New Approaches to Create a Fail-Proof Launch

The odds are not in your favor.

According to a McKinsey & Company report, two-thirds of drug brands today fail to meet sales expectations for their first year on the market.

Even companies with a strong heritage of success aren’t immune. You’ll see drugs from Sanofi and AstraZeneca among the many brands that have suffered the humility of a place on top 10 launch disaster lists.

And it only gets worse. McKinsey also found that 78% of the drug brands that failed to meet launch expectations continued to lag behind in year 2. And 70% remained behind forecast in year 3.

It’s clear that a strong launch is critical to brand success. But it’s easier said than done when you have a high level of drugs coming through the pipeline alongside intense competition.

Now for the good news. Marketers today have a secret weapon their predecessors never had: Real World Data, Big Data and Artificial Intelligence. We can collect and examine a large combination of data from the real world (patient registry data, social media data, claims data, sensor data, CRM data, etc.) and understand the key areas in the launch plan like never before.

So how can marketers utilize this technology to ensure that their launch succeeds against the odds — even with a large number of smaller launches, hyper-intense competition, and changing customer needs and behavior? And how can they maintain a focus on superior patient outcomes and payer value at the same time?
Actually, there are several ways. Successful product launch planning can be broken up into four main categories of focus, which can be enhanced with Artificial Intelligence.

1. Market
Understanding your market structure and market landscape is a critical first step in planning for your launch.
Along with the trend toward increased consumer engagement we’re seeing an increasing protocolization. Physicians and other providers are accepting and using more standardized protocols and guidelines for treating their patients. US providers have been somewhat slower to embrace clinical protocols than their European or Asian counterparts, but there is little doubt about the direction of this change.

The individual physician is no longer will be the lone decision maker. Medical care is being industrialized, as payers and providers increasingly align their businesses —and results  —and pharma needs to understand and work with this process.

To get to the underlying drivers of these decisions, especially in markets with changing treatment paradigms, we combine big data. This allows us to examine the critical questions to map out how the market landscape is structured and changing and to understand more fully both the what’s and the why’s.  And because this big data is often real-time data, we get a more dynamic view of the market and changes within it.
Stakeholder and Key Opinion Leader (KOL) mapping is also an important part of the process. Utilizing AI to identify the up and coming KOLs who would be a good investment of time and resources within various areas of the business – including clinical, medical affairs, commercial, sales and marketing  — we can create thorough influencer mapping and interconnectivity. You’ll be able to detect any blind spots of missing important influencers and identify opportunities for focused sales and marketing action.
By using big data and Artificial intelligence, you avoid institutional biases and erroneously identifying the LOL (Loud Opinion Leader) ‘has beens’ rather than real KOLs. Once you know who to target here you can start to build relationships and advocacy with the right people.

2. Customers
It is possible today, to integrate multiple touchpoints for individual customers (their discussions with sales reps, their online interactions, their web browsing, their prescribing behaviors, etc.) to provide a more cohesive 360-degree view.
Types of things we can use Artificial Intelligence to examine here include:
    •    Customer unmet needs
    •    Physician and Patient segmentation and targeting to home in on how to play to win in this space
    •    The optimal key messages that resonate with most customers and are in line with the unmet needs and clinical trial data, to be adapted by individual customer for maximum impact with use of AI.
    •    The key driver channels and sequences by individual customer.

Then we can apply modeling to understand the drivers of each customer’s behavior and preferences, and identify which customers are the higher value opportunities and which are not.

Combining all of this we can create modelling to serve up the right messages to the right customer at the right time in the right sequence to maximize the customer engagement and journey to your brand.

We can also identify which will respond to a sales rep, and which will not, as well as which will be likely to be influenced by specific messages and channels, and which will not.

These can then be programmed and automated into systems, alongside modelling to serve up the next best content, channel sequence and action for that individual customer.

3. Product
The top four strategies for developing and marketing strong products are:
    •    Creating a strong value proposition
    •    Standing out from the crowd in a way that meets the customers’ unmet needs
    •    Creating a category
    •    Shaping a market

These are obvious. But how do you actually achieve them?

One way to understand a market is to use big data and custom Artificial Intelligence algorithms to identify things such as:
    •    Where the opportunity spaces to play are for the brand
    •    Combining customer unmet needs and where the product/brand would have the most chance of success
    •    The optimal value proposition for the brand
    •    Market access and optimal pricing to demonstrate superior patient outcomes (potentially in a niche) and superior value for payers
    •    The optimal brand positioning to create distinctive points of relevance and differentiation

Market access and planning your pricing to reflect strong value to payers is a critical part of the mix. The traditional ways of doing this clearly (based on the fact that this tends to be one of the key areas for failure of launch success) are inadequate and flawed. Using machine learning algorithms, Eularis now have found a way to utilize big data (claims data, patient registry data and clinical trial data) to identify the optimal pricing that offers payers a strong perception of value, while providing the highest profit to pharmaceutical companies.

4. Organization
One of the key failures identified in launch failures was lack of resources. We all know pharma are having to do more with less these days with all the pressure on the bottom line from both expired or expiring patents and lack of uptake for new drugs. However, without putting adequate resources behind the launch, the product could fall short.

Big data can be used to ensure that the right resources are put in the right places.  Determining the right amount of budget for field force versus non personal promotion (including all the digital channels) is an important step to plan. Big data and AI can help analyze that and turn it into a well-honed and automated machine at the individual customer level.