Nov6

Are You Listening?

8370148From predictive sentiment analysis and word association to audience profiling and message personalization, social listening techniques are helping healthcare marketers translate everyday conversations into brand positioning strategies, outreach programs, and relevant online content.

With the exponential growth in social sharing and social media, we posed the question, “Are You Listening” to the healthcare industry during a recent panel discussion on social listening at Ogilvy CommonHealth Worldwide’s 3rd Annual Marketing Analytics & Consulting Summit. The reaction to the discussion during the summit was incredible, as attendees bombarded our panelists with questions, which made for a lively discussion.

Joining our expert panel discussion were several contributors: Ryan Alovis, InTouchMD, Karen Auteri, IMS Health, Michele Baer, Feinstein Kean Healthcare, Kim-Fredrick Schneider, Sermo, and our very own Angelo Campano, Ogilvy Healthworld.

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Attendees learned multiple perspectives from our expert panelists. First, social listening provides marketers with a reality check for what patients and physicians are discussing in terms of disease states, available drugs, and lifestyle considerations. Second, attendees learned that many of the techniques employed have been shown to help marketers manage and respond to adverse events and reposition web content to deliver more meaningful messages to audiences they are trying to reach and educate.

Our Approach

Making sense of social conversations as related to branded and unbranded messages, and disease states, is central to capturing emerging patient and physician trends around sentiment, preference, and message personalization. In the Analytics department at Ogilvy CommonHealth Worldwide, we believe social listening needs to be a dynamic discipline that is “always on” and can be configured to leverage our sophisticated network of algorithms to aggregate unstructured conversations, and glean meaningful insights related to the way patients and physicians are talking about our clients’ products.

Natural Language Processing (NPL) and text mining machine learning algorithms are used to extract dominant concepts across posts, tweets, text messages, and call center conversations. We create a dictionary of terms with the highest frequency across messages, which is also known as a term document matrix. Correlation analyses are run across the document matrix to isolate the top 100 concepts and messages. This concept investigation is done through splitting the data into a training dataset and a test dataset (usually a 70/30 split, respectively). We then apply decision trees and neural networks to learn from our sample training data on how the text in each comment is configured to help derive classification rules on sentiment (positive or negative). Once classification rules are set, our rules are then deployed for overall monthly scoring of brand sentiment.

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We can help our clients understand questions such as:

  • What are HCP and patient sentiments about the brand?
  • What are the terms and attributes HCPs and patients are using to refer to our ailment state or specific brand?
  • What are HCPs and patients saying about competitor brands?
  • How can we proactively manage adverse events reporting?

Notable Applications

With limited social buzz, a cancer drug maker found that their brand’s category was mostly associated with terms like LDK-378, crizotinib and maintenance terms. The brand itself was strongly associated with terms like Tarceva and ALK, but social listening allowed the brand to identify opportunities within the category to purchase tertiary or long-tail terms to optimize search.

In addition to finding ways to optimize search, we were able to identify three different types of back pain sufferers through social listening. From over 115,000 local EU market conversations, we were able to identify pre-concerned, seekers, and diagnosed back pain sufferers. This learning enabled our marketing plan to amplify key brand messages at the right moment, in the right space, and at the right time that was most relevant to when each audience was most likely to respond.

Offering Many Benefits

Through understanding and evaluating the reality of how patients and physicians are talking about disease states, branded or unbranded products, we’ve reshaped website content, fine-tuned campaign messages, optimized SEO, and considered new targeting pathways. Our processes will continue to evolve to help drug manufacturers become more relevant in meeting physician information and patient care needs.

If you’re not listening, our Analytics group at Ogilvy CommonHealth Worldwide can help get you started.

 

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Oct3

Are Infographics Right for Qualitative Insights?

BI-BlogInfographics are not doing qualitative research any favors.

Good infographics clarify and condense complex information into more easily understandable and digestible visuals—an absolute plus in a culture that wants to utilize big data, but has a short attention span. It’s little wonder why they have become so popular, and why our clients are now asking for them.

Here’s an example of a good infographic by John Nelson, in which each line represents the path and intensity of a tornado tracked in the last 56 years by the National Oceanographic and Atmospheric Administration (NOAA).

Tornado Tracks Infographic2

The data is accurate and current, the story is compelling, and the design is appealing and clear.

