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January 28, 2016 0

At this year’s CES conference, MC10’s two showcased products were nothing short of groundbreaking. By debuting two different wearables, one aimed to satisfy the medical community while the other targeting the general consumer, the company succeeded in sparking interest. The first product, known as BioStamp Research Connect, is designed to assist researchers with a multitude of health-related deficiencies. Inside the band-aid sized wearable contains both a gyroscope and an accelerometer, which are aimed towards tracking electrical activity, motor skills, and movement. To top it off, this miniature device can perform a real-time ECG. Moving on to their consumer oriented product, MC10 has partnered with L’Oréal to create My UV Patch. Designed to monitor sun exposure, this stretchy sticker syncs with smartphones to review results, and even includes helpful tips about healthy tanning.

Only time will tell how these two new innovations will fair in the 2016 market. To learn more, click here to read about it in Tech Times by Horia Ungureanu.

Lily Stauffer


January 27, 2016 0

By Faruk Abdullah of Applied Predictive Technologies

Abdullah-Jan2015artworkPharmaceutical marketing is changing. With increased industry spend, new marketing channels, a more informed consumer, and a shifting healthcare landscape, it is clear that marketers need to be smarter than ever with their investment decisions. It is also clear that DTC marketing is no longer just about pumping money into national TV ad buys (though they continue to receive substantial investment). It’s now also about targeted messaging. It’s about digital. It’s about patient engagement. In this environment, it is more complex than ever to understand how to get the right message in front of the right consumer at the right time. Marketers will need to innovate.

As organizations try new marketing strategies, they will inevitably realize that not all of their ideas will achieve the desired outcome. In fact, it is incredibly risky to dive head-first into new ideas without empirically validating them first.

The question is how can marketers get the most accurate, data-driven recommendations about which actions work with each customer segment? And, how can they do this without first risking significant budget or opportunity cost of not rolling out the most effective ideas sooner? The traditional method of allocating marketing spend has been to use promotion response models, which rely on historical data to attribute the impact of a given marketing action (e.g., print ads) on KPIs (e.g., NRx). Promotion response models are important tools for the industry and can serve as great sources of hypotheses about what the impact of a given action might be. However, traditional regression-based approaches are unable to uncover what would have happened if the action had not been taken.

It is time for life sciences marketers to move towards a process that leading retailers, restaurants, manufacturers, and banks have been driving for 15 years: rapidly testing their ideas to figure out what works and where they work best before making significant investments. As pioneers of controlled experiments for clinical trials, life sciences companies know that test versus control experimentation is the gold standard in analytics. Trying an initiative with a test group (e.g., in just some markets or with some physicians) and comparing those results with a highly similar control group (not experiencing a change) is the only way to truly understand the incremental effectiveness of each investment. Leading commercial organizations are now also beginning to use this experimental methodology to optimize their marketing and sales programs.

Experimentation is most valuable where the outcome of the experiment will directly impact a decision and create a new learning for the organization. With the consumerization of healthcare, consolidating health systems, new competitors, and a shifting reimbursement paradigm, the outcome of a new marketing action today is highly uncertain. We suggest that organizations should test all shifts in marketing strategy before making any changes.

There are three critical reasons why marketers should begin to incorporate credible, empirical data into their marketing decision-making process.

  1. Correlation is not causation. Promotion response models seek to identify relationships between marketing actions and KPIs. With rich data and complex equations, it’s easy to conclude that this approach leads to optimal recommendations. Unfortunately, because promotion response models are generally not based on test vs. control analytics, they are unable to isolate cause-and-effect relationships between changing a given marketing lever (e.g., increasing digital spend) and a change in KPIs (e.g., TRx).
  1. The future is different than the past. Companies construct promotion response models on the basis that historical relationships between marketing actions and changes in KPIs will continue to hold true in the future. As fast as healthcare is changing today, relying on data from the past to make decisions about a volatile future is a mistake.
  2. It can’t be modeled if it hasn’t been done. Promotion response models rely on measuring the relationship between past marketing investments and KPIs. To state the obvious, there is no real world data from an action that has never been put in market, and thus no way to build a model on that action. For these situations, marketers may rely on market research or analysis of similar campaigns before rolling out a new tactic. However, these approaches may not generate the most actionable, accurate insights. Alternatively, by embracing rapid, statistically credible, field experiments, marketers can know with confidence what will happen before they take the risk of rolling it out more broadly.

