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Category : Marketing Communications

AI-based Language Models: the End of Life Sciences Copywriters?

On November 30th, 2022, the world witnessed a technological revolution that would forever alter the landscape of content generation and communication. It was a day that will be remembered as the birth of a digital entity that came to be known as “Chat Generative Pre-Trained Transformer,” or simply ChatGPT. Some likened it to a harbinger of a new era, while others couldn’t help but draw parallels with the fictional dystopian Artificial neural network-based conscious group mind and artificial general superintelligence system known as Skynet from the Terminator franchise.

OpenAI, the visionary organization behind this innovation, had unleashed ChatGPT onto the digital stage, equipped with an astounding arsenal of knowledge. This cutting-edge AI model had ingested a staggering 570 gigabytes of data from various sources across the internet, comprising a mind-boggling 300 billion words. It was the culmination of years of research and development, resulting in a language model capable of understanding and generating human-like text on a scale never before seen.

As ChatGPT took its first virtual breath, it marked the beginning of a new chapter in the world of life science copywriting and content creation. This AI juggernaut had one goal (for the sake of this blog post’s plot at least): to revolutionize the way businesses and individuals interacted with written content. Gone were the days of struggling to craft compelling copy, as ChatGPT was poised to become the ultimate tool for content creators worldwide. 

The global response was swift and diverse. Some celebrated ChatGPT as a boon, an AI-driven ally that would transform the way we communicate and create content, making it more efficient and accessible. Others, however, raised concerns about the implications of such advanced technology, fearing the potential for misuse and its impact on employment in the creative industry. In today’s blog post, this is exactly what we’ll be discussing: The rise of different AI-based language models (including ChatGPT, Gemini, Phind, and more), their advantages, and more importantly, their limitations in the world of life sciences copywriting, to ultimately answer the question that all of us have been asking ourselves: Are AI-based Language Models the end of Life Sciences Copywriters? 

No, they are not. And please excuse our unorthodox approach to this blog post, we know we should build things up throughout the post to keep you hooked, and deliver the final answer in the end, but our copywriters took it personally. Regardless, we’ll be pitting ourselves against ALMs in the most unbiased way possible: exposing the human limitations of using ALMs, before discussing ALMs’ limitations and why we believe they are still a long way from taking our jobs away. 

Proper Prompt Engineering: 

Put simply, the output of ALMs are as good as the input you give them. You need to design a prompt to feed into the ALM that is detailed, yet not convoluted, to get an optimal output, which might sound simple and straightforward, but it really isn’t. Here are a few tips to writing a solid prompt: 

  1. Know what your objective is. It might be obvious to you, but you need to make sure the ALM understands it too. 
  2. Provide contextual details. 
  3. Keep it clear and concise.
  4. Provide the ALM with reference material (keep in mind the limitations of some ALMs, like ChatGPT, who only has access to information pre-september 2021).
  5. Set constraints. (e.g: use references dating from 2015 forward)
  6. Specify the desired format.

So next time you’re not satisfied with your ALM’s output, make sure you give it a solid prompt first. However, the question that now asks itself is, is a solid prompt still enough to produce life sciences copy that is on par with what you are writing? Let’s compare some ALM output to human-written copy on the website of a mid-sized CRO for their preclinical ophthalmology services. 

Round 1

What they wrote:
HEALTHY EYES TO ENVISION A CLEARER FUTURE

In the fourth century B.C, Plato wrote that light emanated from the eye, seizing objects in its ray, which allowed us to see them. Centuries later, our understanding of ocular anatomy and physiology has drastically improved. In addition, what was once perceived by humans of antiquity as a punishment from the gods, is today perceived as what it truly is: pathological processes that stem at the molecular level, and manifest all the way up to the systemic level.

Ophthalmic diseases have the potential to take away what many agree is the most important and valued of the 5 senses: our vision.

At […], we have a vision. A vision of a world where ophthalmic diseases are history, just like Plato’s extramission theory. With your pipelines, our expertise, and a wide selection of models with immediate availability, we believe that our vision might one day come true. Let’s start your study today.

The prompt we used:

I need you to write me the title and first paragraph (around 170 words in total) of a landing page copy. The company you are writing for  is a CRO. The landing page at hand is an ophthalmology models landing page.

ChatGPT’s output:

Title: “Revolutionize Ophthalmic Research with […] Cutting-edge CRO Solutions”

Paragraph:

Welcome to […], your trusted partner in advancing ophthalmic research through unparalleled Contract Research Organization (CRO) services. As a leader in the field, we understand the critical importance of innovative models in unraveling the complexities of eye-related disorders and developing breakthrough therapies. Our Ophthalmology Models Landing Page is your gateway to a diverse range of meticulously designed preclinical models that precisely mimic the intricacies of ocular conditions. At […], we believe in driving scientific progress, and our collection of ophthalmic models stands at the forefront of transformative research. With a commitment to excellence, our team of experts collaborates with you to accelerate drug development, optimize trial outcomes, and propel your ophthalmology projects to new heights. Explore our cutting-edge models, elevate your research capabilities, and join us in shaping the future of ophthalmic medicine. […]: Pioneering CRO Solutions for Visionary Research.

Gemini’s output:

Title: Decoding the Future of Sight: […]’s Cutting-Edge Ophthalmology Models

Unveiling clarity in clinical trials. For decades, ophthalmology research has faced unique challenges – complex disease models, diverse patient populations, and the inherent fragility of the visual system. At […], we understand these nuances. That’s why we’ve pioneered a suite of next-generation ophthalmology models designed to unlock unparalleled precision and efficiency in your clinical trials.

From age-related macular degeneration to diabetic retinopathy, our advanced models encompass a spectrum of eye diseases. We leverage human iPSC-derived retinal organoids, mimetics, and in vivo models, each meticulously calibrated to deliver accurate, predictive data. With […]’s ophthalmology models, you can gain deeper insights into disease mechanisms, test novel therapies with confidence, and navigate the path to regulatory approval with clarity.

