However, in the panel, the majority reporting still targeting based on decile usage (heavy therapy category use) and physician type. On the face of it, they seem like a perfect target. But are they? Does it really make sense to assume that just because the physician prescribes a lot of drugs in your Rx area, and is a non-user or low user of your specific brand, that they will be a perfect target for your sales force and respond how you want them to i.e. increase their prescribing of your brand? On the surface, yes, the reasoning sounds logical…. but let’s examine this a little more deeply.
These physicians prescribe a lot of the category, and you want your share of it. However, some of these will never prescribe your brand so much money is being wasted on these targets that will never prescribe your brand. Clearly, volume-based targeting and segmentation does not take into account a whole host of other types of key differences in Physicians that can often make a large difference to your results.
So how can we avoid spending time and money on the ones that will never prescribe your brand no matter what you do?
The consensus from the panel was that they needed to understand more about which physicians would be the most likely to increase prescribing from influence from all channels, as well identify those who can be influenced to increase in prescribing from sales rep activity. Sales reps play an integral role in helping physicians understand and choose brands. However, they are not the only influence and the other influencers such as online interactions, social media interactions, peer group discussions and others must be included and understood in terms of how they influence an individual physician’s decision process, brand retention, and loyalty in order to be able to target most effectively.
Big data allows us to now combine numerous data sources on the same physicians, handling infinite attributes, factoring in objectives, and ensuring that the segmentation and targeting approach is weighted to achieve the objectives. By taking as much of a 360-degree view of your physicians as you can by combining all the data you can access on them (internal and external). Using all the data, you can create far stronger informed decisions on who can be influenced by a sales rep, and who cannot – ever. This is next generation targeting.
Today artificial intelligence techniques can be used to drive far more useful and relevant customer segmentation that allows you to predict which individual customer can be influenced – and how to achieve that influence by customer – and which are not worth spending valuable time or resources on.
This is not theoretical but reality today. Big data and AI are making mass personalization a reality in pharma targeting. Using AI algorithms, your systems can learn from all data sources and individual behaviors and tailor who we target, and how we interact with the individual targets, and when we interact in which channels, to tailor the experience for them unlike anything we have ever done previously.
By doing this, you are then able to learn from every individual physician interaction, personalize every customer interaction to lead to great engagement and results, and provide a single connected experience to take your customers through their customer journey making the most of both rational and emotional dimensions to achieve the outcomes.
This is key for companies to improve their targeting as well as the relevance of the content and channel of their communications for impactful real world results. You can then combine the data to get individual personalization to know which physicians specifically should be targeted for results.
This would be a far more efficient use of your ever-shrinking budget. Consider ways you could do this for your brand. With a strong segmentation and targeting analytics you can identify who can be influenced, how and when to tie your sales force actions into real world results.
Conclusion
Big data analytics is a powerful tool for ensuring that your targets are optimal and profitable, and yet it appears to be rarely used for targeting while companies continue to rely on simply targeting all high decile or high category prescribing doctors.
Keep in mind that utilizing analytics for targeting should not be a one-time thing but a living, dynamic analysis taking account of what is happening in real time with the doctors and results. The market conditions change constantly and your targets may change their alliances, depending on what is happening in the marketplace, because it is a dynamic and changing environment which can alter depending on adverse events, reimbursement changes, new codes and more.
With big data and AI powered analytics and planning, the power of targeting relevantly can be maximized even with a shrinking budget. If you are not getting this kind of power, you are not using analytics properly or, at least, you are not using the right analytics.
For more information on this topic, please contact the author Dr Bates at Eularis: https://www.eularis.com
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To learn more about how Eularis can help you find the best solutions to the challenges faced by healthcare teams, please drop us a note or email the author at abates@eularis.com.