In today’s changing healthcare and marketing environment, it is critical that marketers truly know which ideas work and which do not. Organizations should rapidly tests their ideas, discard the unsuccessful ones, and understand how to refine their strategies to dial up ROI.

Faruk Abdullah


November 19, 2015 0

Stop the coughing before it becomes a social media plague.

By Peter Friedman of LiveWorld

Friedman-Nov2015-artworkIt’s taken a while, but the pharmaceutical industry has slowly realized the value of social media to reach caregivers, health care professionals, and patients to raise awareness and even track adverse events. According to the IMS Institute for Healthcare Informatics, only half of the 50 largest pharmaceutical companies worldwide use social media, and only 10 are on the Big Three: Facebook, Twitter, and YouTube.[1]

But patients, caregivers, and healthcare professionals are, and they’re posting about everything from what they need to, questions and adverse events. Placeholder pages aren’t going to cut it in this world where those affected are quick to complain and slow to retract. For pharmaceutical companies, that means taking cautious steps when social media becomes a petri dish for a full-blown PR crisis. As a regulated industry, pharmaceuticals especially need to be prepared for a possible social media crisis. Here are a few precautions pharmaceuticals should take.

  1. Re-evaluate – or even stop – scheduled posts.

Schedules are great when everything is going smoothly. But during a crisis, what may seem perfectly innocuous in any other context could further ruffle feathers. In crisis management mode, stopping all scheduled posts, on every channel, can help you refocus the message, as can pulling the plug on paid social promotions. This is the time to make sure your message is being seen, undiluted.

Only after you’ve got a handle on the crisis can you resume social media scheduling, but you’ll want to make sure anything being posted is unlikely to throw gas on smoking embers (and when dealing with humans, they are easily reignited). And certainly watch any new posts until you’re completely sure the crisis is over, even involving the legal team as necessary to ensure new posts don’t create additional liability.

  1. Take a time-out.

While a time-out won’t work in the OR, it can work wonders in the midst of a crisis. When you need to buy some time to work on the company’s response, use a “pause post.” Basically, the pause post will be tailored to the situation and the person and make it clear that you hear and acknowledge the issue, you’re working on a response, and you’ll have an update at [insert date and time here] on [your site].

While this can be difficult if discontent has spread virally over social media, it’s still worth it – just don’t post the same response to everyone.

  1. Pick your battles – and your battlefield.

Sometimes, just one person is stirring the pot, and there’s a way to nip it in the bud: get that person on the phone, preferably employing the most compassionate person you have on staff (if the person who calls the irate party is rude and argumentative, the situation will deteriorate very quickly). Listen to that angry person; just simply ask what happened. Take notes, recap your understanding to that person, and then make sure you’re following through. Address the issue and provide the person with a resolution, not just an apology.

But if the problem has spread, you’ll have to herd the cats, so to speak, and get everyone on the same platform to open up a dialogue. The company blog is an ideal place to get everyone off social media and into a single spot. It’s easy to share with a link and allows for a longer response.

  1. Write a well-thought out response.

In pharmaceuticals, there’s no easy way to respond to a crisis. But as with other brands, there are ways to successfully respond and diffuse a bad situation.

First, answer the question head-on and own the problem. Acknowledge that there’s a problem; avoid “we’re sorry you feel that way” at all costs. Then, unless there is a critical factual error, don’t start explaining. Just acknowledge, take responsibility, and apologize. Acknowledge the experience, and keep the brand human – convey humility in your response, and make sure it reads like a human wrote it. Get the resident Devil’s advocate on your team to read the response and ask for the snarkiest reply possible – that may transform the message altogether.

And this can’t be said enough: make sure you run the response through legal, indicating that this is a rush job, to avoid any liability in the future.

  1. Stay on the ball – don’t drop it.

Managing the crisis doesn’t stop at posting a response. It’s important to follow up, monitoring social media channels and answering questions as they arise – and they will arise. Make sure that the link to the central discussion is easily accessible as well.

In pharma, it’s also very likely that the media will jump on a crisis, and the PR and legal teams will be key in ensuring the right message gets to the press. Keep track of industry articles, bloggers, and more, and make sure that if an outlet publishes incorrect coverage, someone is able to reach out to provide correct information. As bloggers post, reach out directly to them to thank them.

Ultimately, the most important thing is to let the public know that the company is learning from the experience. It will turn the crisis from “evil pharmaceutical company” to “compassionate pharmaceutical company” much faster.

