Next-Best Omnichannel Programs Are Transforming Sales, Marketing Teams

The pharma industry is seeing an increased use of rules-based algorithms and insights driven by artificial intelligence/machine learning (AI/ML), which are transforming sales and marketing. Speakers at a recent webinar discussed how companies can implement such approaches and how that information can be useful through “next-best” programs.

Companies “can drive omnichannel differentiation by using smart technology to combine engagement with analytics,” maintained Antonio Pregueiro, vice president of new offerings at IQVIA, during the webinar, titled “Next Best” Brings Intelligence to the Point of Execution. “For those of us who have been reps, who’ve worked with reps or MSLs [i.e., medical science liaisons] or anybody who’s customer-facing in the field, the distinctive feature of these teams is precisely because they’re masters of the art of conversation. They know how to speak, they know when to listen to a customer, they know when to stop to make sure they don’t overburden a customer, they make sure to follow up, and they are experts at building relationships over time.”

According to Pregueiro, “if you bring into this the science to actually increase this engagement so that you can more accurately predict who, when and where you should be speaking to and what is the right information that they need to listen to and what should be the content of the message they are receiving from you, whatever channel you’re using, and making sure that things like follow-ups can be automatically scheduled and that you can track the reaction to content — if you bring that scientific analysis, and you merge it with that personal attention that teams can deliver for customers, when you bring all of that together, that’s really where next best sits. For us, that’s what a next-best program is.”

A next-best solution, he explained, can provide sales reps with information such as how much drug a provider has prescribed or whether they have shown that “they’re interested in a product because they’ve attended an event or because they’ve already gotten in touch with the company. You can basically do the same sort of analysis, the same sort of preparation that a rep would normally do, and this can be automated to be delivered to the rep as a recommendation as the next-best customer to see, for example.”

With content delivered through an array of channels, a next-best program “can easily track whether the doctor has been seen by a person or how many people in the company they’ve spoken to, whether a doctor has searched for certain content online, whether they’ve gone to a website, whether they’ve actually already sent a request to a medical person, to an MSL,” he said. “By knowing how they’ve interacted with the company resources, then a next-best program can also deliver a suggestion on how you should be engaging with this customer next.” As the program learns, it can predict “not just what you should do tomorrow but also what kind of engagement you should have next week, what kind of topics should you bring because of the previous conversations that have happened, and you can even have notifications to other parts of the company, other team members, to follow up.” This collaboration across channels and team members is a key component of these solutions, he stated.

The programs can track if a provider downloads content on a product about to launch and notify team members, said Pregueiro. “This could be a search or something else, something that can be captured digitally that is then sent to the KAM [i.e., key account manager] as a notification saying, ‘Hey, this doctor should be prioritized.’ So as the usual effort that would be focusing on just total prescriptions potential, etc. is still a valid point, is still a very valid approach to prioritizing visits to a customer, this additional piece of information may actually suggest to a KAM, ‘You should see this customer now, not next week, not two weeks, because right now they are interested, and you’ll be probably much more welcome to come in and talk about your product because they just downloaded the content that’s relevant to the product.’”

In a situation where a drugmaker has a new publication, this “could also trigger a suggestion to send an email, and this email can be tracked, so again the KAM could get another suggestion that they follow up on the email if” a provider fails to respond to it after a rep visit. Another use includes requesting “a medical person to go deep into some subjects and this can easily be automated within a next-best program.

“And if we start seeing requests around information, and if you start looking through several customers, maybe even across territories that have similar questions, a next-best program can very clearly and very quickly identify needs that could be shared across a group of customers suggesting a program that can really help a multitude of individuals,” he continued. This, in turn, “can very quickly lead to identifying areas where the brand team or even the local team should be investing in, such as organizing events,” which can “be used as yet another input to suggest next-best recommendations to reps, who are then going to be inviting people to attend such events.…If you just think of it from a purely omnichannel approach, you can see how next best can support the teams in the field in doing their job and in coordinating their efforts with those of other teams and of other channels.”

