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Why is Content Marketing failing the Pharma Industry?

Information Without Insight?

We outlined in our article “Pharma Sales Strategy – here are 3 strategies that are proven to work”  the failings of content marketing in the Pharma industry. Here are some practical tips to help pharma brand teams sidestep the pitfalls  when appraising and presenting product and market data

A recent conference on content marketing encapsulated what has been a growing problem in efforts to glean insights from today’s swell of healthcare information: An astounding number of statistics were presented with no context, along with infographics with much more focus on the visuals and little picture elements, and no scrutiny on the totality of erroneous and misrepresented data.


The Content Marketing infographic and how not to create it

The term “infographic” suggests that the “information” is primary and is supported by graphical elements. In reality, most of them are “graphormation”. The integrity and accuracy of the data take a back seat to the primary element, which is just intended to be eye candy. These graphics try (and often fail) to convey information. They often show isolated numbers and statistics in adjacent blocks of space with no context. One shouldn’t try to purvey such analytical smut.
With a few expert tips, you can avoid it.

How not to display actionable information



We explain the concept of being overconfident in data will in the next section. The simplicity of infographics makes it easy to misuse them. Most are full of spurious correlations, poor decision-making, and superficial “science.”


One of the big problems with analyzing big data to look for trends is that there’s no hypothesis to test. People tend to look at volumes of data after the fact to try to find correlations. This is a sure-fire way to convince yourself of causality where none exists. This fallacy is known as post hoc, ergo propter hoc (after this, therefore because of this). There seems to be some trend of interest in the data and one looks to see what preceded it. The “rush to conclude” bias has viewers drawing associations [where they don’t exist] to some recent market launch or ad campaign.


There are so many unrelated factors that a person is likely going to make an incorrect choice.  Said another way, there are actually many factors that preceded the interesting data trend, and most aren’t the cause. How do you know which one(s) is/are the cause if you didn’t control all the other variables? You don’t.


The precision of the numbers

Another pervasive practice in all data analysis most visible to the public in marketing data, is the seeming unwavering precision of published numbers. “59% of patients refuse tablet medications,” or “88% of clients use social media to make decisions.” ALL their decisions? There must still be decisions made by healthcare providers and patients which aren’t prompted by their Twitter feeds! They are capable of driving to the grocery store, with all the complexity that that entails, and can choose their own food items. They may even make it back home.


Also, if resampling different groups,the percentage of people who use or rely on social media is not “88%”. In most cases, these “precise” numbers were drawn from a non-scientific poll or a small sample group. From both of which it is irrelevant and erroneous to extrapolate to the broader society. An estimate of uncertainty constrains every inferential statistic. We call this set of boundaries the “confidence interval”. It not only helps data analysis to be more accurate, but it drives better business thinking, and better content marketing..


Imagine this: A company is going to make corporate-level decisions about the success of a new product launch. They base this decision on a sample or a focus group which indicated a 92% adoption rate. It may now find itself in a position of extreme financial loss if the actual uptake rate in the broader population was more like 60%-70%. Each 1% difference could mean thousands (or more) individuals not adopting a company’s technology. And in a financial sense, each unrealized planned customer conversion is equal to potential dollars lost. How is this? Through the concept of time value of money (TVM), where money now is more valuable than the same amount of money later. Whether missed customer opportunities or uninvested dollars – each is an opportunity loss. This loss is driven by an overestimate of the accuracy of statistics.


Confidence intervals act as a safety net for the ability to “know” what is going on within the data. Think about driving on the street: Each lane has a bit of buffer space to your left and right, and then there are lane markers (lines) to keep you honest. Presumably, if everyone operated their vehicle within the painted lines, there would be no accidents. This could be one’s business if operating within the certainty of their knowledge.


Let’s use the driving analogy: Pay attention to your market and your data (keep your eyes on the road). Don’t take overly risky actions and veer outside your data’s confidence intervals (traffic lanes). You’ll then be doing a great deal to insulate your business from unnecessary risk.

In the example depicted in the chart above, 60 people were polled to ask the likelihood that they would make a particular choice. The average (mean) is 53%. Not acknowledging the confidence interval is to disavow a significant amount of information about how uncertain the estimate is. The 95% confidence interval for the mean ranges from 43%-64%. What this means is that there is a 19-in-20 chance that the actual average of an individual’s dataset is between 43% and 64%. It’s not known with any more precision than that. This represents a huge difference.



Another concept that companies may not give enough credit to, and is often not executed well, is that of framing. What this refers to is putting numbers in a context (or framing them) in such a way that they’ll increase the likelihood of customers to make certain choices. This works extremely well for content marketing.

Example: If one works in clinical settings with physicians and patients, there is a much greater chance that people will elect to choose a particular treatment if it is framed as having a 90% success rate at treating their particular condition. This is in contrast to being told the drug has a 10% failure rate. Even though 10% is just the corollary of the success rate – it isn’t perceived that way. And this confounds physicians, as well as patients often. Why? Because human beings have emotional reactions to “success“ and “failure”. Our brains are not very good at working with probabilities.



Does someone want to engage with your content? Are they searching your site for particular things, or just browsing? How about email “hit rate”? “Clickiness” describes how well customers respond to your content marketing depending upon what they are looking at. There are non-random patterns in web traffic’s clickiness. Certain sites do better than others, and within sites, there are zones, sections, and content which is more engaging and “clicky” than others.

What defines this? Sure, there are visual cues. Some people have a stronger response than others to certain visual stimuli or cues. Because of this, there will never be one type of site or content which always outperforms others. The subjective variance in the “clickers” will always leave noise in the data. For example, eye-tracking analysis (where subjects’ eyes tend to track on a given image) shows differences in the focus of visual interest. Some areas of an image attract the attention of users’ eyes the most – other areas have lower visual scrutiny.

So, to enhance your contents’ clickiness, you’d want to build up the visuals and other stimuli.  Do it in such a way that their density drives proportionally greater traffic to where you are deriving the most value from their presence.


The smart path

When it comes to content marketing in the biopharmaceutical and healthcare space, there are several overlooked but common pitfalls in statistical thinking. These need an improved approach by drug makers in appraising their own data. Small, smartly-taken decisions in looking at and analyzing data can make a considerable difference in helping an organization’s analytics not be a quantitative disaster.



This article was reproduced from an article on PharmaExec.com

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