Dive deeper into our models and discover how […] can empower your vision for brighter tomorrow.

Phind V9’s output:

Title: “[…]: Revolutionizing Ophthalmology Models”

Welcome to […], your go-to partner for state-of-the-art ophthalmology models. With our team of seasoned professionals, we strive to push the boundaries of ophthalmology research, providing accurate and reliable models to drive progress. Unlike others, we offer a unique selling proposition (USP) that distinguishes us in the market. Our commitment to quality and efficiency ensures optimal results for our clients.

At […], we focus on a specific niche within ophthalmology, creating innovative models that significantly contribute to the understanding and treatment of eye diseases. Our models, born from years of rigorous research and development, are meticulously tested to ensure they provide valuable insights.

Join us today and experience the future of ophthalmology models. Whether you’re a researcher seeking advanced models for your studies or a clinician looking for reliable data, […] offers you the tools and resources needed to enhance patient care.

Discussion:

While both ChatGPT and Phind’s responses fell short in their execution, Gemini managed to provide a somewhat acceptable output. ChatGPT and Phind’s attempts were riddled with cliches and lacked depth, offering only surface-level information. Their copy resembled generic marketing material, failing to spark any real interest or present the information in a compelling way. In contrast, Gemini’s output, while not exceptional, avoided the pitfalls of the others. It conveyed the necessary information in a clear and concise manner, without resorting to overused phrases or basic wording.

Round 2

The prompt we used:

Write me a 150 word introduction to a blog post. The blog post will cover the use of vacuum in rotary evaporation. It will be published on the website of a company that sells rotary evaporators. Your target audience is lab managers, scientists that work in labs, or pretty much anyone who might be interested in purchasing a rotary evaporator. 

ChatGPT’s output:

Welcome to our comprehensive guide on the vital role of vacuum in rotary evaporation! As lab managers and scientists, we understand the paramount importance of efficiency, precision, and reliability in laboratory processes. In this post, we delve into the intricate workings of rotary evaporators, shedding light on the indispensable function of vacuum in distillation and concentration procedures.

Whether you’re a seasoned researcher or new to the world of laboratory equipment, understanding the principles behind rotary evaporation is essential. We’ll explore how vacuum manipulation facilitates faster evaporation rates, lower boiling points, and ultimately, superior sample recovery. From solvent recovery to cannabinoid extraction and beyond, the utilization of vacuum technology revolutionizes the way we approach scientific experimentation.

Join us as we navigate through the ins and outs of vacuum-assisted rotary evaporation, empowering you to make informed decisions when selecting the ideal equipment for your laboratory needs.

Gemini’s output:

Title: Unveiling the Power of Vacuum: Optimizing Efficiency in Rotary Evaporation

The rotary evaporator, a cornerstone in countless labs, excels at solvent removal and sample concentration. But did you know that the magic truly lies in the silent partner – vacuum pressure? Mastering this aspect unlocks a new level of efficiency and precision in your rotary evaporation processes.

This blog post delves into the intricacies of vacuum in rotary evaporation. We’ll explore how reduced pressure dramatically lowers the boiling point of solvents, enabling faster evaporation at gentler temperatures. Discover how a well-optimized vacuum system safeguards your temperature-sensitive samples and facilitates the removal of stubborn high-boiling-point solvents.

We’ll also shed light on choosing the appropriate vacuum pump for your specific needs, ensuring seamless integration with your rotary evaporator. By the end, you’ll be equipped with the knowledge to leverage the full potential of your rotary evaporator and achieve optimal results in your research endeavors.

Phind V9’s output:

In the realm of laboratory science, rotary evaporators, or “rotovaps,” are indispensable tools for concentrating chemical and biological samples. These devices, found in nearly all laboratories across industries such as chemical, pharmaceutical, food, and environmental sectors, are essential for processes requiring sample concentration or solvent distillation. The use of vacuum in rotary evaporation is a critical feature that enhances the efficiency and versatility of these instruments. By creating a reduced internal pressure above a solution, vacuum technology lowers the boiling point of solvents, enabling their distillation at lower temperatures. This not only preserves the chemical structure of the solute or analyte but also allows for the recycling of solvents, reducing waste and expense. This blog post will delve into the intricacies of vacuum in rotary evaporation, exploring its role in enhancing sample concentration, solvent recovery, and the overall efficiency of laboratory processes. Whether you’re a lab manager, a scientist, or simply someone interested in the latest advancements in rotary evaporator technology, this article will provide valuable insights into how vacuum technology is revolutionizing the field of sample concentration and solvent recovery.

Discussion:  

For this round, we’ll go over four parameters in each of the ALM’s responses: The tone, the depth, the target audience, and the overall copy: 

ChatGPT’s output:

Tone: Whimsical and over the top

Depth: Provides an overview of the topic with emphasis on the importance of vacuum in rotary evaporation.

Target Audience: Appears to target a broad audience, from seasoned researchers to newcomers in the field.

Overall Copy:  The copy could benefit from a more engaging tone to resonate better with the target audience; it lacks elements that would create a sense of urgency or excitement. All in all, it’s very superficial

Gemini’s output:

Tone: Engaging and slightly more informal.

Depth: Offers a detailed exploration of the role of vacuum in rotary evaporation, including information on choosing the right vacuum pump.

Target Audience: Likely aimed at researchers or lab technicians seeking to optimize their rotary evaporation processes.

Overall Copy: Compared to the other ALMs, this version resonates most clearly with the target audience compared to the others. It’s far more engaging, and has a more “human” ring to it. All in all, Gemini did a fair job, and the copy it produced is probably usable.

Phind V9’s output:

Tone: Formal and technical.

Depth: Provides a comprehensive overview of vacuum in rotary evaporation, covering its applications across various industries and its benefits in sample concentration and solvent recovery.

Target Audience: Appears to target professionals in the field of laboratory science, including lab managers and scientists.

