Vijay Raghavan is an Associate Vice President, Market Analytics and Business Insights at AbbVie. He was previously Head of Marketing Analytics for Allergan and was Program Committee Lead and Membership Chair for the Pharmaceutical Management Science Association. He is focused on measuring ROI of various marketing campaigns, optimizing spend and utilizing machine and deep learning for the commercial organization. He has an MBA from Columbia Business School and MS in engineering from Cornell University.
Vijay talks about the importance of speed and data analytics in a world of exponential data growth and how life sciences organizations should look at the commercial team as a revenue generator, not a cost center. It’s important to note the views and opinions expressed here are Vijay’s own and do not reflect those of AbbVie.
A: My main goal is to be able to extract and utilize analytics to address business problems and uncover trends to drive business revenue. We use analytics to diagnose problems as they come up and then help find ways to address them. That means accessing different data sources and analyzing trends that can help lead to solutions.
A: It could be a number of things. For example, we might be monitoring the performance of a brand that is in decline or one that is experiencing significant growth. We would then take a deep dive into the data to uncover the causes of the decline or growth. We can then use the results of that analysis to take appropriate action to help halt the decline – starting a new marketing campaign for example. In the case of the growing brand, we can use the results to determine what we are doing well there and apply it to other brands.
A: Speed is very important. We look at trends on a weekly basis, and then analyze what we are finding very quickly so we act on them. To do that, you need the right tools and techniques. We need to develop results and actionable insights within a week or two so we can quickly pass the information on to the marketing and sales teams.
A: The business will often bring them to us since they are in regular communication with customers. Sometimes we will find issues on our own that we think require more in-depth analysis. For example, we use IHD to identify newly diagnosed patients suffering from a particular disease, and we can easily uncover if there is a downward trend in that patient population. No one else might be aware of that trend, but we uncover it during our regular monitoring. We would then inform the business and undertake some deeper analysis of the issue.
A: The most important aspect of analytics is identifying issues early enough so that the business can quickly take appropriate corrective action. Leveraging analytics is the best way to ensure that the organization can drive revenue to remain profitable. It’s all about increasing sales in an ethical, compliant way.
A: There is no specific breakdown between the two. You may identify certain trends that will help the organization deal with a problem they are facing right now. There are other times when the business will ask us to do an analysis for a future initiative like growing a business in a certain geography or for a certain set of doctors. Whether we focus on the present or future depends on the particular circumstance and needs of the business. Once we start on an analysis, regardless of the initial goal, we go where the data takes us and develop our results accordingly.
A: We may uncover some unexpected insights that we present to the business that they may disagree with or may not be able to leverage at that time. It all depends on the current business strategy. They key is that it isn’t a matter of judgement or whether the insight is right or wrong, it just might not be the right time to utilize it.
A: IHD has been critical in helping us achieve greater speed in our analysis. It lets us act faster without having to get programmers involved. It makes it simple for non-programmers to conduct analyses much easier than with other traditional tools.
A: Expanding analytics beyond programmers allows us to get a different set of people looking at the data. This gives us a perspective we might not get and may uncover different insights from the data. That can become a real advantage for life sciences commercial teams.
A: Integrating all these new data sources can unlock more insights but processing it all becomes more complex. You need faster machines and more people who can conduct the analyses. Deep Learning will be the paradigm of the future, and a tool that minimizes the need for programming and enables more time on advanced algorithms, which will be very important.
A: I believe organizations should think about their analytics team more as a revenue generator versus a cost center which is how some companies look at it. Our goal as an analytics team is to help the company generate revenue. We analyze the data to look for opportunities and offer suggestions on how they can best be leveraged. To accomplish that, you need an analytics team that not only has the technical analytics skills, but also an understanding of how the business works. That enables the analytics team to think like business leaders and helps them look at the data in a different way – a way that can truly drive revenue and make the company stronger.
A: It doesn’t happen overnight. You have to build relationships between the analysts and business leaders, work together to identify opportunities and repeat the process. As you build trust and the business starts to see the value you are bringing to the table, they begin to recognize you as more than just a cost center. That leads to more targeted requests for information and a focus on things that are relevant to the business – this is what moves the needle.
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