Reference:

[1] http://www.fiercepharmamarketing.com/special-reports/top-10-pharma-companies-social-media

 

About the Author

We hope you enjoyed this adapted excerpt from The CMO’s Social Media Handbook: A Step-by-Step Guide for Leading Marketing Teams in the Social Media World, by Peter Friedman, CEO and Chairman of LiveWorld. To read more, download a free PDF version, or buy the hardback or ebook via online booksellers. Connect with him @PeterFriedman.

 

Peter Friedman


February 18, 2015 0

Tenuta and Gallagher artwork - DTC_programatic_futureThe experience of encountering advertising tailored to one’s behaviors or interests on the internet has become ubiquitous in a very short time. We’ve all had that experience – shopping for a particular shoe on Zappos or gadget on Amazon, not buying it, then having an ad for that shoe or that gadget magically appear in a whole variety of other websites during the course of our browsing over a period of days or even weeks. Or, perhaps, buying that shoe or gadget, and then encountering ads for similar shoes or gadgets, or shoe/gadget accessories.

It isn’t magic, of course – it’s programmatic buying, bringing together technology and data to serve media to specific audiences by using exact or inferred behaviors. The reason it has become so prevalent so quickly is because it works. Programmatic buying offers consumer marketers of all stripes the opportunity to narrow their audience focus, increase the efficiency of their campaigns, and optimize their campaigns; rather than scattering the seeds of a campaign the old fashioned way, those seeds can be planted only in what has proven to be the most receptive earth, thereby optimizing the campaign, saving marketing dollars, and increasing the potential return of the dollars that do get spent.

Unfortunately, those of us in healthcare have largely missed out on this thrilling media revolution. We’ve missed out because we are stuck behind a privacy barrier that strictly limits what we can learn about the medical history of any consumers we might want to reach through media. In other categories like CPG, finance, and travel, advertisers can use actual purchase behavior and sales data to identify and target more qualified audiences. Purchases can be tracked and used to inform the media that is served to an individual in the future. But this type of precision-based, one-to-one audience targeting is not permitted or possible in health care; the data is unavailable for marketing purposes due to HIPAA regulations, which protect patient privacy and prevent the abuse of sensitive, potentially identifiable medical data.

But a pathway exists around this obstacle, and that pathway is called predictive targeting. By using tools that are already at hand, plus some cutting-edge mathematics, we can identify an audience’s predictive health behaviors by connecting other more commonly used, non-health related consumer data variables – demo, geo, media consumption, lifestyle, et cetera – to health behavior data. Once the correlations between the consumer data variables and health behavior data are found, we can then segment audiences according to their respective propensity – or likelihood – to treat within a condition or on a brand – as opposed to their actual treatment behavior. This exceeds the demands of HIPAA – since there is no way to connect actual, identifiable health data to a specific individual – and represents a privacy-compliant way to target audiences more efficiently.

So how does predictive targeting work, more specifically? Crossix Solutions, a healthcare data analytics provider, connects its patient-level healthcare data – past treatment, physician visits, brand conversions, adherence, and the like – for millions of individuals through its proprietary network of data tracked by pharmacies, payers, and other entities that play roles along the transactional chain. And data analytics providers, including Crossix, also have access to more traditional, consolidated consumer data – demographics, household income ranges, spending within specific categories, interests, media and shopping habits, online behavior. By studying these two data sets in concert – tying patient healthcare data to consumer data, all behind firewalls that keep individual identities private – correlations can be determined between them. The output of this data modeling process is a propensity score algorithm – a formula that translates all of those correlated consumer variables into a probability of treatment for a particular condition or on a specific drug brand.

Putting it into action

What makes this so empowering for the pharmaceutical brand manager is how it mitigates the privacy issue from the targeting equation. The initial development of a propensity score algorithm happens behind secure firewalls, so the marketer will never actually see any of that individualized healthcare data. And once a propensity score algorithm is developed, marketers can use it to target media to audiences based solely on the correlated consumer data variables – demographics, interests, shopping habits, the lot – still not knowing a thing about the target’s treatment history, prescription purchase activity, or anything else that’s HIPAA-protected. We can use what we are permitted to know to infer what we aren’t, and infer it with a great deal of empirical evidence.

