Apr9

Painting by Numbers: Using Enterprise-wide Analytics to Guide Marketing Strategy

Posted by Christina Connor from Ogilvy Healthworld - New York, North America

By Christina Connor from Ogilvy Healthworld - New York, North America | Published: April 9, 2013

Paint Brush Man_Thumbnail

Today, analytics is playing an ever-expanding role in developing marketing strategy as clients have become aware of the importance of data in guiding campaign decisions. Like the Paint by Numbers crafts of our childhood, marketers can use stats and data points to guide the creative marketing process, resulting in a more predictable end-product.

However, according to a recent Accenture study, the same brand managers who understand the importance of analytics tend to rely on gut instinct instead of logical recommendations, preferring to paint outside the lines rather than follow the data.

The reason for this departure is that analytics is still typically brought in on a project-by-project basis.  While a team of expert analysts may provide insight on one particular channel or campaign, they do not have a panoramic view of the overarching strategy or history of the brand. The brand manager’s gut, on the other hand, has years of experience in overall performance, customer relations, and sales. This often results in a strategy that conforms to a “more of the same” mentality.

Within the Ogilvy Healthworld Marketing Analytics & Consulting team, we have found that insights and recommendations  can be more impactful when done at a portfolio or enterprise level. For example, we had been performing analytics for a particular brand, observing that over time, the paid search costs were increasing for unbranded terms. Over the next year, we grew our analytics practice across this pharma company’s entire therapeutic department. When the data started to pour in, our analysis uncovered that two brands were competing against each another for the same unbranded terms, artificially driving the costs up. By taking an above-brand look at the data, we were able to identify an issue and resolve it in a way that benefited both brands and became a best practice across the therapeutic area.

While this example looks at a very specific issue, enterprise analytics has a number of strategic benefits:

  • First, it sets a tone of accountability across brands, defining a standard of data quality that can be enforced in measurement and optimization. Not only does this ensure that different groups are using the same metrics to define success, but also it makes it easier to compare performance across brands and categories. Over time, this organized collection of data can serve as a starting point for performing more advanced modeling and predictive analytics.
  • Second, strategic data collection and analyses can offer dramatic cost-savings as each brand does not have to finance its own projects and can anticipate a more organized array of reporting and analysis options.
  • Third, enterprise-wide analytics enables across-the-board education in reporting and data. At this time, many brand managers are in the habit of glancing at a report for information, but not yet using it as a compass by which to navigate. With an enterprise-wide analytics presence, these brand managers are forced to embrace the numbers and start making more strategic brand recommendations. The result is a more consistent and strategic step forward in marketing growth and optimization.

Ultimately, analytics and reliance on predictive modeling are here to stay. As marketing partners, it is our responsibility to make our clients as agile as possible and ensure they have the most accurate information in their toolbox while making decisions. So the next time your clients go to the easel and begin painting their strategic plan, make sure they have numbers to guide their work of art.

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Jul24

Why Is It So Hard to Learn From Somebody Else’s Mistake?

Posted by Donna Ambriano from OCH Medical Marketing - NJ, North America

By Donna Ambriano from OCH Medical Marketing - NJ, North America | Published: July 24, 2012

Recently, I was fortunate to have the opportunity to drive to Key West from southern Florida. (I know, I know, it sucks to be me. If you’ve done it before, you know what I’m talking about. If you haven’t, I highly recommend it). It’s an absolutely spectacular drive along Route 1, with crystal blue water as far as the eye can see on either side of a long, straight, wide-open road. Really long. Really straight. And really wide open. With a heavy foot on the accelerator and some decent horsepower, one could really cut some time off this trip. Or at least pass that annoying RV piled high with bicycles, kayaks, and fishing gear, going a grinding 40 mph, and towing a rusting, mustard yellow 1994 Pontiac Aztec.

That was probably the same thought that flashed across the minds of many of my fellow drivers…right before they became names on the roadside memorial signs that dot both sides of the Overseas Highway from Homestead to the Southernmost Point.

I’ve been driving to the Keys at least once a year for 20 years. And every time, there’s a big sign with flashing lights that announces how many fatalities to date have been reported. And every time, it’s in the double digits (13 in 2012 at the time of my trip, in case your inquiring mind wants to know). So why do we never learn, even when the evidence is right in front of our eyes?

I started wondering about this as we meandered along behind the RV under cloudless blue skies, taking in the stunning vistas of mangroves, watching the boats from far away and the pelicans from closer up, and counting the small, white signs that represented the site of gruesome tragedies. At least 39 people in addition to the aforementioned 13 had ignored the history of the unfortunate drivers before them—and that was just between Key Largo and Key West.