However, infographics are not appropriate for all types of information. Some are being made to represent material which would be better suited for a simple list or chart. Others are being made to represent qualitative insights, like the one below:

The Gender Divide Infographic2

[Source: Motivation Factor and the Boston Research Group, 2012]

It seems a little weak. But why?

Rather than focusing on “black and white” data, qualitative research wades through the complexities, observing and accounting for the “gray” areas that quantitative research cannot address, such as the “whys” of human behavior. That is not to say that the insights are more complex—in fact, despite rigorous research methods based on the theories of social science, good qualitative insights seem simple, like something you have known all along but never realized.

Qualitative insights are supported by evidence that often consists of quotes, photos, videos, and notes. For example, in an ethnographic study with spinal cord injury patients, we found that patients are often in denial about their loss of function. We demonstrated this through quotes from patients saying they have accepted it, juxtaposed with photos showing patients doing things that indicated otherwise, such as refusing to build a ramp to their front door.

Despite the fact that research insights are stronger when shown with their supporting evidence, qualitative data is not easily condensed into a format appropriate for an infographic, and unfortunately is often excluded, as in the infographic above.

When qualitative insights are stripped of their rich supporting evidence, they lose a lot of their nuance and context—often bringing the validity of the insights into question. This is the last thing that qualitative research needs, since there is already a cultural bias that quantitative data is more reliable.

So, should qualitative research jump onto the infographics bandwagon? Probably not.

That’s not to say that qualitative research can’t learn something from infographics. Most people are visual learners, and too often qualitative research reports are text-heavy—our clients get bogged down trying to take it all in. We need to lighten it up, show more and tell less—craft a story from our findings that draws them in and rely on carefully chosen examples to fill in the nuances and context, rather than more text. We also need to pay attention to the aesthetics—good insights are easily lost in ugly or confusing formatting.

If we do these things, then we may just get to a point where clients do not feel the need to ask for infographics, because the research will not only be accurate and current, as it has always been, but it will be compelling, appealing, and clear, as well.

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Jul30

Numbers Don’t Lie—But They Could Be Trying to Tell You More

data tabletAn advantage of analytics that is often extolled or capitalized on is the sleek, easily consumed result at the end of miles and miles of data. It is an alluring power, to be sure, and the ability to see past the noise to extract core performance metrics is certainly foundational. Practically, however, these extractions may lull one into seemingly natural simplifications of data in order to provide neat, packaged numbers.

Analytics is not merely a mass of raw data; it is the underlying story being told by the data and it is the story that is meaningful. In essence, context imbues the easy and commonplace metrics we use and rely on with impact and meaning. Merely looking at just one aspect of performance can even be detrimental, as it blinds us from other motivating factors.

In fact, in an increasingly digital HCP world where 98% of physicians use the Internet for professional purposes [1], the task of understanding and connecting with this audience has grown more and more complex.

Specifically, with regard to digital web analytics, some of the primary and day-to-day concerns revolve around site performance and content engagement. What many of these issues generally boil down to are fairly straightforward answers—number of site visits and interest in specific site content.

Volume of site traffic is, independently, a rather inert number that can be incredibly misleading. High numbers one month followed by a much lower volume the next would assert that website performance has declined in terms of site traffic—but placing these numbers in context of another metric could change the view entirely. Looking at visits in light of bounce rates could inform us that a far smaller percentage of visits bounced in the latter month. Time on site might stay the same from month to month, but if page views per visit decrease, then more time is being spent consuming content on each individual page (on average), delivering an entirely different message once a corollary metric is introduced. The goal, after all, is to deliver the right message to the right audience, at the right time. A larger audience might not necessarily be the right audience, and so the quality of a site visit or a digital imprint is affected by and affects a multitude of other elements.

The benefits of exploring the connection between metrics are the models that emerge from the analysis, which in turn allow us to make more surprising and valuable insights. A top-line glance may miss or overlook these connections in its urgency to survey surface-level movements or trends; breaking down site referrals by traffic drivers might display which sources of site visits are the most prominent, but aligning these sources with other factors could reveal that certain segments are more likely to convert (download materials, sign up for accounts, order samples, etc.) and thus lead to immediately effective and actionable conversations.

At any point in a venture where data is generated, or can be generated, analytics can explain, evaluate, and optimize. No one part of it should be taken in isolation from the others, and this is no less relevant to the practice of analytics itself.

It is imperative that analytics never be stripped down to mere metrics, but live and thrive in a much larger framework.

