“Digital channels haven’t just changed for the pandemic. They’ve changed permanently.”
– Greg Carpenter, Sr. Director, US Transformation Lead for Sanofi General Medicines
After being thrown into the digital deep end, the life sciences industry has finally evolved to a fully mixed model of engagement. Now, it’s time for pharma to reimagine what a meaningful customer engagement looks like—and how it’s best delivered.
As a result, companies like Sanofi are finding new ways to equip their reps with the visibility and guidance they need to orchestrate a personalized HCP experience. At this year’s Veeva Summit Connect, Derek Choy joined Greg Carpenter to discuss learnings from Sanofi’s digital transformation journey and share insights from their experience supporting the field as part of an omnichannel engagement strategy.
Derek and Greg discuss:
- How to get started with simple yet critical changes, like providing the field with real-time visibility and coordination guidance
- The importance of using all sources of analytics to personalize the customer journey
- How organizations can leverage AI-driven optimization to support the field of the future, regardless of their role
- Change management lessons from the past year
So, hello everyone. Thank you for joining. I am Melanie Pollard. I am the director of product alliances at Veeva. And thank you very much for joining this session with Aktana and Sanofi, Outfitting the Field for a New Digital World. Greg Carpenter from Sanofi and Derek Choy from Aktana are our presenters today. Please don’t hesitate to use the Q&A at the bottom of the screen to enter your questions during the presentation. We’ll be able to answer all of those at the conclusion of the presentation, and we’ll answer those by calling on you to ask your question at the end, if we’re able to do that. So Derek, I’m going to hand it over to you to kick things off.
Awesome. Thank you so much, Melanie. Hi everyone. My name is Derek. I’m co-founder and president at Aktana, and it’s great to be here today with Greg Carpenter, who’s senior director of US commercial transformation with Sanofi General Medicines. He’s had a lot of different positions across commercial, medical and leadership roles. One of the hats that Greg’s recently been wearing has been commercial lead for the Aktana engagement at Sanofi in close collaboration with his field force automation and analytics colleagues. So we’re really fortunate to have him here today, and Greg and I are going to be sharing our perspective on how to better support the field as we transition to a new digital world.
I wanted to kick us off by just stepping back and looking at some of the trends that we’ve all been living for some years now as an industry. The steady increase in digital efforts, but still in a primarily rep-driven engagement model, the focus on improving personalization to increase physician engagement, the amount of investment that we will be making in cloud infrastructure, big data and analytics, and as a result, this need that we have now to leverage all channels and data and help decision-makers make sense of it all.
But there has been this reality over the previous couple of years before last year, that this change has been gradual because there’s been this desire to minimize disruption. As we all know, with COVID-19 last year, we saw a rapid acceleration affecting many of these efforts, with a lot of this change occurring immediately and causing a lot of disruption. So as we look back at last year, we saw digital channels quickly becoming pretty much the only way to reach physicians, and becoming critical, not just being nice to have for commercial success. As you can see in some of this data from Aktana’s North America customers, we see that the number of interactions per rep over the last year, and we see that every physician essentially became a no-see physician around Q1 or Q2 of last year, with suggestions for remote and virtual interactions replacing face-to-face ones. We also saw this dramatic increase in the use of non-personal interactions like rep-triggered emails and handoffs to marketing automation systems.
If we dig just a little bit deeper into data from one specific engagement, we can see that there’s been this huge acceleration in digital over the last year, with 28 more emails sent by reps to nine times more physicians, and a 36-times increase in open rates, which is a huge change. Greg, would you be able to share what this journey looked like at Sanofi?
Sure, Derek. Thanks. You just have to take that in for a minute, the amount increase that we saw in email and those digital channels, and just how fast it happened. I think, like in many pharma companies, we were already moving in this direction since the advent of the tablet about 10 years ago, and technology had just been creeping along. But with the advent of the pandemic, we were all thrown into the deep end of digital, just trying to figure all this stuff out really overnight. Just to support some of the numbers that you’ve shared, we saw an eightfold increase in the number of virtual meetings at a sevenfold increase in the number of emails being sent in just a matter of weeks to months. Some of the challenges that we had is that we just didn’t have enough digitally-fresh content. So we thought we had plenty of emails, and then we were quickly going through all that material at a pretty fast clip once the pandemic hit.
