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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?


How CDOs & CIOs are Driving Digital Transformation

Ajay Khanna, VP of Marketing, Reltio

I recently got an opportunity to present at MITCDOIQ Symposium in Cambridge, MA. Here is the outline of my presentation where I discussed how today’s CIOs and CDOs are driving digital transformation across their enterprises. It discusses the key drivers for digital transformation and how Modern Data Management is helping them with their initiatives. You can now download my slides from the event from this page.

Today’s business landscape more dynamic than ever. New revenue models, stringent regulations and high customer expectations are forcing organizations to evolve or face being overrun by more nimble competitors.

CDOs and CIOs of established business are looking to digital transformation as a key initiative. But what exactly does digital transformation entail? At its core, any digital transformation requires clean and consistent data, reconciled across systems and channels. An enterprise-wide data management foundation that ensures real-time access to reliable data of all types at scale and is non-negotiable. Data access must be democratized across all groups and divisions so that teams can get a 360-degree view of customers, products, organizations and more.

However, it’s not just about disconnected siloed analytics. It’s about the next generation of operational data-driven applications that allow frontline business users to gain relevant insight and intelligent recommended actions so they can achieve their goals. This session explores how some of the largest companies in the world are transforming themselves using the same modern data management technology used by Internet giants such as Amazon, Facebook, LinkedIn and Google.

The presentation covers the following topics:

  • Changes in business environment and need for agility

  • Digital transformation drivers

  • Digital transformation examples

  • Data-driven digital transformation with Modern Data Management

Please fill out the form below to download the presentation slides:

Turning Customer Data into Actionable Insights

Ajay Khanna, VP of Marketing, Reltio

This week I got an opportunity to present at DBTA’s 2017 Data Summit conference. The topic of my discussion was “Turning the Customer Data into Actionable Insights.” All enterprises want to understand their customers better so they can engage the right customer, at the right time, with the right offer, via the customer’s preferred channel. The objective seems simple, but is quite hard to deliver if you do not have access to reliable data. Large volumes of data are being collected, but the data is scattered across multiple systems. There is no single source of truth across functional groups like sales, marketing and support. Different channels have their own version of the truth. Therefore, the customer experience remains disconnected, and customer insights are quite shallow.

The presentation covered how we can get to personalization at scale using Modern Data Management. The following aspects were covered:

Establishing a Reliable Data Foundation

To Make this experience more connected, we must bring the customer data together and then use that data for meaningful consumer insights and intelligent recommendations.
Start with connecting to all required data sources – internal systems (CRM/Marketing Automation etc.), external systems, social streams if needed as well, and enrich it with third-party data subscriptions as needed. Match, merge and clean the data to create a single, reliable source of truth of your customer profiles. Modern Data Management lets you identify potential matches and overlaps of the profiles. It helps to compare and contrast similar profiles and then automatically consolidate to create operational values using survivorship rules.

Please fill out the form below to download the presentation slides:

Uncovering and Understanding Relationships

The next important step is to reveal the relationships between the data entities. This where the graph technology helps us understand relationships – with a Commercial Graph (similar to LinkedIn or Facebook) you can relate customer profiles with products, accounts, family members and locations. You can establish many-to-many relationships between these data entities to understand where customers shop, what the products of interest are and who can influence their decisions. Uncovering relationships using graph technology helps you with identity resolution, finding influencers in the customer segment, or group individuals into a household and develop targeted campaigns. For B2B customers, you want to see the organizations and business units connected to it, key stakeholders and users of your products, or even contracts associated with various entities.

Single Source of Reliable Consumer Data for Operations and Analytics

Once you have the reliable data foundation, you can provision the data to all customer applications and channels for the connected experience. Moreover, you can provide the data to analytics systems to gain deeper insights about:

  1. Relationships: Modern Data Management lets you utilize predictive analytics and machine learning to guide users and provide intelligent recommendations, based on data and behavior. It helps with identity resolution, can suggest your new relationships and identify influencers (like LinkedIn.)

  2. Next-best-action: Recommendations like the next best offer to send to a customer, at the right time, using their preferred channel and identifying the key influencer to contact in an account and what to offer.

  3. Data quality: Recommendations to improve data quality by suggesting better matching rules, finding potential matches as you onboard new data sources and determining profiles with poor data quality and wrong addresses.

With Reliable Data, Relevant Insights and Recommended Actions enabled by Modern Data Management, we can understand the customer better and provide more connected experiences.