How to avoid becoming a ‘failed launch’ business case study

Pharma news is filled with failed launch stories. Why? Because companies failed to thoroughly analyze what drives greater value to their customers pre-launch and then, once the product launched, failed to address the resulting problems.


 

 

I discussed this recently in a newsletter article: A good example of a bad launch is Zaltrap® (ziv-aflibercept), a colon cancer drug that Sanofi launched in 2012. It had the astounding (but not unusual for cancer biologics) price tag of $9,600 per month. Its closest competitor was Avastin® (bevacizumab), which was just as effective -selling for about $4,583 per month. A few months after launch, Zaltrap was in the news after a group of Oncologists at New York’s Memorial Sloan-Kettering Cancer Center publically refused to prescribe the drug because of its cost. Sanofi eventually slashed the drug’s price by half, but the damage was already done. The public was left with the perception that Sanofi tried to get rich on the back of dying cancer patients. Clearly, the company had not used data and research to determine its pricing strategy.

 

Sanofi also had challenges when it launched its anti-arrhythmia drug in 2009, Multaq (dronedarone). Investment bank Morgan Stanley predicted that Multaq could garner sales of €3 billion a year. That was high, but even Citigroup predicted to have sales between $1.3 and $1.9 billion. It was not to be. As it turns out, the drug was linked with a higher risk of liver, cardiovascular and lung disease, which then created concerns about using it as a first-line therapy. In France it was set a low reimbursement rate and restrictions on use were recommended. In addition, the US Food and Drug Administration warned against prescribing it for patients with permanent atrial fibrillation (the drug is approved for AF patients in sinus rhythm with a history of paroxysmal or persistent AF). By the end of 2012, sales stagnated at $570 million according to Reuters, far short of the billions the company had banked on.

 

Sanofi is not the only company to crash and burn at launch. Consider AstraZeneca and its oral anticoagulant, Brilinta® (ticagrelor). Analysts predicted sales of $2.7 billion by 2015. However, it was not to be as the FDA required more data analysis and concerns about the drug began to emerge. By the time they eventually won FDA approval in July 2011, many analysts had cut expectations, although a few still had blockbuster predictions. However, they had already lost 6 months and had limited time to make an impact. They needed a strong launch hitting all relevant driver messages and driver promotional channels, and strong analytics should have been conducted to show the teams how to do this. However, this was not done effectively at the time, and although during third quarter 2011 they managed to create $15 million in sales, in quarter 4 of 2011 they dropped back to $5m in sales. Fortunately they did improve in 2012 and steadily moved up to $9m in Q1, $18m in Q2 and $24m in Q3, but then began fighting generic versions of Plavix. The launch was not well executed and Berstein analyst, Tim Anderson, stated that it was one of the most disappointing new drug launches in the industry.

 

In all cases they failed because they failed to use analytics to fully understand how to capture the market effectively.

 

 

Analytics is the biggest game-changing opportunity for marketing and sales since the Internet. That statement often prompts vigorous head nodding from executives but is quickly followed by head scratching. “How can we make this happen?” The companies that succeed in their marketing today incorporate the following six principles with their analytics:

 

    1.    Start with a specific problem and stay focused. For example, your market share is flat or in decline and a competitor is beating you. Use analytics to understand where the competition has the strongest edge, and how and where they are vulnerable. What about pre-launch planning, where you need to ensure that your brand has the optimal messages and channels to accelerate uptake upon launch? Analytics is the perfect tool to examine these kinds of issues.



 

    2.    Start with strong data. Data is the foundation of everything. If you don’t use the best data, you will not have an accurate solution. Historical data or analogues are highly unreliable in the dynamic Pharmaceutical market, with most ROI, promotional response, and econometric predictive models failing to consider the current market environment (i.e. customer perceptions, customer sentiment, customer dynamics, customer, brand equity) and market data.



 

    3.    Understand the customer. Data must come from your customers. Without understanding what is driving them, you will not be able to impact your results. The trick, however, is to ensure that you get customer insights but don’t not rely on them as fact, since (as Malcom Gladwell wrote in his best-selling book Blink) people rarely know what truly influences them. Always keep in mind the adage attributed to Henry Ford: “If I asked people what they wanted, they would have said faster horses.” That means understanding the customer through data and math. Simply put, the industry needs to follow Eularis and move from the simple analytics techniques that have been used for decades, which are simply hypothesis-testing approaches, to include more complex advanced analytics, which are hypothesis-generating approaches.



 

    4.    Focus on the most valuable opportunities. There will always be many things you can change to impact sales and market share, but do you have the budget for all of them? No. So stay focused on the changes that provide the fastest results, and use analytics to help you identify what they are.



 

    5.    Create easy-to-understand data. It’s wonderful to have great data, but if it’s difficult to understand and use, it’s worthless. You need a program with a simple interface that doesn’t require an advanced degree in statistics to understand. An executive from one of the top five Pharmaceutical companies described how he spent two years working on the company’s internal analytics approach but nobody understood it, so were not using it. Instead, the people providing the analytics need to make the results understandable to people who do not have PhDs in math so they can understand how to use it effectively.