I think that was one of the biggest challenges for us, and I think now, where it’s left us is realizing that these digital channels, they haven’t just changed for the pandemic they’ve changed permanently, and that moving forward, we really have to re-imagine what the customer experience is going to look like, and as well, how we help to improve the rep productivity.
Yeah. I think that’s makes a lot of sense, Greg, and I think with this change in terms of how we’re engaging with physicians, but also now as face-to-face visits return as part of the mix, I think we can all agree that we’ve finally evolved to this truly mixed model of engagement with our customers, that includes this mix of face-to-face engagement, Veeva-engaged meetings, phone calls, rep-approved emails, headquarter emails, and the digital interactions. But the challenge I think that we also see is that this is not proactively managed, it’s just really easy for there to be challenges like uncoordinated overlaps, unintended relationship gaps, and potential digital fatigue, and these things don’t actually help the physician relationship that can actually negatively impact it.
In addition, we’re also seeing that now that we’re engaging through digital channels as part of the mix, these channels are inherently less personal. So personalization becomes even more important so that we can get that meaningful engagement, and being relevant is really key when doing that. So what we’re starting to see is that what we need is to figure out how to use each channel with the right message at the right time, based on that analytic-driven understanding of each physician’s context, so we can get some of these benefits like those seamless handoffs, the responsive engagement to what physicians want to engage with, and those timely followups. So we’re truly personalizing what the physician journey looks like. Greg, clearly Sanofi has dealt with some of these challenges, as you outlined, and accelerated during this digital period. Can you share some of your thoughts on how you did that?
Yeah, sure. I think as we were looking recently at the number of channels that we have to reach out, it’s a myriad of channels, which is available now to reach out to customers. I think one of the epiphanies that we had late last year was that when you look at all these channels and when it’s a multi-channel engagement, the customer is not in the middle, you just have all these channels that you’re firing at the customer, but they’re not linked together. It’s not an intelligent journey.
So we’ve really been making the move to omnichannel, where you have the HCP in the center, and where you’re not just peppering them with all these different channels one off, but that there’s an intelligent journey that you’re taking the customer on, based upon what they engage most to, and as you said, linking the really relevant content to that customer. In the past, it was always about the branded message, but now it’s about the right message at the right time to the right customer that’s going to be the most engaging to that customer.
So sometimes that might be branded content, sometimes it might be patient education materials, it might be copay information, but it’s no longer just white noise. You’re really trying to help your customer hear a story which is tailored to them. That is not possible to do without AI. So when we were ready to launch early, just before the pandemic hit, with Aktana, we had to make a quick decision about whether to launch or to hold. So looking back, launching definitely turned out to be the right decision because it really helped us make the most of our email, virtual and digital efforts, so they were more targeted during that incredibly chaotic time for the industry.
Moving forward, digital is absolutely going to continue to play a critical role in driving commercial operations, even as the in-person visits come back. I’ve seen recent research that says about 45% of HCPs will still prefer a virtual rep visit. I think that number’s probably going to land somewhere between a 70/30 to a 60/40 split, if I had to guess. Certainly, the face-to-face visit is still going to be important, but a virtual is going to open up other channels, and we know there’s those doctors that prefer virtual. I think we’ve realized that there’s definitely a subset of new doctors, who virtual is their preference and it makes sense. There’s the doctors coming out of medical school now who grew up playing video games, grew up texting. They’re incredibly comfortable with the technology, and this is going to be their preferred way to communicate moving forward, and we want to make sure that we’re really relevant, engaging, and that we really master this channel to make the most of it.
I think all of pharma, not just Sanofi is in the middle of a major evolution right now, and we really have to adapt or die. We really do as far as how we approach our customers. It’s just simply not feasible to try to be everything to every potential customer. You can’t be everywhere, it just dilutes down your efforts. But with Aktana, with these new technologies, we don’t need to be everywhere. Our efforts can be truly tailored to the customer and we can reach them on their terms when it’s most convenient to them, and with the content, as you said, that is most relevant to them.
Yeah. Greg, I think that’s really helpful color and really useful as we set up now, and we start thinking about how did we solve for this? And what are some of the learnings and insights that we have from our experience that we can share here today? We wanted to focus on three key things. Firstly, we wanted to share some thoughts on how you can get started with simple but critical things, like providing real-time visibility and coordination guidance. Secondly, we wanted to dive a little bit deeper into the importance of analytics and how we ensure we use all sources of it as we are going down this path. Then finally, how we use AI-driven optimization to support the field of the future, regardless of the type of role that they’re playing, because we know that there are different visions and different perspectives on the role that the rep will play, whether it be an orchestrator or potentially only being involved in execution.