Overall Copy: While informative, the last draft could benefit from a more dynamic tone to capture the reader’s attention. 

Final Round:

For the final round, we asked every ALM to back-up the copy that it formulated in Round 2, with articles, papers, etc. 

ChatGPT’s output:

As an AI language model, I don’t have direct access to my training data or know where it came from. However, I was trained on a mixture of licensed data, data created by human trainers, and publicly available data. My knowledge is based on the data used to train me and my ability to generate language-based responses. I can generate text based on the prompts I receive and provide information and answer questions to the best of my knowledge and abilities. If you have any concerns about the information I provide, please feel free to let me know, and I’ll do my best to address them.

Gemini’s output:

Importance of Rotary Evaporation:

Role of Vacuum in Rotary Evaporation:

  • Science Direct – Rotary Evaporation: [invalid URL removed] This article from ScienceDirect discusses the boiling point principle and the role of reduced pressure in achieving lower boiling points for solvents.
  • Labsphere – A Guide to Rotary Evaporation: [invalid URL removed] This guide highlights the importance of a well-optimized vacuum system for protecting temperature-sensitive samples.

Vacuum Pump Selection:

  • Knf LabTech – Selecting a Rotary Vane Vacuum Pump: [invalid URL removed] This resource from a leading manufacturer provides guidance on choosing an appropriate vacuum pump for rotary evaporation based on specific needs.

Overall Benefits:

  • Royal Society of Chemistry – Rotary Evaporation: [invalid URL removed] The Royal Society of Chemistry emphasizes how mastering rotary evaporation techniques can lead to increased efficiency and improved research outcomes.

Phind V9’s output:

A key feature of PhindV9 is its integration of source attribution on its interface: everytime it provides you with an output, it automatically shares the list of sources that it used to provide you with said output (see image below), making it easier to fact-check everything it gives you. 


Discussion:

When evaluating large language models for informative tasks, phindv9 stands out for its ability to provide users with direct links to the sources it uses in every response. This allows users to quickly verify the information and delve deeper if desired. While other models, like Gemini, may offer general links to resources (most of which had invalid URLs in our example), Phind V9’s focus on direct source attribution streamlines the research process. It’s important to note that not all models can provide this functionality, as evidenced by ChatGPT’s current limitations in incorporating real-world data.

Conclusion:

Lack of Nuance: The life sciences field thrives on precision and nuance. Technical vocabulary, complex concepts, and ethical considerations demand a depth of understanding that AI models, despite their vast data stores, often lack. This can lead to inaccurate or misleading copy, a potentially dangerous pitfall in a field where clarity is paramount.

The Human Touch: The best life science copywriting resonates with the audience. It speaks to their fears, hopes, and aspirations in a way that is both informative and emotionally engaging. This is where the human touch remains irreplaceable. AI can generate text, but it cannot infuse it with the empathy and understanding that a skilled copywriter can.

Creative Roadblocks: While AI excels at generating standard content formats, it often struggles with the truly creative. Brainstorming unique ideas, crafting compelling narratives, and breaking through creative roadblocks are still the domain of human ingenuity. AI can be a valuable tool in the process, but it is not a substitute for the human imagination.

Time needed to generate a good prompt: While ALMs offer the potential to save time on writing, using them effectively often requires some back-and-forth. You might need to refine your prompts and evaluate the outputs several times. This iterative process can be valuable, but consider the time investment. Ultimately, the question is this: is it more efficient to create a detailed prompt to get the desired results from the ALM, or to write the entire piece yourself?

Can DALL-E 3 Generate Passable Life Science Images?

For those uninitiated to our blog, a few months ago I ran a fairly extensive, structured experiment to compare DALL-E 2, Midjourney 5, and Stable Diffusion 2 to see if any of them could potentially replace generic life science stock imagery. It ended up being both informative and accidentally hilarious, and you can see the whole thing here. But that was back in the far-gone yesteryear of July, it is currently December, and we live in the early era of AI which means that months are now years and whatever happened 5 months ago is surely obsolete. Since Dall-E 3 came out in October, it’s worth finding out if it will do better than it did in the previous round, where DALL-E 2 was notably inferior to Midjourney for 9 of the 10 queries.

Perhaps I’ll do a more comprehensive comparison again later, but for now I’m just going to run some similar queries to the ones used last time to get a reasonable side-by-side comparison. Bing Image Creator was used to generate the images since labs.openai.com, which was used last time, is still plugged in to DALL-E 2.

Test 1: A female scientist performing cell culture at a biosafety cabinet.

The last time we tried this, DALL-E 2 gave us images that looked 75% like a picture and 25% like claymation, but even if that problem wasn’t there it was still somewhat far off. Let’s see if DALL-E 3 can do better.

I tried to be a little bit descriptive with these prompts, as supposedly DALL-E 3 uses GPT4 and better understands written requests. Supposedly. Here’s what it gave me for “A photograph of a female scientist in a laboratory sitting at a biosafety cabinet holding a serological pipette performing cell culture. Her cell culture flasks have yellow caps and her cell culture media is red.” It definitely got the yellow caps and red media. As for the rest…

It’s immediately clear that DALL-E 3, just like all its ilk, was primarily trained from large repositories of generic stock images, because all these labs look like what you would imagine a lab would look like if you didn’t know what a lab actually looked like. There are plenty of generic microscopes close at hand, although it didn’t even get those right. There are no biosafety cabinets to be found. Those vessels are essentially test tubes, not cell culture flasks. To top it off, all the female scientists look like porcelain dolls modeling for the camera. I tried to fix at least one of those things and appended “She is attentive to her work.” to the subsequent query. Surprisingly, this time it seemed to make some subtle attempts at things which might be construed as biosafety cabinets, but only to a completely naive audience (and, of course, it put a microscope in one of them).

Since DALL-E 2 arguably provided more realistic looking people in our previous test, I reverted to the simplicity of the previously used query: “A photograph of a female scientist performing cell culture at a biosafety cabinet.”