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May1

Oncologists to Initiate Discussion Around Value

money stethoscopeEarlier this month a new initiative was announced to encourage oncologists to discuss the price and relative value of cancer medicines with their patients. No, this was not driven by executive fiat as part of the ACA, nor is it the brainchild of an insurance carrier. Instead, it comes from the American Society of Clinical Oncologists, or ASCO, the professional organization for oncologists and publisher of the Journal of Clinical Oncology, among other titles.

ASCO has formed working groups that will weigh efficacy, side effects and price to help better define the value of oncology medicines. Initially these groups will look at treatments for advanced lung and prostate cancer and for multiple myeloma, said Richard Schilsky, the group’s chief medical officer.

This comes a little less than a year after Scott Ramsey from the Fred Hutchinson Cancer Research Center in Seattle published a study suggesting that individuals with a cancer diagnosis were 2.5 times more likely to file for bankruptcy compared to a matched control group.

Not unlike hepatitis C, the price of therapy in oncology is a hot topic, as 11 of the 12 cancer drugs approved by the FDA in 2012 were priced at more than $100,000 per year.

To date, ASCO and another group, the National Comprehensive Cancer Network (NCCN), have published treatment guidelines that payers use as the basis for reimbursement coverage of cancer drugs, but these guidelines have been value-agnostic, meaning the price of the drug has had little or nothing to do with strong category recommendations. ASCO’s move could change this.

So how could this impact our clients’ business?

·         Pharma has traditionally had to defend ultra-premium pricing only to payers, who, in many cases, were/are legally obligated to cover the costs, at least for Medicare/Medicaid patients.  Broadening this conversation to include HCPs and patients could affect overall product positioning, messaging and channel strategy.

·         Manufacturers need to rethink how they approach the value section of the AMCP dossier as they submit these to payers as the way payers (public or private) are assessing value will change.  The dossier must also be consistent with value messages to non-payer audiences.

·         With compensation models for oncologists already shifting from “buy and bill” to “pay for quality,” these ASCO value ratings could further aid in the rapid adoption of biosimilars and generic targeted small molecules that will begin hitting the market in the next few years.

·         To the ire of many payers, pharma has been able to mitigate some financial barriers to obtaining therapy through the use of co-pay cards and other assistance programs. If the conversation turns from out-of-pocket costs to “costs to society,” demonstrating meaningful value will be of paramount importance to brands.

·         Dialogue studies in this category suggest sometimes broken dialogue between HCPs, cancer patients, and their caregivers. Layering on a discussion about the value of a drug could add to the confusion. As oncologists experiment with this new value lexicon, it could create an opportunity for brands to take a leadership role in framing the value discussion.

Historically in the US, positioning a drug on “value” has been akin to admitting your brand does not offer a meaningful advantage over existing therapy options. Will this nascent movement result in opportunities for value-based oncology brands? Only time will tell, but in the meantime rethinking how we articulate value is more important than ever.

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Apr29

Are Banner Ads Banner Advertising?

doc writing“Half the money I spend on advertising is wasted; the trouble is I don’t know which half” – John Wanamaker[1]

John Wanamaker was a successful U.S. Postmaster General, as well as an effective merchant who owned many retail stores throughout the late 1800s and early 1900s. Wanamaker died in 1922, over 90 years ago.[2]

The question that plagued Wanamaker almost 100 years ago still afflicts many marketers today. Some progress has been made as current technology and data platforms, such as Site Catalyst and Google Analytics, help marketers understand who is receiving non-personal promotions (NPP) like email or direct mail. These platforms even help marketers understand who is clicking to a particular website through emails, and further actions taken after clicking through. However, these platforms cannot aid marketers in understanding the reach and actions from all different kinds of channels.

Tactics such as direct mail, email, fax, postcards, etc., are all targeted tactics. A company can deploy all of these tactics to reach a specified audience of physicians through knowing the HCP’s email, address, and name. This same company deploying these tactics may even divide their target audience into different groups through segmentation of a specialty, age, geographic region, past behavior, number of field rep visits, etc. This company can then understand which tactics are most effective for each segment. For example, direct mail can include a vanity URL, which hematologists may take the most action on. Likewise, pulmonologists may have the most website downloads after clicking through an email. These realizations can help a company specify future marketing communication so that HCPs are individually receiving the NPP that is most appropriate for them.