As we dive into the first area, and then we’ll get into more discussion around this, the first thing we thought was important to recognize is that the role of the field is changing as they become now, one piece of this omni-channel engagement strategy. The reality is we need to support them better as they deal with new challenges, such as increased breadth and complexity, because often we’re asking to cover more physicians and accounts, and execute through these new channels that they’re less familiar with. But then also, there’s challenges of remaining aligned and prepared in this dynamic world when each of their interactions is becoming more critical to execute well, but at the same time, they need to make sure that it’s harder to execute well, because it requires more planning to be aware of what’s already occurred outside of their scope, what the best way to engage that physician is right now and get their attention and be relevant for them, but then also being aware of what’s planned for the future so they can coordinate their activity.
So what this is meaning is that we need to provide them with more real-time visibility as well as guidance on how to manage this complexity. One of the key lessons I think that we saw is that it is possible to get started with some key suggestions and insights, to help with awareness and coordination. Some relatively simple, but very impactful things like coordination of events and team activity, awareness of digital email and market activity, and that guidance on meeting strategic plans, especially when it comes to leveraging new channels. Greg, what did you see when you launched with this type of project at Sanofi?
Yeah. It was funny. I think when the rest of us heard about it, when most people don’t have a lot of experience with AI, their mind goes to Skynet, which was not a positive outcome for a society. There was just general, I think distrust with AI when the reps first heard about it, because they saw it as being Big Brother, and that it was going to be monitoring everything they did and tracking their efforts, and reporting back to management. So that was one of the biggest challenges I think that we had to overcome, was just doing education and helping people understand what this is and what it isn’t.
I think with any time that you communicate change within an organization, it can never be one and done. It’s not just one town hall and then you can check the box on it. You have to communicate tenfold more than you think you need to, and it needs to be personal communication. You need to get into small group sessions and really talk to people to help them understand exactly what this is and why we’re doing it, and then what the benefit is going to be to them. I think as we’ve put time in and we’ve had those conversations, we’ve definitely seen engagement improve, adoption of the technology improve, and people are starting to see the benefits of working with AI.
Certainly, when we first went to roll out Aktana, and I am guilty of this, I wanted to turn everything on. We saw your video about all the capabilities and I was like, “I want all of that for the sales force.” You guys were really great coaches because you guys were like, “No, you want to take a crawl, walk, run mentality. Yes, you can change technology really fast, but people can’t change that fast. People need time to adapt. They need time to learn.” So I think that was one of the most important decisions that we made early on was to move slowly with this and to allow people to adapt and to build trust, and that trust, I think is the key which has really helped us to carry this forward.
Thanks Greg. That makes a lot of sense. I think the other thing to note that we optimized for when we got started was really ensuring that we focused on standardization and planning to scale up front, because I think our experience has shown us, when we at Aktana look generally, that AI pilots often easy enough to get off the ground, but then to be able to scale and have that impact across brands and markets can then become challenging. So a lot of that approach has been to make sure we’re identifying those necessary and common data formats and integrations for the key use cases, and then standardizing those, avoiding the custom setup and rework, and then codifying that experience into libraries of templates that can be used as a starting points, like those you can see on the screen. I think that really can help accelerate both getting started and then starting to scale later on across brands, as well as across the organization.
Moving on to our second point, I think the way that we use data and analytics to understand each position’s context is also really critical so we can personalize that engagement strategy. We’ve been talking earlier about how important that personalization is and the more we use analytics and machine learning, we can use it to balance the expert-system type approach, which is things like rules, which are great for getting started and capturing best practices. But overtime, you do want advanced analytics and machine learning to play their role, so you can identify those compelling insights you might not be able to gleam otherwise, but also, so you can ensure scalability, so we don’t have to codify and maintain every single permutation of a strategy.
But as we think about accelerating this use of machine learning and analytics, one of the important things I think that we’ve learned is ensuring that we leverage all sources of analytics because different analytical components are required to personalize the physician journey, and some of those components are really well suited for at-scale productized machine learning models that can be leveraged across brands, then others are really well suited for deep-dive analytics performed by data science teams or specialty vendors. For example, we look at use cases like using machine learning to identify priority targets, for example, based on patient journeys. Those really do benefit, often from a deep understanding of your therapeutic area, making a good candidate to develop internally or with a specialty partner, and it’s also what’s likely to change by brand or market. Also, it’s likely to be that deep understanding of your customers that you find strategic and want to build differentiation.
