Case Study:
Thorough Pre-Launch Planning and Launch Preparation Using AI Creates Winning Launch
Thorough Pre-Launch Planning and Launch Preparation Using AI Creates Winning Launch
Avoiding others’ mistakes in launch and having through pre-launch planning with AI results in a winning launch.
The Client Problem
The brand team decided to utilize AI in the years pre-launch to understand the environment and strategic options available in order to be prepared for a rapid launch and rapid uptake in a competitive market. They also wanted to learn from some of the big launch failures (e.g. Sanofi’s Zaltrap and Multaq; AstraZeneca’s Brilinta; Dendreon’s Provenge) so as to learn lessons from these and avoid making the same mistakes.
The Solution
The team turned to Eularis for a thorough Artificial Intelligence Powered approach to prepare for launch. Included in these analyses were patient flow analyses from claims data, overall market landscape, market drivers, competitor analysis, customer pain points (both patient and physician), opportunity space identification, pricing optimization, market access analysis for faster reimbursement, segmentation and targeting, optimal positioning and messaging, and optimal channel allocation. During the pre-launch years, the team prepared the market maximizing awareness of the clinical trial results and the advantages of the drug, to ensure high anticipation amongst their target customers. They then did something that drives success when implemented well – they associated their strengths with patient outcomes and forced the market to play their game rather than the reverse. This has been done well over the years with many brands (e.g. Detrol in overactive bladder – a term which did not exist till the Pfizer marketing team came up with the term to focus on their strength and yet is now in medical textbooks; Xarelto in NOACs ((Novel Oral Anticoagulants) in which they focused on the fact that they had 6 indications instead of clinical superiority and that won them the game). They also examined what potential counter launches would be employed by competitors to anticipate and prepare positioning to ensure that these would not entrench the brand negatively.Then they used AI to identify which sub populations of patients would be more likely to receive stronger clinical results from the treatment, which prescribers would be more receptive to prescribing the drug which led into precision targeting of physicians for the reps, and sales call customization by individual prescriber.
The Outcome
The drug was first approved in the U.S. and is on track to achieve sales of close to $1 Bn by the end of its’ first year on the market. The initial market growth has been driven by its precision targeting to identify which physicians will be most likely to switch to the drug rather than more traditional targeting that tends to focus on high decile prescribers, and precision messaging by physician. They also utilized online tools to identify the influencer networks (and not just traditional KOLs) to influence the influencers. In addition to that, the next best action modelling is allowing the digital channels employed to learn from all data from all customer interactions to be able to serve up the next best content for that individual to move them through the customer journey faster, as well as lead them to the next digital channel that the individual should visit (and with personalized content for them in that channel) to ensure their customer experience and engagement is heightened while leading them effectively through their customer journey to the brand. The results speak for themselves in the sales figures seen in the figure seen to date.
To achieve these kinds of results, contact Eularis today.
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