For example – based on its analysis of the relationship between consumer and healthcare data, a company like Crossix might find that women who are married with three children, have college degrees, spend time on Facebook, shop for athletic wear, have a household income of about $100,000, like to travel domestically, and use the internet frequently have the highest correlation with household treatment of ADHD. And beyond that highly specific peak correlation, a propensity score algorithm can segment or rank audiences based on their relative propensity or likelihood to perform a specific health-related action. So for a particular branded ad campaign, if the total universe available to serve digital media is, say, 50 million consumers, a propensity score algorithm can determine which of those 50 million exhibit the combination of correlated/weighted variables with the highest propensities for the behavior in question. It may, for instance, find that only 12 million among those 50 million are the real target. Thus, DTC advertisers can design media buys in a more granular, evidence-based fashion, leading to greatly enhanced campaign efficiency and effectiveness, while reducing media waste.

 

Intouch Solutions and Crossix recently employed the predictive targeting model with a top-ten pharma client’s brand, in a target disease state with about 200,000 patients in the United States. We developed propensity score algorithms as described above, tying various consumer data points to health behaviors. Then we used those algorithms as the basis for audience-targeted online media buying. And we optimized the campaign using those algorithms daily. In doing this we demonstrated that audience-based media buying can be more effective and cost-efficient than contextual/content-based media buying.

Did it work? We used Crossix’s health data to measure campaign performance at the script level – Crossix analyses de-identified data from actual prescription transactions and determined how many individuals exposed to the ad visited the doctor or began treatment with the client’s brand as a result of their ad exposure. As the campaign test ran, we discovered that physician visits of people exposed to the audience-targeted campaign components vs people exposed to the contextual/content-focused components were nearly three times higher, and the estimated cost per patient start was about one-twentieth as much for the audience-targeted components as it was for the contextual parts of the buy.

So yes – it worked. In fact, these experiments in predictive targeting have shown such promise that the tool has rapidly become a part of Intouch’s standard media conversation, and these pilots have now become the norm. And while a conversation about “individualized healthcare data” clearly piques the interest of clients’ regulatory teams, once we explain the process of developing predictive algorithms and prove the strong separation between identifiable healthcare data and actual targeting activities, Intouch has seen no resistance.

 

All this is not to say that the age of traditional endemic or contextual media buying is over. Predictive targeting will not replace those tools any time soon – patients will always go to contextual locations, so it’d be silly to abandon them altogether. But predictive targeting does offer healthcare marketers a whole new way to plan and optimize their media buying, a way that is both data-driven and data-proven. Plenty of ink has been spilled over the past year or two about so-called “big data” and how it might impact the business of healthcare marketing. But predictive targeting is not a “maybe” proposition. It’s a real tool that brands can use today to more accurately find their intended audiences and serve them the most relevant media, based on those patients’ statistically established propensities for performing the behaviors the media is designed to encourage. The tale of “big data” in healthcare marketing may largely remain to be written – but predictive targeting is already an exciting part of this evolving story.

 

About the Authors:

Angela Tenuta headshot  As Executive Vice President, Angela Tenuta leads client services for Intouch Solutions, a digital-centric marketing agency focused on the pharmaceutical industry. With 18 years’ experience in pharmaceutical marketing, Angela is driven by the prospect of creating programs that inspire meaningful connections between pharma, patients and HCPs. Since joining Intouch in 2006, Angela has led teams through many pharma digital “firsts” including the first pharma e-CRM campaign, first pharma Yahoo! homepage takeover, the first pharma CPA campaign, and the first digital sales aid. Prior to joining Intouch, Angela rose through the ranks of Draftfcb, spending nine years in client service roles there. Connect with her on LinkedIn, email her at angela.tenuta@intouchsol.com, or call her at (312) 540-6905.

 

Shannon Gallagher headshotShannon Gallagher serves as Vice President, Analytics Services at Crossix Solutions, where she leads the ongoing expansion of Crossix services and capabilities at the intersection of pharmaceutical and consumer healthcare. A veteran consultant in market research and data analytics for the pharmaceutical, healthcare and CPG sectors, Shannon is passionate about Crossix’s unique position to harness Big Data to empower better communication to the patient as a consumer. Prior to joining Crossix, Shannon spent 10 years working at Nielsen in Innovation Analytics, consulting on new product development for Rx and OTC/CPG manufacturers. Connect with her on LinkedIn, email her at shannon.gallagher@crossix.com, or call her at (212) 994-9367.

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