Traditionally, the pharma industry relied on its field sales teams and deep connections with the healthcare professionals (HCP) community to increase customer engagement. Although the COVID-19 pandemic led to an increase in digital interactions, it was not the only factor driving pharmaceutical companies to diversify their customer engagement strategies. On the one hand, HCPs have been increasing their digital awareness and willingness to adopt newer ways of absorbing information, while, on the other hand, marketing teams within pharma companies have been coming under increasing pressure to justify the spending on marketing efforts. However, the COVID-19 pandemic served as a significant catalyst. What was a gradual progression was given a massive shot in the arm.
Omni Channel Analytics in Pharma
The return of face-to-face meetings and conferences did not mean going back to the old ways of working. Instead, it meant additional touchpoints from a customer's (HCP) journey point of view. Combining information from the sales team with information accessed by HCPs has become necessary to ensure the organization's marketing efforts are targeted and reach the intended audience. This has propelled the growth of generated data, which has been made available, collated, and used effectively to generate insights.
As communication channels proliferated, marketing teams grappled with creating 360-degree views of the customer. Imagine a situation where the sales team regularly updates details of face-to-face meetings in your CRM, your marketing efforts in terms of campaigns run, and website information on customer visits are captured within your Martech solutions, and you are trying to bring these two sources of information together.
Wasted marketing efforts not just cost the enterprise money but a disappointed customer, which will not contribute to revenue growth.
This is where omnichannel analytics becomes a major aspect of an organization's marketing efforts. It helps improve customer experiences, personalization, customer satisfaction, and, above all, an ability to quantify these and find ways to improve upon what is being done. Focusing on lead generation and customer experience as a whole and across the customer value chain moves marketing's value from the top of the funnel to a more broad-based enabler of revenue outcomes.
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Omnichannel Analytics and Marketing in Pharma
The start of any omnichannel analytics initiative is data. However, enterprise data is often “dark data,” a mix of unstructured and semi-structured data that requires varying degrees of cleansing and wrangling. Ensuring data is complete in breadth and depth is an equally important part of the process. The first step is to ensure that all the data is in one place. Ensuring a robust data management system is almost table stakes in today’s world. The struggle is with ensuring data coverage. This is crucial as any analytics performed on half-baked data will result in insights that may not lead to correct conclusions.
The other big challenge for most pharma organizations is that data across marketing and sales is often siloed. Data ownership is often with the functions themselves, and gatekeepers of data often are bottlenecks to sharing and ensuring uniform availability of updated data. In addition, the problems caused by legacy systems and the ability to have complete and real-time data for one source of truth is often a challenge that needs added professional support.
Omnichannel Data Analytics and Marketing: Identifying the Gaps
Once the data challenges are addressed, the next challenge is analyzing the data for insights. In most organizations, we have seen that functional experts are not augmented with data and analytics capabilities.
One cannot truly expect a marketer or a salesperson to be delving into data to develop insights. Several organizations have tried to solve this by having a centralized data and analytics function. While this does perform better than having no analytics at all, the challenge is that a contextual layer is missing from the analysis. Any analysis without a business context layer would leave a gaping hole in the insights delivered. For insights to be truly useful, a complete data management process, a single source of truth, must have an overlay of analytics with a business context. Only then can the insights derived be applied across the enterprise for impact at scale.
Furthermore, Generative AI has entered the complex realm of data incompleteness and algorithmic model construction, creating a rush to embrace AI across all processes. The focus, however, should be on using AI effectively rather than using it as another tool in the system.
With technology commoditizing analytics and the cost of analysis falling, the ‘human in the loop,’ the contextual layer that ensures that all the predictions, recommendations, and simulations are providing value to the business, becomes even more crucial in this process. AI, by itself, is not going to solve all gaps in the process; using AI effectively will be the differentiator.
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Contextual understanding is essential for gaining valuable insights as it helps remove AI bias, ensure compliance, and determine relevant input data. It goes back to the earlier point of having a professional mix of both AI/analytics experts and domain experts working in tandem to achieve organizational goals.
Additionally, the pharmaceutical industry is highly regulated. Compliance standards like the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) limit what information can be shared and what channels can be used by pharmaceutical companies to share it with HCPs.
While implementing a process of omnichannel analytics, always start with the business problem, which in this case would be improving customer experience, increasing revenue, and optimizing marketing costs. Post narrowing down the outcome ensures data governance across sources and silos, including legacy systems, frequency, and types ensuring transparent and complete data.
Overlaying the analytics on top; embedding it into the business with contextual knowledge and driving outcomes is the final process step. It is not just AI; machine predictions still require human judgment. AI can analyze large volumes of multidimensional, real-time data to generate intelligent recommendations, but when you combine that with human judgment, business outcomes can truly be achieved.
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A leader in the healthcare domain, SG Analytics assists healthcare companies in leveraging the power of information. Contact us today if you are in search of efficient Healthcare solutions to make sound business decisions.
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