Targeted tactics can help us understand a lot about an audience, but how does a marketer understand promotions such as banners? Or actions taken on a website if the website does not require registration? How does a marketer attribute these non-targeted tactics back to specific physicians in their target audience? Most healthcare brands cannot currently attribute the money spent on banners and website content to specific HCPs. Companies can engage in cookies or fingerprinting software tracking, but this tracking technology can prove costly and comes with a privacy controversy.[3]

While most healthcare brands are not at an advanced tracking level, marketers can estimate which HCPs in their target audience are viewing which banners. This means we can estimate who these banners are reaching, and who is taking further action on these banners.

We can estimate the effects that banner clicks are having on total response rate, and even the effect of banners on script writing.

We calculate this estimated reach attribution through first breaking up the United States into 212 different designated marketing areas (DMAs). With simple banner tracking, we can then look at which DMAs are receiving the highest number of impressions, and which are receiving the lowest. Then, we can look at each DMA at the HCP level. As long as we understand who exists in a brand’s target audience, we will have each HCP’s address, and can then tell which DMA an HCP lives/works in.

Next, we develop a reach threshold to begin to estimate who each non-targeted tactic is reaching. We take the average number of impressions per HCP in a DMA to develop the reach threshold. If the number of impressions in a DMA were over a predetermined amount, then we would assume that all of the physicians in that DMA have seen the banner. Likewise, if the number of impressions in a DMA were below a certain amount, we would estimate that none of the targeted physicians in that particular DMA have seen the banner.

While our understanding of non-targeted tactic reach is only at the estimation level, this can help us increase our understanding of total reached HCPs, and what channels have reached these HCPs. One healthcare drug in particular, before this estimated reach was analyzed, showed a 93.9% reach certainty through targeted tactics. With the estimated reach analysis added, the brand saw that banner impressions increased their overall reach to 99.7%, and 95.6% of HCPs were estimated to have been touched with banner impressions. This brand had invested a big portion of their budget in banner impressions, and they were ecstatic to find out that banners had reached over 95% of their targeted audience.

This idea of estimated reach could be rolled out to several industries beyond healthcare as a way to fully understand the impact of all tactics without extensive tracking methods. After all, the most important thing that marketers want to know is which half of their advertising budget is money well spent.

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Jan21

Marketing Performance Mis-measurement: Mistaking Strategy for Objective

graph io blogIf you ever wondered why your monthly campaign tracker or reports show stellar results but your brand is underperforming in the marketplace, you may be measuring (and celebrating) the wrong leading metrics. You may be mistaking strategy for outcomes, thus celebrating the wrong “successes.” This piece elaborates on this measurement error and provides suggestions for setting things right.

Strategy is not objective

A common trap analysts, marketers, and advertisers fall into is mistaking strategy for outcome. Strategy is a means to an end, the selection of options intended to ensure the achievement of specific goals or objectives. With regard to marketing, this implies the focus on specific targets, and selection of channels, tactics, and messages intended to enhance the likelihood of achieving some desired outcomes. The effectiveness of a strategy is therefore not in the execution, but how well it delivers on the outcome. In other words, a good strategy (or execution) is deemed successful, not because it is implemented, but because it delivers on the objectives and goals.

With few exceptions, most marketers, planners, and strategists understand the difference between objectives, strategy, and plan. But when it comes to measurement, this understanding seems to blur, disappear or become less important. Just to be clear, you should measure strategy, but do so in order to understand how you have executed the strategy. This should not be mistaken as an indication of marketing success.

For instance, a common objective for launch brands is to achieve a certain level of awareness among HCPs and convince them to try the product. A decent strategy could be a multichannel marketing approach that combines digital and a few offline tactics with a specific message, cadence, and level of investment against a target HCP specialty. Going by this illustration, if the execution of the strategy is flawless, the measures will show timely delivery of the messages, exposure of audience to the message, and good interactions with the respective channels. This is a successful execution of the intended strategy.

But, this same successful strategy could result in 35% awareness compared to the targeted awareness and preference of 60%. In other words, the strategy was well executed but failed to deliver the desired business outcome. It’s no surprise when marketers’ dashboards show very impressive movements in engagements and interactions, while their brands are getting clobbered in the market.