Never mind the cause of their crashes (yes, the majority are alcohol-related). How about, “Those who don’t learn from history are doomed to repeat it”? The sad signs were there. Obviously. How could somebody miss them?

Maybe the answer lies in the dual concepts of “proximity” and “causality.” The ability to learn from our own mistakes is globally recognized: we make a mistake, and the result affects us directly as we are very close to it (“proximity”). There is a direct connection between our action (or inaction) and the consequence (“causality”). This is borne out at a neurological level and can be documented via electroencephalography. Within 50 milliseconds of a screw-up, your brain involuntarily sends out an initial reaction called error-related negativity (ERN) involving the anterior cingulated cortex. This part of the brain monitors behavior, anticipation, reward response, and regulates attention, helping you recognize that an error has occurred. (Also known as the “uh oh.”) The second signal, called error positivity (Pe), shows up 100 to 500 milliseconds later. This signal shows that you are aware of and paying attention to your mistake and its results (informally, the “you IDIOT” response). Numerous studies have shown that we learn more effectively when the ERN signal is larger, suggesting a bigger initial response to error, and the Pe signal is more consistent, demonstrating we are paying attention to, and thus trying to learn from, the mistake.1

 Our ability to learn from others is a little more complex. A 2011 Scientific American article shows that people can learn from other people in a competitive situation—but more from their competitors’ failures (what not to do) than their successes. In an experiment, volunteers played a simple game, modeled after foraging for resources in the wild, against a computer. While the computer was making its move (which simply consisted of changing the color of a box), the live player’s mirror neuron system (a system known to respond to the actions of others) was engaged as if the player him/herself was making the same choice. If the computer’s choice failed, the mirror neuron system of the live player immediately shut down the mental simulation—in other words, the live player’s brain learned from the computer’s mistake so he/she would not make the same error.2 Why? Proximity and causality: the live player was directly involved with the computer player, and the decisions the computer player made directly influenced the decisions and actions of the live player. The relationship between the person and the computer came down to learning “what’s in it for me” by seeing what failure looked like, and acting on that knowledge to achieve success.

So back to those unfortunate accident victims: Shouldn’t they have learned from the examples of their fellow fatalities? I would think that living to see another day would be a pretty strong motivator to trigger the “what’s in it for me” learning response, wouldn’t you? Again, this is all about proximity and causality. If you don’t see the accident, or the fates of the victims don’t impact your life directly, your neurons won’t react the same way. You may feel distressed or sad about the people behind the memorial plaques, but you have no direct experience of their failure and therefore no context for how you could learn from their mistakes. On the other hand, if your brain is sending out ERN signals while you’re behind the wheel, you’re probably already involved in something awful…and hopefully, will have the opportunity to experience the Pe response and learn how not to repeat the mistake in the future. Unlike the unlucky 13.

 

 

  1. Lehrer, J. Why do some people learn faster? Wired. 2011. Available at: http://www.wired.com/wiredscience/2011/10/why-do-some-people-learn-faster-2/. Accessed June 19, 2012.
  2. Swaminathan S. Monkey see, monkey don’t: learning from others’ mistakes. Scientific American. 2011. Available at: http://www.scientificamerican.com/article.cfm?id=monkey-see-monkey-dont. Accessed June 19, 2012.

 

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Jul17

HCPs Who Access Data: Just Like the Rest of Us!

Posted by Mary Anderson from Ogilvy CommonHealth Med Ed and SCI Scientific Communications & Information-NJ, North America

By Mary Anderson from Ogilvy CommonHealth Med Ed and SCI Scientific Communications & Information-NJ, North America | Published: July 17, 2012

Guess what! Scientists, clinicians and other healthcare professionals own and use smartphones, iPads and an array of desktop and personal computers. These same people are fundamentally interested in the clinical studies and scientific evidence that result from research studies. They read specialty and  peer-reviewed journals and are asking, “When will I be able to read more via my personal devices?” As lay consumers, they can access everything from instructions on how to build a nuclear bomb to the recipe for Uzbeki-style lamb via their digital devices, yet the journal articles that satisfy their professional needs and passions are not yet uniformly available. Go figure!

SCI Scientific Communications & Information recently utilized a three-wave electronic survey to understand just how eager clinicians, journal authors and industry stakeholders are to receive data in a digital format. The results are in line with society at large. They want more!