Knowing what potential customers are interested in benefits not only the company but also those customers, maintained Pregueiro. Having that intelligence “means that you are able to engage with a customer at the right time to bring up that content. It also means that the time to coordinate, the time spent on planning, on verifying that something has been followed up on or even the time that it takes to plan the next move, all of this can be taken away from the minds of the individuals because by automating all of this, you can actually speed up the reaction; you can speed up the delivery of content that doesn’t always have to be sent through an individual. It can be delivered through something online, through a digital channel.” This also helps to personalize the approach, he said. And with the program triggering follow-ups, this takes the pressure off the customers to do so.

“We have certainly seen this in the success of some of our next-best programs,” he stated. “In early implementations, from, say, 2017 or 2018, we started to see a relative increase in the number of customers that teams could interact with” of about 5%. “But we’ve actually seen that now going as much as 25% of additional customers — not just because the reps are seeing customers that are more relevant but because also the customers themselves have other touchpoints that are not just driven through the field teams.

“This obviously also translates to a big impact on prescriptions,” he continued. “You can look for an impact in the short term of 1% to 4%, [but in more sophisticated programs], we’ve seen the impact be as high as 19%. And finally, one of the relatively early measurements is really in channel adoption. If you’re trying to get your teams to start engaging using new digital channels, a next-best program is a fantastic way, and we’ve seen upwards of 40%, sometimes three times the increase off baseline in channel adoption when you’re trying to get teams to start using email or trying to promote using certain channels.”

While these programs are not necessarily new, the COVID-19 pandemic seems to have increased their use. “In the last few months, and certainly in the last year, I’m sure a lot of us have seen all kinds of news around how the COVID-19 pandemic has done a lot to disrupt how we engage in the pharma industry and how it’s driven a big shift toward remote [and] toward omnichannel engagement” and away from face-to-face interactions.

In August and September last year, IQVIA conducted a survey on the pharma industry’s digital engagement that included 27 life science companies: nine of the top 20 firms, nine small/mid-size and nine emerging.

“What we saw is that a lot of the historical investment and focus on customer engagement has been on deploying or improving CRM [i.e., customer relationship management solutions], but in reality, we should start to move past that. We also saw that there’s been quite a bit of focus on data management, and that is still important,” he asserted.

The company learned that “there’s some big, important focus, maybe more than there’s been in the past, around understanding customer preferences and around understanding the analysis that needs to happen around the customers interacting with the organization across different channels. We also saw some major shifts in priorities or at least a major desire to shift priorities to train people on how to use new tools, to make sure that there’s a capability to produce content in a modular fashion that can be used across channels. And we also started to hear more about the fact that different tools needed to be integrated and that maybe we needed to start using artificial intelligence, machine learning or just other smart algorithms because obviously the complexity is increasing, and the more you integrate, the more that becomes obvious.”

At IQVIA, “we still see omnichannel engagement as something that’s going to remain very much central to the customer engagement model in the industry.” A subsequent survey the company conducted earlier this year on the future of consumer engagement revealed that respondents also felt that a multichannel approach to engagement is the most important capability that a company can have. “The key here [is] interacting because a lot of remote channels are really around just delivering content to physicians in a convenient way, which is fine, but getting that interaction, meaning being able to also learn from that and how they react, that’s a big part of this omnichannel set of capabilities.

And we also started to see that things around analysis and around artificial intelligence, these topics, these concerns actually became much more important.” He noted that many companies now have data science teams to help with that aspect.

He shared data from the first two weeks of April compared with data from the year-ago period on the use of face-to-face vs. remote engagement with health care professionals across a handful of countries. In countries that reopened earlier than others after COVID-related shutdowns, such as ones in Asia, “we’re starting to see that the face-to-face engagement is actually growing relative to the period last year.” In Western Hemisphere countries, “you’re starting to see that even though there’s a reduction in the face-to-face channel and a growth in the remote engagement, you’re starting to see that actually that decline in the face to face is much smaller.”

“This engagement model is going to be different” in the future, he said. “We have now gone through the highly disruptive COVID pandemic, and, again, I’m not saying that it’s all resolved, that it’s all great and we’re going to have no issues from now on, but what we’re starting to see is that with the vaccines being deployed, with some more information being in the minds of people, we are starting to think about what the future is going to look like and what is it that we need to do in terms of redeploying and reorganizing our customer model to make sure that we can move forward with an even better normal, maybe an even better way of engaging with health care professionals in general.”

