnected digital world.

Clearly the vast amount of information that marketers need to internalize to develop effective campaigns is simply daunting. Legacy IT systems set up to address repeatable processes are not able to scale to modern day demands, and contrary to popular opinion, standalone visualization and analytics isn’t a panacea either.  

In fact, a recent survey by Trailblazer research of marketing professionals highlighted the following reasons for their dissatisfaction of their existing marketing applications and processes:

  • Ease-of-use

  • Limitations surrounding customization capabilities

  • Lack of partner/vendor support

  • Lack of in-house expertise

  • Excessive costs

  • Difficulties surrounding implementation

It’s no wonder that marketers are crying out for linkedIn or Facebook-style enterprise data-driven applications equivalent to those that they themselves as consumers get to use on a daily basis.

In fact the survey further revealed some of the data management pain points preventing marketing teams from being agile and more targeted in their efforts, and their corresponding plans to address the issue:

Many of the 10 pain points can be addressed through a modern data management platform, that offers reliable data through master data management, big data, data-as-a-service, seamlessly fused with relevant insights with recommended actions through analytics and machine learning.

  1. Access customer data without IT or 3rd-party support

  2. Add new social or messaging channels quickly and with ease

  3. Analyze cross channel efforts to gain marketing insights

  4. Deliver consistent messages across all channels

  5. Deliver relevant, contextual messages to customers in order to create ongoing dialog

  6. Do A/B testing to improve email campaigns

  7. Integrate messages, data, and insights across siloed channels

  8. Turn unknown website visitors into identifiable prospect opportunities

  9. Use predictive analysis to improve marketing effort

  10. Use real-time data and insights to drive personalized next-best offers

During the session we also discussed what the future holds, including IoT and growing interest in data monetization

A lively panel following my presentation offered further insight from experienced pharma marketing executives appropriately titled “OK, So Pharma Is Now Successful at Data Aggregation, So What? How to Take an Applied Approach to Leverage Data in a Meaningful Way: Using Data to Meet Objectives.”

The panel was moderated by Nuvan Dassanaike , Vice President, Lead, Global Integrated Marketing at Mylan and included: 

  • Bill Keller , Vice President of Marketing , Acadia Pharmaceuticals

  • David DeJonghe , Worldwide Director of Marketing, Digital Solutions and New Product Development , Lifescan, a Johnson and Johnson com

  • John Vieira , Senior Director, Global Brand Strategy, Edoxaban , Daiichi Sankyo

Being data-driven may sometime be deemed an overused term, but for marketing professionals in pharma, it’s very much becoming the norm. If you would like a copy of my presentation or to exchange thoughts on how data is the new lifeblood of life sciences, please send me an email or leave a comment below.

Why Life Sciences Must Go Beyond Master Data Management (MDM)

Many of the team here at Reltio formed the nucleus of Siperian (acquired by Informatica in 2010), the leading on-premises MDM tool widely adopted by life sciences companies. Back in 2005, master data management  (MDM) was just taking shape and companies used MDM primarily to improve Siebel CRM data quality before upgrading and migrating their on-premises systems. Back then Siperian was preferred by many to the “seamlessly integrated Siebel Universal Customer Master (UCM)” offering, proving that best-of-breed solutions can be superior to integrated offerings that are designed for a single primary purpose.

One of the biggest issues we faced with Siperian (now Informatica MDM) was defining a relational life sciences data model that could capture not only the basic attributes of healthcare professionals and organizations, but represent real-world  HCP-to-HCP, HCO-to-HCO and HCP-HCO relationships. Also thrown in for good measure was an emerging need to master product data, product hierarchies, groups and baskets for pricing and competitive analysis, and to feed product information (PIM) systems.

At Siperian we admittedly struggled with basic hierarchy management and performance issues with merge and especially unmerge. While we preached multi-domain and coined the term Universal MDM, we were never completely successful with standalone product master data management, let alone bringing together both customer and product data into a single consolidated Siperian Hub.

Back then, the best databases we had to model and store life sciences entities and their relationships were the likes of Oracle, DB2 and SQL Server. Cloud and big data technologies such as graphs, columnar stores, HBase (on Hadoop) and Cassandra simply weren’t available.

Fast forward to the present, the MDM landscape remains more or less unchanged despite a quantum leap in technology. 

  • Informatica MDM is still an on-premises solution with many of the same challenges we faced while at Siperian

  • Cloud-based customer master only offerings focused on improving CRM data quality a limiting. Much like Siebel UCM did for Siebel CRM over 10 years ago

  • Customer and product masters are still supported through separate siloed hubs, even when built using the same tool. In fact, Gartner continues to publish separate customer and product magic quadrants as if to re-enforce this fact

  • Master data must still be delivered to data warehouses or operational data stores in order for business users to get a promised “complete view”

  • MDM systems and tools built on 1990s relational database technologies continue to hinder the ability to model real-world many-to-many-to-many relationships that graph technologies are designed for

For the most part, life sciences companies are no closer to getting basic affiliation management functionality, or their dream of an all encompassing key account management application as they are hindered by legacy MDM tools. Even a new wave of cloud-based MDM solutions do not make things any better. The good news is that life sciences companies can avoid a new kind of MDM (“Making Da-same Mistake”).

The most popular consumer facing applications today such as LinkedIn and Facebook have shown that business facing data-driven applications can be cloud-based, handle multiple data domains, manage structured, unstructured, master, transactional, activity and social data. Companies should expect complete end-to-end “modern data management” instead of relying on recurring “next generation master data management” promises that remain unfulfilled.