Data collected from 50 internal medicine and primary care practitioners showed 86% accessed peer-reviewed literature from 2010 to 2011, and the overall proportion of information accessed with these modalities increased from 52.2% to 64.6%. Mobile tablets showed the highest percentage increases.  Preliminary results from 15 authors who published more than four articles over the last three years show that they decreased their print-only submissions to 15.3%, from 25% of the submissions two years ago.

While computers and laptops remain the primary devices for accessing online peer-reviewed content, HCPs say they will want and expect that journal articles become available for e-readers and smartphone applications. These devices are likely to outpace PCs/laptops based on portability and convenience.  Industry stakeholders anticipate a rise in open access and non-print options. They aim to please as long as regulatory and compliance agents within their organizations get on board and clarify the rules around more novel dissemination approaches, such as podcasts. In the meantime, they support open access publications and utilize QR coding at congresses to disseminate posters and presentations.

Like all other consumers, HCP readers perceive that technology will make their access to information more timely, cost-effective and convenient. They want to see e-mail notifications of new articles, smartphone applications that work for middle-aged sets of eyes and tablet applications.

Summary excerpted:

Hudson C,  Cecere E, Yalamanchili R, Anderson M, Pucci M, Aloia D, Scheckner B. Utilization and attitudes on technological advances in medical publications. Podium presentation, ISMPP, 2012.  

 

 

 

 

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Jul5

Will Health Plans Of The Future Take Care Of Widows And Orphans?

Posted by Stephen Lubiak from OCH Payer Marketing- NJ, North America

By Stephen Lubiak from OCH Payer Marketing- NJ, North America | Published: July 5, 2012

“Widows and orphans” is a long-established phrase that connotes one of the neediest segments of societies. Throughout time, communities have been asked (or commanded, as in the Bible) to support them in some way. One modern-day version of this support takes the form of estate planning. In the 20th century, stocks that provided a relatively high degree of safety (from declines in price) and steady dividends were nicknamed “widows and orphans” because they were good to have in the portfolio and provided relatively steady income. Prevalent among this type of stock were utilities. Utilities offered consistent returns because state or federal governments had established these companies as monopolies. In return for their monopoly status, governments regulated (or seen another way, guaranteed) a specific level of profits after fixed and variable costs were covered.

In 2012, a new group of companies that may fulfill the role of utilities is health plans. The Affordable Care Act fixes the medical loss ratio (MLR) of health plans at 80%, or 85% for large health plans. Here, the medical loss ratio is a metric that means 85% of health plan revenues must be spent on patient care. You don’t need to be a mathematician to figure out that 15% of revenues is left for overhead expenses and profits. So, the level of profits is regulated, just like those of utilities.

The business leaders of health plans are not settling for lower profits, which are estimated to fall from the 7-8% range to 3-5%. Health plans are already diversifying and are either acquiring or developing higher margin businesses. Here are a few examples:

Information technology (IT) or information management (IM) is a popular area. The thought here is: instead of assuming the financial risk of insuring patients, acquire the financial and actuarial know-how to do so, and sell that expertise to others who will assume the risks (and the lower profit levels). The Wall Street Journal says that managed care plans have made about 20% of their merger-and-acquisition deals with IT firms since 2010, up from about 7% in 2007. They’ve reduced their M&A of other insurers from 39% to 27% in the same period.

  • Aetna purchased Medicity in 2011, a company that sells software that transmits health care data across the different systems in different provider offices
  • Aetna also purchased Prodigy Health Holdings, which will allow midsize companies the financial and information knowledge to offer self-insurance options

Other insurers are purchasing physician practices. Humana purchased Concentra, which runs urgent- and occupational-care clinics. The thinking here is to exert more control over physicians and other providers, optimize their approach to patient care, and lower costs (and fatten profits).

Some insurers are expanding internationally, where legal and regulatory (and profit) constraints may be less onerous. Cigna has entered India in the form of a joint venture with TTK.

And recently, WellPoint acquired a contact lens company. Simply, the margins in vision companies are higher, and this is also an opportunity for health plans to cement relationships with consumers without the “middle men” of physicians or external opticians.

What does this mean for marketing communications?