What that engagement looks like may differ depending on the country and therapeutic area, he added. For example, in highly restricted markets where patients still are not visiting providers, “and maybe health care professionals don’t have the time or don’t want to permit access to pharma companies, and you may have to think about ‘Well, then, I really need to understand patients better or even engage with patients if that’s allowed or predict what patients are going to show up so that I can also get an idea of where I’m deploying my overall engagement efforts.”

Or in a “much more open market…[with] some residual inertia,…you may still need to think about engaging thought leaders, engaging in other ways instead of making it clear to patients that they should start seeing physicians, making it clear to physicians that you are here with great therapeutic options and that the potential undertreated patients should be starting to come back to physicians and you need to be ready to do that.”

Next-best programs, he contended, can help differentiate companies’ approach to provider engagement. “The complexity of managing information around multiple channels and the complexity of trying to bring all that data, all that information, all that capability, to make sure that you deliver a truly stronger engagement requires much more than the clunky analysis, the ad hoc analysis that we’ve done in the past to plan for execution or to measure the impact of that execution. We really need to make sure that this becomes live, and that’s why we think next-best programs are so important.”

A IQVIA analysis of the top 20 U.S. pharma companies showed that “whereas a lot of the top pharma companies have already started or implemented field-force rules-based type of next-best programs, far fewer have actually done this using AI/ML,” he explained. “And a few of them have not yet done this for the non-field force, i.e., for the more digital channels in terms of implementing next-best programs there. And again, you’ll see that it’s a relatively small set of companies where we see a complete omnichannel program that’s using next-best type of programs to coordinate not just personal but also nonpersonal or digital.”

To implement these programs, “what we feel is that next best is at the center of connecting commercial planning, resource optimization, engagement and analytics,” explained Aleksandra Ilic, vice president of customer solutions at IQVIA. What we think about this is that next best can be started in isolation, you can try a pilot program,…but at the end of the day, you’ll look at this in a more holistic manner.”

The vision and success factors of such a program, she maintained, are that “it has to be about connecting different insights across the overall ecosystem.…The point of this at the end of the day is surfacing the insights in the right place at the right time. And by implementing more of a modular program, you’re going to see how you can fit it in your existing ecosystem.” She recommended looking at “open architecture to enable things you’ve already started or things you’re planning to implement.”

“There are various types of inputs that can be leveraged for next-best solutions,” Ilic said, including health care professional targets, prescription data, customer engagements, channel data, resource allocation, customer preference and key opinion leader insights. Once companies have that data, they can consider ways to “define formula groups and parameters. You want to have the ability to specify meaningful messages” for teams, particularly the field force. “Equally, you do not want to bombard your field force with numerous messages where everything becomes just a white noise.” To do this, companies need to specify what notifications are a priority.

One challenge to supporting customer precision “at the end of the day has to do with the data itself being connected and visualized through the right model,” she stated. In addition, “are you able to enrich different segments and apply all the personalization” from customer profiles? And “how are you truly using the preferences and engagement data to drive different predictive models?”

To support personalized engagement, how the journeys are designed, content reach is tracked and experience across channels is connected are typical challenges. And in performance management, “how are you truly able to measure channel effectiveness, any type of behavioral change, and how do you advance these predictive algorithms?”

According to Ilic, there are four basic reasons these programs fail:

(1) “The end user not adopting the suggestions being sent to them.”

(2) “Not being able to better support user adoption.”

(3) Technical issues around fitting the architecture into the existing ecosystem.

(4) “Solutions are not working well commercially.”

Aspects of these programs to ensure success include “achiev[ing] business objectives,” launching such a program quickly, the proper technology solution and organizational setup and management. Platforms need to be “flexible and easily integrated,” have a “solution stack that allows for full data management capabilities and analytics.…We also feel like the productization of the machine-learning features and templates is something that’s quite important for that real-time delivery of insights,” said Ilic. In addition, “the ability to be agile is very important,” as is a solution that is innovative, supports collaboration and is outcomes-driven.

IQVIA, Ilic said, recommends that “programs should start small and scale from there.” Companies should trust program outcomes “through platform transparency.” Start with simple AI/ML before moving to more complex approaches. And “ultimately, you do want to get to the point of self-serve so that you can manage your costs.”

Contact Ilic at Aleksandra.ilic@iqvia.com and Pregueiro at Antonio.pregueiro@iqvia.com.

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