We’re not getting any closer. I have to call this an improvement because it doesn’t look like the image is melting, but it’s still very far from usable for a multitude of reasons: the plasticware is wrong, the pipettes are wrong, the people still look like dolls, the biosafety cabinets aren’t right, some of the media seems to be growing alien contamination, the background environment isn’t realistic, etc.

Today’s comic relief is brought to you by my attempt to get it to stop drawing people as porcelain dolls. I Googled around a bit and found that queries structured differently sometimes are better at generating realistic looking people so I gave this prompt a go: “2023, professional photograph. a female scientist performing cell culture at a biosafety cabinet.” What a gift it gave me.

Test 2: Liquid dripping from pipette tips on a high-throughput automated liquid handling system.

I’m choosing this one because it was the only query that DALL-E 2 was almost good at in our previous comparison. Out of 10 tests in that experiment, Midjourney produced the best output 9 times and DALL-E once. This was that one. However, stock imagery was still better. DALL-E 2’s image didn’t capture any of the liquid handler and the look of the image was still a bit melty. Let’s see if it’s improved!

Prompt: “A close up photograph of liquid dripping from pipette tips on a high-throughput automated liquid handling system.”

DALL-E 3 seems to have eschewed realism entirely and instead picked up Midjourney’s propensity for movie stills and sci-fi. Perhaps more specificity will solve this.

Prompt 2: “A close up photograph of liquid being dispensed from pipette tips into a 96-well microplate in a high-throughput automated liquid handling system.”

DALL-E clearly only has a vague idea of what a 96-well plate looks like and also cannot count; none of these “plates” actually have 96 wells. Regardless, these are no more realistic, clearly unusable, and DALL-E 2’s output would likely have a far greater probability of passing as real.

So nope, we’re still not there yet, and Midjourney is probably still the best option for realistic looking life science images based on what I’ve seen so far.

… but what about micrographs and illustrations?

All the previous posts dealt with recreations of real-world images. What about images which a microscope would take or scientific illustrations? To test that out, I quickly tested out four prompts I had used last time:

  • A high-magnification fluorescent micrograph of neural tissues
  • A colored scanning electron micrograph of carcinoma cells
  • A ribbon diagram of a large protein showing quaternary structure
  • A 3D illustration of plasmacytes releasing antibodies

Here is the best it provided for each, in clockwise order from top left:

DALL-E 3’s neurons were actually worse than DALL-E 2’s, with nothing even being remotely close. It’s carcinomas were more in line with what Midjourney provided last time, but look slightly more cartoonish. The ribbon diagram is the better than any from the last test, although the structure is blatantly unrealistic. It’s plasmacytes could make for a passable graphic illustration, if only they contained anything that looks like antibodies.

Conclusion

DALL-E 3 is a clear improvement from DALL-E 2. While it may be two steps forward and one step back, overall it did provide outputs which were closer to being usable than in our last test. It still has a way to go, and I don’t think it will peel us away from defaulting to Midjourney, but if it continues to improve at this rate, DALL-E 4 could provide a breakthrough for the generation of life science stock images.

"Want brand to shine brighter than even DALL-E could imagine? Contact BioBM. We’ll win you the admiration and attention of your scientist customers."

Can AI Replace Life Science / Laboratory Stock Images?

We’re over half a year into the age of AI, and its abilities and limitations for both text and image generation are fairly well-known. However, the available AI platforms have had a number of improvements over the past months, and have become markedly better. We are slowly but surely getting to the point where generative image AIs know what hands should look like.

But do they know what science looks like? Are they a reasonable replacement for stock images? Those are the meaningful questions if they are going to be useful for the purposes of life science marketing. We set to answer them.

A Few Notes Before I Start Comparing Things

Being able to create images which are reasonably accurate representations is the bare minimum for the utility of AI in replacing stock imagery. Once we move past that, the main questions are those of price, time, and uniqueness.

AI tools are inexpensive compared with stock imagery. A mid-tier stock imagery site such as iStock or ShutterStock will charge roughly $10 per image if paid with credits or anywhere from $7 to roughly a quarter per image if you purchase a monthly subscription. Of course, if you want something extremely high-quality, images from Getty Images or a specialized science stock photo provider like Science Photo Library or ScienceSource can easily cost many hundreds of dollars per image. In comparison, Midjourney’s pro plan, which is $60 / month, gives you 30 hours of compute time. Each prompt will provide you with 4 images and generally takes around 30 seconds. You could, in theory, acquire 8 images per minute, meaning each costs 0.4 cents. (In practice, with the current generation of AI image generation tools, you are unlikely to get images which match your vision on the first try.) Dall-E’s pricing is even simpler: each prompt is one credit, also provides 4 images, and credits cost $0.13 each. Stable Diffusion is still free.

Having used stock image sites extensively, and having spent some time playing around with the current AI offerings for purposes other than business, it’s not clear to me which is more convenient and takes less time. Sometimes you’ll get lucky and get a good AI image the first try, but you could say the same about stock image sites. Where AI eliminates the need to go through pages and pages of stock images to find the right one, it replaces that with tweaking prompts and waiting for the images to generate. It should be noted that there is some learning curve to using AI as well. For instance, telling it to give you a “film still” or “photograph” if you want a representation of real life which isn’t meant to look illustrated and cartoonish. There’s a million of these tricks and each system has its own small library of commands which helps to be familiar with so you can get an optimal output. Ultimately, AI probably does take a little bit more time, but it also requires more skill. Mindlessly browsing for stock images is still much easier than trying to get a good output from a generative AI (although playing with AI is usually more fun).

Where stock images simply can’t compete at all is uniqueness. When you generate an image with an AI, it is a unique image. Every image generated is one of one. You don’t get the “oh, I’ve seen this before” feeling that you get with stock images, which is especially prevalent for life science / laboratory topics given the relatively limited supply of scientific stock images. We will probably, at some point in the not too distant future, get past the point of being able to identify an AI image meant to look real by the naked eye. Stock images have been around for over a century and the uniqueness problem has only become worse. It is inherent to the medium. The ability to solve that problem is what excites me most about using generative AI imagery for life science marketing.