Payer marketers traditionally target 3 audience levels: the payer level, the provider level, and the patient level. While these audiences will remain in the evolving health care landscape, they may need to be approached differently:

  • At the payer level: analytics groups may possess powerful data that show differences in cost or performance for specific drug therapies. Can marketers acquire and leverage these data to reinforce the value of our drugs or other therapies? Conversely, if sophisticated IT systems detect physician deviations from practice protocols sooner, traditional formulary controls such as prior authorizations or step edits may be enhanced and present bigger obstacles to prescriptions
  • At the physician level: if physicians work directly for health plans, their flexibility to practice or prescribe will be constrained more than if they worked on their own. Will drug marketing messages that only contain safety, efficacy, and effectiveness be enough, or will additional message components be needed? How will sales force pull-through campaigns need to be engineered if a greater degree of control binds both formularies and prescribers?
  • At the patient level: cost pressures may make insurance plans a bit more rigid. Out-of-network (or non-formulary) options may be sparse and much more expensive. What value proposition will convince the member/patient to pay for the appropriate therapy?

No one knows what the future will bring. Even if health plans do transform themselves in the 21st century and “take care” of widows and orphans in a hypothetical role as “utilities,” we can probably guess that many payer audiences will still be eager for high-quality information that demonstrates value for each health care intervention. Most likely, health care marketing communications will have challenges and goals that are similar to those of today, yet slightly more difficult.

Readers, will heath plans’ transformations affect drug and device marketing significantly?

 

 

Sources:

  1. King James Bible (James 1:27).
  2. Do “widow and orphan” stocks still exist? Investopedia.com. http://www.investopedia.com/articles/analyst/121802.asp#axzz1x7O6I4Dw. Accessed June 5, 2012.
  3. Reforms prod insurers to diversify. The Wall Street Journal. May 12, 2011. http://professional.wsj.com/article/SB10001424052748703643104576291022457851278.html. Accessed June 5, 2012.
  4. To find new revenue streams, insurers are branching out into nontraditional areas. From Health Plan Week. http://www.henryloubet.com/news030512.htm. Accessed June 5, 2012.
  5. WellPoint to buy 1-800-contacts. The Wall Street Journal. June 4, 2012. http://professional.wsj.com/article/TPBWR0000020120604e8640002u.html. Accessed June 4, 2012.

 

Also posted in Access, Analytics, Clients, Customer Relationship Marketing, Global Marketing, Health & Wellness, Healthcare Communications, Managed Care, Marketing, Patient Communications, Physician Communications, Research, Strategy, Technology | Tagged , , , , , , , , , , , , , , , , | Leave a comment
May16

The Art of the Acronym: CRM Isn’t Just CRM When It’s MCM

Posted by Angelo Campano from Ogilvy Healthworld - NY, North America

By Angelo Campano from Ogilvy Healthworld - NY, North America | Published: May 16, 2012

Customer Relationship Marketing, Non-Personal Promotion, and Direct-to-Consumer are all concepts in marketing that aim to create the most important communication with your target. Whether the communication happens on the phone, on the Internet or in the form of direct mail and/or email, the interaction with your target or prospect should be informed by customer intelligence. The practice of building customer acumen into every interaction, and listening to the response so the next communication can keep getting better, isn’t at the core of intelligent Multi-Channel Marketing—it is Multi-Channel Marketing (MCM).

We talk about “discovery,” or new response channels that can be stored on our database to help our clients understand not only what the value of their targets is, but also what part of the target lifecycle , and what that target’s significance is to the client business as well. These bits of found knowledge are important insights that can be made useable by Marketing Analytics, and they really do accrue value over time. Building a roadmap for including analytics is a step-by-step process.

Analytics Creates the Multi-Channel Roadmap

1.  Building Informational Assets With Strategic Discovery

Often the first engagement with new clients is to develop some customer-focused and market-focused analytical benchmarks that can be used to help make decisions about new marketing campaigns and, more often than not, help forecast ROI for each campaign. Many of our clients don’t have the time to look or simply don’t want to find insights that can come from a 360° view of their customers. We are looking for uniqueness such as target lifecycle stages and target value or segment.  This Strategic Discovery Process begins to answer questions about customers that can drive our Multi-Channel campaign design and how we measure the success of it.

2.  Segmentation—Giving Your Targets the Attention They Need

Normally, a segmentation system is designed to be helpful in driving messaging tone and focus while identifying the proper message to deliver. Segments should have the correct classification by one or more characteristics in order to realize which of your targets will need what type of attention. The perfect segment should meet specific standards:

  • It’s internally harmonized
  • It’s externally harmonized
  • The target responded similarly
  • It can be reached uniformly (through all the MCM channels)

3.  Campaign Targeting, Testing and Analysis

Each campaign plan team needs to establish a clear method for campaign targeting and testing for maximizing results. We need to have a mandate to check the boxes on each of these components:

  • The campaign design should include a consistent “test and learn” approach that can be carried out from one campaign to the next with new learning goals building upon findings from previous campaigns. Add to this a method for building a business case for each campaign to predict ROI and help with prioritization of the campaign changes.
  • When the targeting and testing method for each campaign is recognized, make sure to carefully document this process for potential reproduction.
  • Develop a protocol for predictive analytics for each campaign—whether models will be created for the pilot phase, or be built on results for future stages of campaign development.
  • Of course each campaign needs an established methodology for back-end campaign analysis—which will be documented for future use and roll out.
  • Establish best practices of reporting on campaigns—different types of reports for different levels of management are usually required, and this practice would be established early on in the campaign design process.