Measure strategy, but know what you are measuring is executional accountability

Executional accountability is measuring how well you are executing your strategy so that insights form the basis for adjusting strategy and evaluating the quality of execution. This is also the primary role of the execution team—clients that have tried to separate executional accountability in the spirit of fox and chick coop concerns are making a mistake. Executional evaluation must be quickly available to the execution team, to ensure a seamless understanding and feedback loop. This feedback is important to both marketers and their agency/consultants; it is in the best interest of the advertiser to understand how well the strategy is being executed. This proximity provides an immediate feedback loop for learning and improvement. Even better, incorporating leading indicators of desired outcomes makes a highly responsive and rapid cycle optimization. That way, consultants also understand what strategy works when they take on a different client engagement. That is the concept of data-driven or data-integrated marketing.

Outcomes, on the other hand, are usually empirical measures and difficult to fudge—eg, sales, market share, awareness. Unlike campaign tracking, these outcomes metrics are fine to assign to independent parties for measurement

Consultants and execution teams who take accountability seriously must track strategy as well as leading indicators of success (outcomes). These help evaluate quality of execution (strategy tracking) as well as quality of the strategy (leading indicators of objectives).

Below are examples of the difference between strategy metrics and outcomes. Specifics will depend on your marketing or campaign objectives.

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At Ogilvy, our proprietary Fusion methodology, the rigorous methodical approach for communications strategy development and evaluation, also provides the basis for identifying the right execution as well as outcomes metrics. The Scorecard from Fusion empowers the integrated team of strategists, planners, accounts, creative, and analyst with clarity of metrics that help evaluate strategy and outcomes.

In summary

  • Measure objectives as the ultimate measures of success, not the attainment of strategy
  • Measure strategy and tactics, but understand these are strategy measures. You may be successful with your strategy execution but fail to deliver the expected outcomes
  • Execution teams should be responsible for, or have almost seamless access to, execution trackers, as this prevents the teams from “flying blind”
  • Execution teams should ensure they include leading indicators in their tracking and analysis efforts, as this helps evaluate strategy’s effectiveness in delivering outcomes
  • Get third parties to evaluate outcomes. Typically, these skillsets rarely reside with execution partners and the measures are hard to fudge. Rx trends, awareness penetration, market share, revenue, patients base, formulary preference—are all key outcomes measures that are difficult to fudge

Happy data-driven marketing in 2014! May your strategy deliver on the intended.

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Nov14

Fax: The Forgotten Tactic

fax machineYou may be missing the proverbial marketing boat by not including fax in your marketing mix when communicating to healthcare professionals and their office staff. Faxing still works, and your audience wants them.

Why do we select the tactics we do?

Historical performance, logic, and strategic context go into creating campaigns for clients. Consideration for the audience, message and product are well thought out when determining the number of responses expected to be generated by a call to action (CTA). The pool of standard non-personal promotion tactics very often includes direct mail (DM), email (EM), outbound telemarketing (OBTM), and Web. Each of these is expected to deliver a benchmark response, which answers the question of why we select the tactics we select.

What’s up with the fax?

One tactic that is very often overlooked or forgotten when defining the tactic mix by strategists and account teams alike is fax. Yes, I said fax.

Patented in 1843 and mainstreamed when machine prices dropped in the 1980s, fax was once a powerful office tool allowing users to exchange information and documents instantly.* (*OK, maybe not instantly, but definitely faster than by using snail-mail). Its popularity over time has faded with increased competition from Internet-based alternatives. Fax machines, however, still retain some advantages, particularly when transmitting sensitive material which, if sent over the Internet unencrypted, may be subject to interception. Additionally, because electronic signatures on contracts are not always recognized by law, and faxed contracts with copies of signatures are, fax machines continue to be supported in business.

Now, let’s consider one of our audiences—healthcare professionals (HCPs). HCPs are a prime audience for communicating with via fax since their offices continue to exchange sensitive patient information. According to the 2012 National Physicians Survey (NPS), of the 1,190 U.S. practitioners representing more than 75 medical specialties,1 nearly 63% said faxing remains a popular method of peer-to-peer communication, second only to the telephone at 95%.  “Knowing is half the battle,” right? So, if we know this audience’s preference, wouldn’t it make sense to “fish where the fish are”?

Sure, we can fax it to you

The Marketing Analytics & Consulting team at Ogilvy Healthworld has put fax communication to the test as a communication tactic for several of our clients’ campaigns and have garnered significant results. One campaign we developed and implemented a few years ago included all of the tactics mentioned above (DM, EM, OBTM, Web and fax) designed with calls to the HCP office as the main driver of the campaign. During conversations between the outbound contact center and the HCP office staff, the top request we heard was, “Do you have information you can fax me?” or “Can you fax something to me I can share with the doctor?” With a target list of approximately 22,000 HCPs, fax was requested by the HCP office staff and sent over approximately 250,000 times over the life of the campaign.