But on the other hand, there’s AI that identifies channel affinity, ideal timing of engagements, that relies more on standard-interaction history data across channels and is more easily standardized across brands and markets, and these models, while they should be trained and deployed on your data to provide your teams with that unique insight, you are unlikely to need to differentiate and design the model itself. So those might be areas you may want to consider leveraging out-of-box offerings.
Finally, as we think about the last prong of what we need to do in analytics, determining the right content to personalize that engagement, we’ve been talking a lot about that, is really critical as well. It’s especially important, as we’ve said as we’re engaging through less personal channels, since analytics can play a really important role in helping here. Greg, how have you seen analytics be leveraged to personalize both activity and content at Sanofi?
It’s a really good question. We realized just the past year that a lot of our marketing was a one size fits all, but not all doctors are the same, so they have different evolving preferences for engagement, for content, different needs for their patients. We know that moving forward, there’s still going to be some HCPs who prefer face-to-face engagement on certain occasions, others are going to want virtual, and still others are going to want digital on-demand content for when they can watch it whenever they want, like after work, before work weekends, et cetera. The goal of all this content now is to really personalize it, and so that each HCP fuels like we’re speaking to them. You’re getting to that N equals one.
Asking a sales professional to figure out what all their different customers’ preferences would look like, it’s impossible. The human brain simply cannot conceptualize that. So luckily, with the work that we’ve been doing with Aktana, it has really set us up for success in this new digital world, and now the way that we promote to HCPs is with this true omni-channel approach. There’s been some great videos put out by Innovo and by Veeva in regards to omnichannel and some of the work that they’re doing. I think everybody’s on the same path of trying to use a modular content and to being able to tailor the content to the physicians, and then employ it in an omni-channel model, which takes customers on an intelligent journey.
Now, I’m just a commercial guy. I don’t have the analytics background of some of the people in our team that we work with. But one of the things that I’ve learned going through this journey is that there’s definitely different ways to set up an AI system. We already had a best-in-class, next-best-action engine that was helping to determine the appropriate non-personal promotional channels for our customers. We then added Aktana on, which has now helped manage the field component, and as well, provided a really powerful user interface that the reps are really comfortable with.
We’re now looking at further integrating with the likes of a Salesforce marketing cloud to help with the web traffic and to determine what’s the most appropriate content post a web visit. The beauty of working with Aktana through all of this is that you guys have been able to help guide the integration of all these pieces, and it really has been seamless. For something so complex, it’s gone really well this past year, and as well as during the middle of a pandemic. So it’s incredible how much we’ve been able to transform from a digital standpoint. While I think a lot of other companies may have been almost a paralyzed, we were fortunate to be able to really make some good advances in the past year.
I think another big epiphany for us was that if you think of the AI as an engine, then the content is really the fuel that makes it go. So to be able to have AI determine what’s the most engaging content to a customer, you have to have enough content to allow the AB testing to be able to determine what the customer best responds to you, like what’s the most engaging to them. So the only way to generate enough fresh compelling material is to use modular content, so this led us on an evolution with our MLR review to help set it up, so it was able to handle the volume required and so we had enough fuel.
Now, we have a great MLR team at Sanofi. They’re true partners, we work really well together. But like everyone else, we’ve had to find the best way to use core claims to ensure that we can maintain compliant review while also be as efficient as possible. The only way this works is to be able to set up a core claims library. You can work with Veeva to do that. So you’re not reviewing the same claims over and over again, which is just a huge time and a resource burden to a team. So those are some of the things in mind.
Yeah, that makes a lot of sense. It makes a lot of sense, Greg. I think just to build on your point about content, from an analytic perspective, one of the other challenges we’ve seen is as you move beyond those traditional brand topics, and as you’re developing content faster, as well as adapting it for use across different channels, the analytics to identify that content that’s most likely for any physician to engage with is more difficult to do at scale, because you need to be able to take that content, you need to know what content is the same across channels, and you may want to find some identifiers of content similarity, which is not as simple, it’s just topic. So this is where some of the analytic approaches using things like NLP and text analytics, where you do need to include life-science-specific ontology, and also be able to automate model building can help us do this at scale, as well as making sure you’re still finding that relevant and meaningful content that you can use across channels.