4.  Integrate Analytics for Response Management

As marketers seek to embrace target engagement, their presence takes on singular importance. Multi-Channel marketers need to examine how to bring direct marketing and web activity more closely together for:

  • Fulfilling targets’ needs by providing immediate messages relevant to them on a personal level.
  • Measuring directly ascribes and personally identifiable conversion results from campaigns that cannot be easily achieved through traditional methods, such as Non-Personal Promotion (NPP) or Direct-to-Consumer (DTC) advertisements.

5.  Identify Opportunities for Impactful Insights

We normally use survey methods both to collect critical data needed to drive Multi-Channel Marketing programs/campaigns and to build predictive analytics.

  • Evaluate whether there is data you wished you had for campaigns, but that is not available from any source
  • Behavioral surveys with compound analyses are highly useful for identifying the feature and proper mix for plans as well as prices that consumers are willing to pay for those features.
  • Determine if there is a proof of concept for the use of primary research to devise targeting strategies and campaign design.

By creating a checklist of these five stops on your Analytics Road Map, you can incorporate your target intelligence into Multi-Channel Campaigns and deliver greater relevance, better results, and promise a constant ROI…without hesitation.

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Apr10

Why Numbers Can’t Tell Us Everything

Posted by Brad Davidson from OCH Behavioral Insights - NJ, North America

By Brad Davidson from OCH Behavioral Insights - NJ, North America | Published: April 10, 2012

I’ve been on a tear recently about the numerical bias that is starting to take over our world. There seems to be an obsession with proving that the numbers are all we need to know, and I’m just not buying it. I’m referring to the tendency for news agencies to use statistical tidbits like the fact that, on average, only 12% of people wearing hoodies want to mug you (I just made that up). I’m referring to ESPN, which now uses numbers to tell us that, statistically, Tom Brady did just as well as Eli Manning in the Super Bowl (statistically, they may have; as a Patriots fan, I can tell you that, actually, he didn’t). Today, we as a culture believe that the value of what someone says is measured in the number of Twitter followers they have, or that test scores alone can measure a teacher’s effectiveness. What we don’t see in all of this is that if we only focus on those things we can measure, we miss learning about some pretty basic things that we can’t. Just because Ashton Kutcher has a zillion Twitter followers does not mean his influence will be lasting, significant, or even meaningful, beyond the type of influence Spuds McKenzie has today (which is: a funny trivia question 30 years after they were both overexposed).

Language education is a good place to look if you want to see how a numerical bias can lead to absurd and even counterfunctional behaviors. Those of us who have ever taught languages know that the way to learn a language is not through memorization of vocabulary lists or endless worksheets of grammar exercises. We know this because we know that language isn’t learned, or even used, in this way. The best way to learn a language is to try to practice communicating with it. However, because we have a hard time quantifying communicative competence, especially in junior high and high school, and because one of the main things you have to do in junior high and high schools is give people stratified grades, we forego communicating and focus on vocabulary tests and grammar worksheets. We do this to measure what people “know,” and because with a quiz or a worksheet we can assign a number like “8 out of 10 right.”

But, unless I missed something, the purpose of language instruction is to teach the language, not just prove that student A studied harder or is naturally better at memorization than student B. If we wanted to teach people to speak, we would teach differently, because we know list memorization doesn’t work. But we need grades, and we need to quantify learning, so that’s what we do. This is why someone who got straight As for 6 years of school-age French can show up in Paris a few years later and struggle to order a cup of coffee without sounding like the village idiot. We quantified, we stratified, and we missed the boat entirely.

Don’t get me wrong. I’m all in favor of math. Some of my best friends are statisticians, accountants, and physicists, math geeks one and all. But really—would you want one of these guys to order you a coffee next time you’re in Paris? Or would you rather have that sultry French guy/girl in the beret who can barely add three-digit numbers together order that next cup.

I rest my case.

 

 

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