A sales rep preparing for her week isn’t thinking: What’s in my sales dashboard? What is my progress towards my call plan? She’s thinking: What are the burning issues in my territory this week? How should I adjust my plan based on the latest events and information that I have? Then she manually navigates her way through dashboards and reports to find these answers.
This slow, manual process isn’t unique to reps. It also applies to HQ functions, and it will only get more difficult over time as new data become available. Accessing the right information at the right time and in the right way is critical to more effective decisions and efficient execution. User-centered design, technology and artificial intelligence can transform this experience with personalized analytics.
I like to contrast the rep reporting example with how I use Facebook. I don’t get reports that I need to do my job on Facebook, but it has become an aggregator of the news that I’m most interested in. I read The Economist and Wired for technology news, but when I have a few minutes in the evening to explore my personal interests, I don’t Google “soccer” or open The Economist website. I open Facebook, where I enjoy having all of these interests served up to me in one source. Each post gives me a short summary that allows me to judge in seconds if I’m interested enough to read more or whether I should move on to the next post. Facebook’s algorithms learn which posts I interact with more, and then adjust what I see to continually personalize my news feed.
For some time now, we’ve been discussing the concept of personalized analytics, which involves bringing some of the characteristics of consumer applications like Facebook or Netflix into our work lives so that we consume information relevant to our jobs in a new way. Insights and recommendations from data should come to us the way that my Facebook feed populates itself with posts about soccer, and they should embed seamlessly into our work lives, rather than sitting inside mountains of spreadsheets that we have to dig through.
Several weeks ago, I spoke at eyeforpharma and introduced this concept to the audience. I shared a video that demonstrates how this concept would work in real life so that the audience could see how changing the way we consume information at work will impact brand managers, sales leaders and reps. We’ve been working on this concept for some time, so I was eager to hear how an educated audience of pharmaceutical professionals would respond. Here are my three takeaways:
- It’s a provocative idea that generates excitement. I was struck both by how taken with the concept everyone seemed, and the many comments and questions that arose. The presentation turned into an open discussion, and I left these conversations firmly believing that some form of personalized analytics will become the industry standard at some point. It’s been interesting to introduce a solution that, in some respects, is made of components that already exist, yet at the same time represents a revolutionary leap forward for data consumption thanks to a seamless and integrated experience.
- Pharma is ready for AI and understands it. Even though artificial intelligence’s professional adoption is in its early stages, the potential impact of using AI to help commercial pharma teams become more effective and efficient was not lost on my audience. However, there were some savvy questions about how personalized analytics will be able to offer its suggestions and share only relevant data because of how it learns from user inputs.
For example, one attendee pointed out that because AI is only as effective as its inputs, he was concerned that complex and nuanced feedback would not be understood by the system and it therefore would provide overly simplistic and less valuable insights.
The reality is that, in order to generate trust in personalized analytics, results must be accurate from its introduction, so inputs and insights necessarily will be narrow at first to ensure accuracy and build credibility. Then, if you can leverage that trust to continue to increase the diversity of content that users bring into the system, that will begin to broaden insights. You also will continue to need humans for some of the more nuanced questions.
Another attendee was concerned with how a pharma organization would launch a solution like this: How can those first suggestions be accurate when you only have input from that first handful of adopters? This problem can be solved by first launching with an early experience team or a team of volunteers to help train these tools before a wider release to the organization.
- Rep buy-in and adoption are top of mind. Even though personalized analytics will change the way that all commercial pharma teams consume information, attendees were particularly focused on rep adoption. One questioner was concerned with whether or not it made sense to give the field another tool when the current trend is to simplify field processes. Another asked if we had tested this concept directly with sales reps.
Fortunately, personalized analytics will represent a simplification, one that we have, indeed, field-tested with a working demo. The response from reps was overwhelmingly enthusiastic. In part, this is because personalized analytics will be as easy to adopt as social media or Amazon’s Alexa, and will reduce reps’ analytics burden and increase efficiency.
Whether we’re talking to sales reps, marketers or pharma executives, what I’ve so far found most encouraging is how ready pharma seems to explore this concept.