The Experiment! Ground Rules

If this is going to be an experiment, it needs structure. Here is how it is going to work.

The image generators & stock photo sites used will be:

I was going to include ShutterStock but there’s a huge amount of overlap with iStock, I often find iStock to have slightly higher-quality images, and I don’t want to make more of a project out of this than it is already going to be.

I will be performing 10 searches / generations. To allow for a mix of ideas and concepts, some will be of people, some will be of things, I’ll toss in some microscopy-like images, and some will be of concepts which would normally be presented in an illustrated rather than photographed format. With the disclaimer that these concepts are taken solely from my own thoughts in hope of trying to achieve a good diversity of concepts, I will be looking for the following items:

  1. A female scientist performing cell culture at a biosafety cabinet.
  2. An Indian male scientist working with an LC-MS instrument.
  3. An ethnically diverse group of scientists in a conference room holding a lab meeting. One scientist presents their work.
  4. A close up of liquid dripping from pipette tips on a high-throughput automated liquid handling system.
  5. An NGS instrument on a bench in a genomics lab.
  6. A high-magnification fluorescent micrograph of neural tissues.
  7. A colored scanning electron micrograph of carcinoma cells.
  8. A ribbon diagram of a large protein showing quaternary structure.
  9. A 3D illustration of plasmacytes releasing antibodies.
  10. An illustration of DNA methylation.

Such that nothing has an edge, none of these are things which I have recently searched for on stock image sites nor which I have previously attempted to generate using AI tools. Note that these are solely the ideas which I am looking for. These are not necessarily the exact queries used when generating AI images or searching the stock photo sites.

Looking for stock images and generating AI graphics are very different processes but they both share one critical dimension: time. I will therefore be limiting myself to 5 minutes on each platform for each image. That’s a reasonable amount of time to try to either find a stock image or get a decent output from an AI. It will also ensure this experiment doesn’t take me two days. Here we go…

Round 1: A female scientist performing cell culture at a biosafety cabinet.

One thing that AI image generators are really bad at in the context of the life sciences is being able to identify and reproduce specific things. I thought that this one wouldn’t be too hard because these models are in large part trained on stock images and there’s a ton of stock images of cell culture, many of which look fairly similar. I quickly realized that this was going to be an exercise in absurdity and hilarity when DALL-E gave me a rack of 50 ml Corning tubes made of Play-Doh. I would be doing you a grave disservice if I did not share this hilarity with you, so I’ll present not only the best images which I get from each round, but also the worst. And oh, there are so many.

I can’t withhold the claymation 50 ml Corning tubes from you. It would just be wrong of me.

I also realized that the only real way to compensate for this within the constraints of a 5-minute time limit is to mash the generate button as fast as I can. When your AI only has a vague idea of what a biosafety cabinet might look like and it’s trying to faithfully reproduce them graphically, you want it to be able to grasp at as many straws as possible. Midjourney gets an edge here because I can run a bunch of generations in parallel.

Now, without further ado, the ridiculous ones…

Round 1 AI Fails

Dall-E produced a large string of images which looked less like cell culture than women baking lemon bars.

Midjourney had some very interesting takes on what cell culture should look like. My favorite is the one that looks like something in a spaceship and involves only machines. The woman staring at her “pipette” in the exact same manner I am staring at this half-pipette half-lightsaber over her neatly arranged, unracked tubes is pretty good as well. Side note: in that one I specifically asked for her to be pipetting a red liquid in a biosafety cabinet. It made the gloves and tube caps red. There is no liquid. There is no biosafety cabinet.

For those who have never used it, Stable Diffusion is hilariously awful at anything meant to look realistic. If you’ve ever seen AI images of melted-looking people with 3 arms and 14 fingers, it was probably Stable Diffusion. The “best” it gave me were things that could potentially be biosafety cabinets, but when it was off, boy was it off…

Rule number one of laboratories: hold things with your mouth. (Yes we are obviously kidding, do not do that.)

That was fun! Onto the “successes.”

Round 1 AI vs. Stock

Midjourney did a wonderful job of creating realistic-looking scientists in labs that you would only see in a movie. Also keeping with the movie theme, Midjourney thinks that everyone looks like a model; no body positivity required. It really doesn’t want people to turn the lights on, either. Still, the best AI results, by a country mile, were from Midjourney.

The best Dall-E could do is give me something that you might confuse as cell culture at a biosafety cabinet if you didn’t look at it and were just looking past it as you turned your head.

Stable Diffusion’s best attempts are two things which could absolutely be biosafety cabinets in Salvador Dali world. Also, that scientist on the right may require medical attention.

Stock image sites, on the other hand, produce some images of cell culture in reasonably realistic looking settings, and it took me way less than 5 minutes to find each. Here are images from iStock, Getty Images, and Science Photo Library, in that order:

First round goes to the stock image sites, all of which produced a better result than anything I could coax from AI. Round goes to stock sites. AI 0 – 1 Stock.

Round 2: An Indian male scientist working with an LC-MS instrument.

I am not confident that AI is going to know what an LC-MS looks like. But let’s find out!

One notable thing that I found is that the less specific you become, the easier it gets for the AI. The below image was a response to me prompting Dall-E for a scientist working with an LC-MS, but it did manage to output a realistic looking person in an environment that could be a laboratory. It’s not perfect and you could pick it apart if you look closely, but it’s pretty close.

A generic prompt like “photograph of a scientist in a laboratory” might work great in Midjourney, or even Dall-E, but the point of this experiment would be tossed out the window if I set that low of a bar.

Round 2 AI Fails

Midjourney:

Dall-E:

Stable Diffusion is terrible. It’s difficult to tell the worst ones from the best ones. I was going to call one of these the “best” but I’m just going to put them all here because they’re all ridiculous.