I want to move us on to our final area, which is once you have that feel for the engine, specifically the data and the content, ultimately our goal is to be able to support the field as they play an increasingly strategic role as part of the journey. This means helping the field understand what actions make the most sense from a cost-benefit trade-off perspective, and where they should spend their increasingly precious time. So I think this is where support from AI can be critical because it can help make sure that… It’s really hard for a rep to be able to do this themselves. So if we were looking at just one physician at a time, like looking at an example on the screen now, as humans, we might be able to identify the right action by considering some of these things, like channel preferences impacting that channel or likelihood for a customer to engage.
In this example, it might lead us to logically prioritize, let’s say a face-to-face visit for physician A. But as soon as we start needing to make trade-offs between multiple physicians, we might need to weigh the value of an action against its cost or other constraints to determine the best next step at scale. So in this example, we might then have to trade off a visit for a remote call for physician A because there’s only time for one visit, and then maybe it’s ROI maximizing to be able to use that visit for physician B instead. This sort of calculation, you can start to see is difficult for us to do, even if it’s just two physicians, let alone hundreds, which makes it a very valuable use case for us to use AI-driven optimization.
Here we’re not even covering other really important factors that need to play into it, like looking at each channel’s limitation, we need to consider the physician’s bandwidth to avoid information overload. We need to consider content, as we were saying, how much content is there, and how much have we used, as well as things like in virtual visits, how many can rep reasonably accomplished in a period of time?
So as we start to recognize and accept that this sort of AI-driven optimization is critical to identify the right actions to take for a physician, one of the questions that still comes up and that remains something people talk about now is how we choose to use this output as well as what scope of agency do we give to the rep? So will we be using the AI-driven optimization to only engage the rep when they need to execute their specific action? Or will we choose to give that rep and make the rep an orchestrator for their channels, or maybe all channels, and provide them with the analytic outputs so they can make decisions which are guided by the analytics, but also leverage their own insight as human beings and to the physician relationship and as well as what they see? Greg, do you have thoughts on this?
I do. So I was not on the high school debate team, but I’ll just share a few thoughts on this. It’s not too much of a debate. I think right now, the rep is still the orchestrator, but with that said, we’re in the middle of this evolution, moving to artificial intelligence. Having spent a lot of time in the field, it’s funny. I’ll give you one example. I was with a rep, this was years ago, and I asked why we’re going to this one office, and she’s like, “Oh, it’s Sandy’s birthday, so I wanted to drop off a card.” So there’s always that human element, and that was a great answer because she was doing some relationship building. But humans don’t think like AI. Humans think, “I want to go here, I want to go there,” and there’s not always a concrete, strategic reason why reps are taking the actions that they take on a given day, and there’s a multitude of variables that you mentioned that the human brain is not capable of calculating.
So right now, Sanofi General Medicines has phenomenal sales teams, and they’re definitely the orchestrator today, as I mentioned. But I think, just like in any industry, we know that AI is going to continue to evolve. It’s going to become more mainstream, we’re already seeing this happen. And we know that it takes time for humans to adapt, but they do, it just takes time. It always feels a little bit overwhelming at first, and then it becomes second nature. I think back to the example of the self-checkout lanes and grocery stores. When those first showed up, everybody was avoiding them, nobody wanted to use them, and they were like, “I want a human helping me buy my groceries.” But now we see more and more self-checkout stands being added, you see more people using those than going to the checker. Now we see Amazon making this move to stores where there’s no checkout. So that is going to evolve just like AI is.
The point is that reps are getting more comfortable with artificial intelligence, but you have to start the journey somewhere, and if you haven’t started yet, you’re already behind because it takes time to do this, and you can’t just flip the switch and do it overnight. To be fair, the AI system is still evolving, we’ve had some challenges along the way. I think with the quality of the suggestions that the engine was first putting out, that was impacting some of the reps credibility in the engine, and we had to go back and remind them that this is a machine learning. So the AI has to learn, and that just because you reject a recommendation for the engine does not mean that that’s bad. It’s great that you’re engaging with it and you’re helping the engine learn. As you do that, and as you invest the time and as we incorporate additional data streams, those recommendations are going to get stronger and stronger and stronger, and you’re going to see the more benefit from it.
The example that I often use is that if you were playing poker on your own versus being able to legally have AI help you play poker, who wouldn’t take the AI help, if it’ll help you beat the odds? It’s the same thing here as selling. There’s so many things to keep track of, there’s so much complexity to these customer relationships. Who wouldn’t want an advantage if you can get it?