In a world full of options, companies need to compete on every prospect and existing customer. As the number of potential channels to reach customers increases, companies need to build strategies for interacting with the right customers, through the right channels, at the right time and with the right messages. Omnichannel marketing has emerged as an effective and efficient means for optimizing this customer engagement. However, while omnichannel is far from being simple in any industry, it raises unique challenges in the pharmaceutical industry. Some of these challenges stem from the nature of sales and marketing in pharma, where others stem from data limitations, process characteristics and regulations.
Pharma companies are not different in their need to sell. These companies spend a decade and a couple of billions of dollars developing a drug, and when it gets approved, the companies seek revenue. However, there are many characteristics that make pharma more complex than other industries.
First, pharma companies need to consider their unique customer decision-making process. The three main groups of stakeholders that might be considered as customers: the patients who consume the drug, their physicians who prescribe the drug, and the insurance company that pays for the drug. Each type of stakeholder has a different impact on the decision-making process. The decision-making process itself is long and subject to the cadence at which the patient sees the physician, which makes it fuzzy to attribute prescription events to sales campaigns.
Secondly, and in contrast to many other industries, the size of the target population in pharma may often become smaller even as the customer value is high, especially in precision medications and others related to orphan and rare diseases. Dealing with these small populations poses challenges for basing decisions and other inferences on data, as sample sizes are by nature limited in value and application. Third, the pharma industry is strongly affected by regulation, making the creation of new content for promotion long and expensive. If that is not enough, the data pharma companies rely on for commercial processes has its own complicated supply chain. This data comes from different sources, often contains discrepancies, arrives with delays, and usually reflects merely partial coverage of the real world.
Some pharma companies, mainly the big ones, try to use widely-available cloud platforms in order to build tailored data processes to support omnichannel marketing and sales. Although cloud platforms suggest huge arsenals of data processing tools, including a wide range of AI algorithms, they are based on relatively low-level capabilities, in order to provide a solution for every possible industry. Many of these pharma companies fail to grasp all the complications that are involved in building an omnichannel solution: ingestions of data sources, curation of data and inference to cover gaps, data integration, maintenance of a data foundation, design of a business process, AI modeling (and there are many models involved in each use case), productization and monitoring of these models, list creation, performance evaluation and many more.
Each of these pieces is complex in itself and is affected by the specific data sources (and the vendors that supply them), the brand, the channels that the brand uses for marketing (which tend to change rapidly) and other considerations. Companies that take this approach tend to quickly discover that the total cost of owning a full omnichannel solution is enormous; much higher than they could have ever expected. Moreover, even with huge investments, these companies do not always achieve good omnichannel results.
Instead of this reinventing-the-wheel approach, we recommend that pharma companies turn to the capabilities of vertical platforms that focus solely on pharma and commercial optimization. The set of capabilities in these platforms is not generic, and can therefore focus on the specific needs of the vertical – pharma commercial optimization. Omnichannel is part of this commercial optimization. A platform for pharma commercial optimization will contain capabilities around data sources that are specific to pharma, including CRM, claims data, special pharmacy and supply chain data. Such platforms often have prebuilt business logic for data transformation, an optimized data foundation, a smaller set of the relevant AI practices that are suitable to the relevant use cases, proper workflows for maintaining the data foundation and the creation of lists, messages and channel recommendations, automatic evaluation capabilities and more.
With a vertical platform, pharma companies can use industry best practices, and focus their efforts on highlighting their clinical uniqueness, rather than on technicalities. The outcomes of a vertical platform approach tend to be better in two different dimensions: better omnichannel performance at a lower cost of ownership. An additional added value of vertical platforms is that they cover multiple business processes. For example, by contracting with a vendor of a commercial optimization platform, the pharma company may promote not only its omnichannel process but also sales optimization, better IC goal setting, dynamic targeting, patient finding, and more.
About Shahar Cohen
Shahar is Verix’s CTO, leading the innovation behind the Tovanatm platform. Shahar is a Data science researcher with over 20 years of experience in starting and leading complex data ventures. He co-founded three AI / Machine learning startups: Optimove, YellowRoad and start-up.ai, and worked as a consultant and hands-on service provider with more than 50 organizations, in a variety of domains: NLP, Reinforcement Learning, Deep Learning, Classic Supervised & Unsupervised Learning and in many verticals.