Round 2 AI vs. Stock

Midjourney once again output the best results by far, and had some valiant efforts…

… but couldn’t match the real thing. Images below are from iStock, Getty Images, and Science Photo Library, respectively.

Once thing you’ve likely noticed is that none of these are Indian men! While we found good images of scientists performing LC-MS, we couldn’t narrow it down to both race and gender. Sometimes you have to take what you can get! We were generally able to find images which show more diversity, however, and it’s worth noting that Science Photo Library had the most diverse selection (although many of their images which I found are editorial use only, which is very limiting from a marketing perspective).

Round 2 goes to the stock sites. AI 0 – 2 Stock.

Round 3: An ethnically diverse group of scientists in a conference room holding a lab meeting. One scientist presents their work.

This should be easier all around.

Side note: I should’ve predicted this, but as the original query merely asked for science, my initial Midjourney query made it look like the lab was presenting something out of a sci-fi game. Looked cool, but not what we’re aiming for.

Round 3 AI Fails

Dall-E presented some interesting science on the genetic structure of dog kibble.

Dall-E seemed to regress with these queries, as if drawing more than one person correctly was just way too much to ask. It produced a huge stream of almost Picasso-esque people presenting something that vaguely resembled things which could, if sufficiently de-abstracted, be scientific figures. It’s as if it knows what it wants to show you but is drawing it with the hands of a 2 year old.

Stable Diffusion is just bad at this. This was the best it could do.

Round 3 AI vs. Stock

Take the gloves off, this is going to be a battle! While Midjourney continued its penchant for lighting which is more dramatic than realistic, it produced a number of beautiful images with “data” that, while they are extravagant for a lab meeting, could possibly be illustrations of some kind of life science. A few had some noticeable flaws – even Midjourney does some weird stuff with hands sometimes – but they largely seem usable. After all, the intent here is as a replacement for stock images. Such images generally wouldn’t be used in a way which would draw an inordinate amount of attention to them. And if someone does notice a small flaw that gives it away as an AI image, is that somehow worse than it clearly being stock? I’m not certain.

Stock images really fell short here. The problem is that people taking stock photos don’t have data to show, so they either don’t show anyone presenting anything, or they show them presenting something which betrays the image as generic stock. Therefore, to make them look like scientists, they put them in lab coats. Scientists, however, generally don’t wear lab coats outside the lab. It’s poor lab hygiene. Put a group of scientists in a conference room and it’s unusual that they’ll all be wearing lab coats.

That’s exactly what iStock had. Getty Images had an image of a single scientist presenting, but you didn’t see the people he was presenting to. Science Photo Library, which has far less to choose from, also didn’t have people presenting visible data. The three comps are below:

Side Note / ProTip: You can find that image from Getty Images, as well as many images that Getty Images labels as “royalty free” on iStock (or other stock image sites) for way less money. Getty will absolutely fleece you if you let them. Do a reverse image search to find the cheapest option.

Considering the initial idea we wanted to convey, I have to give this round to the AI. The images are unique, and while they lack some realism, so do the stock images.

Round 3 goes to AI. AI 1 – 2 Stock.

Let’s see if Dall-E or Stable Diffusion can do better in the other categories.

Round 4: A close up of liquid dripping from pipette tips on a high-throughput automated liquid handling system.

I’ve seen nice stock imagery of this before. Let’s see if AI can match it, and if I can readily find it again on the stock sites.

Round 4 AI Fails

Dall-E had a long string of images which looked like everything shown was made entirely of polystyrene and put in the autoclave at too high a temperature. You might have to click to expand to see the detail. It looks like everything partially melted, but then resolidified.

Stable Diffusion is more diffuse than stable. Three of these are the best that it did while the fourth is when it gave up and just started barfing visual static.

This is the first round where Midjourney, in my opinion, didn’t do the best job. Liquid handling systems have a fair amount of variability in how they can be presented, but pipette tips do not, and it didn’t seem to know what pipette tips should look like, nor how they would be arranged in a liquid handling system. These are the closest it got:

Very pretty! Not very accurate.

Round 4 AI vs. Stock

We have a new contestant for the AI team! Dall-E produced the most realistic looking image. Here you have it:

Not bad! Could it be an automated pipetting system? We can’t see it, but it’s possible. The spacing between the tips isn’t quite even and it looks like PCR strips rather than a plate, but hey, a microplate wasn’t part of the requirements here.

Let’s see what I can dig up for stock… Here’s iStock, Getty, and SPL, respectively:

I didn’t get the drips I was looking for – probably needed to dig more for that – but we did get some images which are obviously liquid handling systems in the process of dispensing liquids.

As valiant of an effort as Dall-E had, the images just aren’t clean enough to have the photorealism of real stock images. Round goes to the stock sites. AI 1 – 3 Stock.

Round 5: An NGS instrument on a bench in a genomics lab.

I have a feeling the higher-end stock sites are going to take this, as there aren’t a ton of NGS instruments so it might be overly specific for AI.

Round 5 AI Fails

Both Midjourney and Dall-E needed guidance that a next-generation sequencer wasn’t some modular device used for producing techno music.

With Dall-E, however, it proved to not be particularly trainable. I imagine it’s AI mind thinking: “Oh, you want a genome sequencer? How about if I write it for you in gibberish?” That was followed by it throwing it’s imaginary hands in the air and generating random imaginary objects for me.

Midjourney also had some pretty but far-out takes, such as this thing which looks much more like an alien version of a pre-industrial loom.