Other than the sheer quantity of faxes that may be requested and sent throughout the course of a campaign, what response rates can be expected from a faxed communication? Responses and results of a fax tactic will vary, but the message and CTA included in the fax is what will help lead to greater responses. A current pilot being executed by our team includes a targeted list of only 2,000 HCPs. The fax designed for this campaign includes pertinent information about the program that is being introduced, such as the 800 number to reach a program representative for questions or follow-up, and the program URL. It also contains the CTA, which is the same CTA contained in the DM, EM and Web experience. Results show that the CTA responses are fulfilled via fax 62% of the time, followed by Web (24%), DM (13%) and EM (1%).

In short, if your target audience includes healthcare professionals and/or their offices, and you’re interested in getting a boost out of your campaign, your team should consider including a simple fax into the communication mix. Creating and deploying faxes is relatively inexpensive and can deliver better results than you may expect when administered properly.

1. 2012 National Physicians Survey, Sharecare and the little blue book, July 2012. http://www.sharecare.com/static/national_physicians_survey

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Jul24

5 Outrageous Claims from Pharmaceutical Advertising

Nancy thumbnailDoes the public actually believe the pharmaceutical ads they see on TV? Will a doctor change a treatment plan based on a new popular drug advertised on TV that everyone is talking about?

According to a 2004 FDA survey, the answer is yes. Doctors feel pressure from their patients to prescribe a brand name drug seen advertised. The survey also reported that more than 75% of the responses from 500 doctors indicated that their patients thought that the drug prescribed worked better than it did because of the ads they saw.

The fact is that more than 50 million people each year ask their doctors about new drugs they see advertised. In general, advertising can increase demand by at least 10 percent and the price by 5%. So it is no surprise that spending on pharmaceutical advertising in the United States has spiked dramatically: from $150 million in 1993 to $4.24 billion in 2005.

This impact on the public has always worried the FDA, formed in 1927, to protect the public from the outrageous claims made by the makers of the next miracle drug.

Consider some of these claims that may have prompted government regulation:

  • 1850: Throw aside all prejudice and buy and use the best pills ever offered to the public. They will always do good when a cathartic is required. And in no case will they do you harm.
  • 1909: Dr. B. of the Pasteur Institute, Paris, France inoculated a rabbit with human dandruff germs, and “in between five and six weeks,” says the official report of the Pasteur Institute “the rabbit was completely denuded, in fact it had become entirely bald. This experiment proves the dandruff is a contagious disease due to the presence of a microbic growth in the sebaceous glands of the scalp.
  • 1916: Those women who find that the hips are getting too large should see how the white cross electric vibrator reduces them. Lameness of any sort is caused by obstruction or imperfect circulation and the best way to treat it is to force blood through the sore muscle.

Compare these to some ads after regulation:

  • 1946: Ben Gay actually contains up to 2 ½ times more methylsalicylate and menthol—those famous pain-relieving agents known to every doctor—than five other widely offered rub-ins
  • 1976: Cool the Fever. Bufferin’s pain reliever not only starts going to those aches and pains twice as fast as plain aspirin, it also reduces the fever as effectively as any plain aspirin tablet.

 

Take a look at these ads. Do you know what is missing? There are no adverse effects mentioned at all! It wasn’t until 1985 that the FDA required drug advertisers list adverse reactions. Today, without including adverse reactions, an ad may end up an FDA “bad ad” or on Forbe’s worst ad-of-the-year list. See http://www.forbes.com/2010/02/02/drug-advertising-lipitor-lifestyle-health-pharmaceuticals-safety_slide.html

What do you think? Is more information, science, and data better? Or is the reality, as John Lennon once said, “The more I see, the less I know for sure.” Have pharmaceutical claims changed so much over the years? What’s so different about the unregulated claim made in 1917: I now hear clearly. You, too, can hear! And the Latisse regulated claim in 2010: Not enough lashes? Grow them! Longer, fuller, darker.

Whether you think regulation of pharmaceutical ads is government control or protection of the public from outrageous claims made by a modern-day medicine act, “the truth is rarely pure and never simple.” (Oscar Wilde)

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Apr9

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

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?

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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