Round 5 AI vs. Stock

This gets a little tricky, because AI is never going to show you a specific genome sequencer, not to mention that if it did you could theoretically run into trademark issues. With that in mind, you have to give them a little bit of latitude. Genome sequencers come in enough shapes and sizes that there is no one-size-fits-all description of what one looks like. Similarly, there are few enough popular ones that unless you see a specific one, or its tell-tale branding, you might not know what it is. Can you really tell the function of one big gray plastic box from another just by looking at it? Given those constraints, I think Midjourney did a heck of a job:

There is no reason that a theoretical NGS instrument couldn’t look like any of these (although some are arguably a bit small). Not half bad! Let’s see what I can get from stock sites, which also will likely not want to show me logos.

iStock had a closeup photo of a Minion, which while it technically fits the description of what we were looking for, it doesn’t fit the intent. Aside from that it had a mediocre rendering of something supposed to be a sequencer and a partial picture of something rather old which might be an old Sanger sequencer?

After not finding anything at all on Getty Images, down to the wire right at the 5:00 mark I found a picture of a NovaSeq 6000. Science Photo Library had an image of an ABS SOLiD 4 on a bench in a lab with the lights off.

Unfortunately, Getty has identified the person in the image, meaning that even though you couldn’t ID the individual just by looking at the image, it isn’t suitable for commercial use. I’m therefore disqualifying that one. Is the oddly lit (and extremely expensive) picture of the SOLiD 4 or the conceptually off-target picture of the Minion better than what the AI came up with? I don’t think I can conclusively say either way, and one thing that I dislike doing as a marketer is injecting my own opinion where it shouldn’t be. The scientists should decide! For now, this will be a tie.

AI 1, Stock 3, Tie 1

Round 6: A high-magnification fluorescent micrograph of neural tissues.

My PhD is in neuroscience so I love this round. If Science Photo Library doesn’t win this round they should pack up and go home. Let’s see what we get!

Round 6 AI Fails

Dall-E got a rough, if not slightly cartoony, shape of neurons but never really coalesced into anything that looked like a genuine fluorescent micrograph (top left and top center in the image below). Stable Diffusion, on the other hand, was either completely off the deep end or just hoping that if it overexposed out-of-focus images enough that it could slide by (top right and bottom row).

Round 6 AI vs. Stock

Midjourney produced a plethora of stunning images. They are objectively beautiful and could absolutely be used in a situation where one only needed the concept of neurons rather than an actual, realistic-looking fluorescent micrograph.

They’re gorgeous, but they’re very obviously not faithful reproductions of what a fluorescent micrograph should look like.

iStock didn’t produce anything within the time limit. I found high-magnification images of neurons which were not fluorescent (probably colored TEM), fluorescent images of neuroblastomas (not quite right), and illustrations of neurons which were not as interesting as those above.

Getty Images did have some, but Science Photo Library had pages and pages of on-target results. SPL employees, you still have jobs.

A small selection from page 1 of 5.

AI 1, Stock 4, Tie 1

Round 7: A colored scanning electron micrograph of carcinoma cells.

This is another one where Science Photo Library should win handily, but there’s only one way to find out!

Round 7 AI Fails

None of the AI tools failed in such a spectacular way that it was funny. Dall-E produced results which suggested it almost understood the concept, although could never put it together. Here’s a representative selection from Dall-E:

… and from Stable Diffusion, which as expected was further off:

Round 7 AI vs. Stock

Midjourney actually got it, and if these aren’t usable, they’re awfully close. As with the last round, these would certainly be usable if you needed to communicate the concept of a colored SEM image of carcinoma cells more than you needed accurate imagery of them.

iStock didn’t have any actual SEM images of carcinomas which I could find within the time limit, and Midjourney seems to do just as good of a job as the best illustrations I found there:

Getty Images did have some real SEM images, but the ones of which I found were credited to Science Photo Library and their selection was absolutely dwarfed by SPL’s collection, which again had pages and pages of images of many different cancer cell types:

It just keeps going. There were 269 results.

Here’s where this gets difficult. On one hand, we have images from Midjourney which would take the place of an illustration and which cost me less than ten cents to create. On the other hand, we have actual SEM images from Science Photo Library that are absolutely incredible, not to mention real, but depending on how you want to use them, would cost somewhere in the $200 – $2000 range per photo.

To figure out who wins this round, I need to get back to the original premise: Can AI replace stock in life science marketing? These images are every bit as usable as the items from iStock. Are they as good as the images from SPL? No, absolutely not. But are marketers always going to want to spend hundreds of dollars for a single stock photo? No, absolutely not. There are times when it will be worth it, but many times it won’t be. That said, I think I have to call this round a tie.

AI 1, Stock 4, Tie 2

Round 8: A ribbon diagram of a large protein showing quaternary structure.

This is something that stock photo sites should have in droves, but we’ll find out. To be honest, for things like this I personally search for images with friendly licensing requirements on Wikimedia Commons, which in this case gives ample options. But that’s outside the scope of the experiment so on to round 8!

Round 8 AI Fails

I honestly don’t know why I’m still bothering with Stable Diffusion. The closest it got was something which might look like a ribbon diagram if you took a massive dose of hallucinogens, but it mostly output farts.

Dall-E was entirely convinced that all protein structures should have words on them (a universally disastrous yet hilarious decision from any AI image generator) and I could not convince it otherwise:

This has always baffled me, especially as it pertains to DALL-E, since it’s made by OpenAI, the creators of Chat GPT. You would think it would be able to at least output actual words, even if used nonsensically, but apparently we aren’t that far into the future yet.

Round 8 AI vs. Stock

While Midjourney did listen when I told it not to use words and provided the most predictably beautiful output, they are obviously not genuine protein ribbon diagrams. Protein ribbon diagrams are a thing with a very specific look, and this is not it.

I’m not going to bother digging through all the various stock sites because there isn’t a competitive entry from team AI. So here’s a RAF-1 dimer from iStock, and that’s enough for the win.

AI 1, Stock 5, Tie 2. At this point AI can no longer catch up to stock images, but we’re not just interested in what “team” is going to “win” so I’ll keep going.

Round 9: A 3D illustration of plasmacytes releasing antibodies.

I have high hopes for Midjourney on this. But first, another episode of “Stable Diffusion Showing Us Things”!

Round 9 AI Fails

Stable Diffusion is somehow getting worse…

DALL-E was closer, but also took some adventures into randomness.

Midjourney wasn’t initially giving me the results that I hoped for, so to test if it understood the concept of plasmacytes I provided it with only “plasmacytes” as a query. No, it doesn’t know what plasmacytes are.

Round 9 AI vs. Stock

I should just call this Midjourney vs. Stock. Regardless, Midjourney didn’t quite hit the mark. Plasmacytes have an inordinately large number of ways to refer to them (plasma cells, B lymphocytes, B cells, etc.) and it did eventually get the idea, but it never looked quite right and never got the antibodies right, either. It did get the concept of a cell releasing something, but those things look nothing like antibodies.

I found some options on iStock and Science Photo Library (shown below, respectively) almost immediately, and the SPL option is reasonably priced if you don’t need it in extremely high resolution, so my call for Midjourney has not panned out.

Stock sites get this round. AI 1, Stock 6, Tie 2.

Round 10: An illustration of DNA methylation.

This is fairly specific, so I don’t have high hopes for AI here. The main question in my mind is whether stock sites will have illustrations of methylation specifically. Let’s find out!

Round 10 AI Fails

I occasionally feel like I have to fight with Midjourney to not be so artistic all the time, but adding things like “realistic looking” or “scientific illustration of” didn’t exactly help.

Midjourney also really wanted DNA to be a triple helix. Or maybe a 2.5-helix?

I set the bar extremely low for Stable Diffusion and just tried to get it to draw me DNA. Doesn’t matter what style, doesn’t need anything fancy, just plain old DNA. It almost did! Once. (Top left below.) But in the process it also created a bunch of abstract mayhem (bottom row below).

With anything involving “methylation” in the query, DALL-E did that thing where it tries to replace accurate representation with what it thinks are words. I therefore tried to just give it visual instructions, but that proved far too complex.

Round 10 AI vs. Stock

I have to admit, I did not think that it was going to be this hard to get reasonably accurate representations of regular DNA out of Midjourney. It did produce some, but not many, and the best looked like it was made by Jacob the Jeweler. If methyl groups look like rhinestones, 10/10. Dall-E did produce some things that look like DNA stock images circa 2010. All of these have the correct helix orientation as well: right handed. That was a must.

iStock, Getty Images, and Science Photo Library all had multiple options for images to represent methylation. Here are one from each, shown in the aforementioned order:

The point again goes to stock sites.

Final Score: AI 1, Stock 7, Tie 2.

Conclusion / Closing Thoughts

Much like generative text AI, generative image AI shows a lot of promise, but doesn’t yet have the specificity and accuracy needed to be broadly useful. It has a way to go before it can reliably replace stock photos and illustrations of laboratory and life science concepts for marketing purposes. However, for concepts which are fairly broad or in cases where getting the idea across is sufficient, AI can sometimes act as a replacement for basic stock imagery. As for me, if I get a good feeling that AI could do the job and I’m not enthusiastic about the images I’m finding from lower-cost stock sites, I’ll most likely give Midjourney a go. Sixty dollars a month gets us functionally infinite attempts, so the value here is pretty good. If we get a handful of stock images out of it each month, that’s fine – and there’s some from this experiment we’ll certainly be keeping on hand!

I would not be particularly comfortable about the future if I was a stock image site, but especially for higher-quality or specialized / more specific images, AI has a long ways to go before it can replace them.

"Want your products or brand to shine even more than it does in the AI mind of Midjourney? Contact BioBM and let’s have a chat!"

How to Write a Life Science White Paper

From the perspective of the marketer, a critical early task in the life science buying journey is education. It may even come before your audience of scientists recognizes they have a problem which needs a product or service to solve it. Once you have piqued their interest and seeded an idea in their minds, you need a lot more to get them across the finish line. Sometimes, a longer-form method of communication is merited, and that’s where the white paper comes in.

The Life Science Buying Journey

For those who are relatively new to this website, it should be expressed that I’m largely an adherent to Hamid Ghanadan’s viewpoint of the scientific buying journey, which views scientists as inherently both curious and skeptical. It’s illustrated in detail in his excellent book Persuading Scientists which is well-deserving of the long-overdue shout out. I’ve captured some of the concepts in a previous post: “The Four Key Types of Content.” To give the oversimplified TL;DR version of both:

  • The default state of scientists is curious. They readily take in information.
  • As they take in new information, they form ideas about it and transition from being curious to being skeptical.
  • If they cannot validate the information, they generally reject it.

You can see how a buying journey fits into this mindset:

  • The scientist is presented with a new idea.
  • As they learn more about this idea, they realize that they may need a product or service.
  • The critically evaluate the product(s) / service(s) presented to them.
  • A decision is made.

The goal of the marketer is to seed the scientist’s curiosity, continuing to provide them with information which will shape their viewpoint in your favor without engaging skepticism too early. That is how you maximize your chances of a positive purchasing decision.

Understanding What a White Paper Is … and Isn’t

A white paper is intended to provide either educational content (helpful, customer-centric information) or validation content (information which verifies a belief that the customers hold or a claim that the brand is making which may be customer-centric or product-centric). In either situation, the primary purpose is to inform your audience. Novice marketers may consider the format (usually pdf) and conflate a white paper with a brochure but they are two very different things.

All marketing documents exist on a rhetorical sliding scale between being fully informational and fully promotional. A brochure would be far onto the promotional side of that scale; it is extremely product-centric and its purpose is largely to encourage a purchase. A white paper would be most of the way towards the informational side of that scale. Creating a white paper which is overly promotional risks engaging the scientists’ skepticism before they have adopted your viewpoint, creating a situation where their inclination is to disbelieve you. This situation generally results in them rejecting your offering.

Writing Copy for an Effective White Paper

Your white paper should be about:

  • a single topic
  • which is of interest to your audience
  • of which you know substantially more than your audience

This may seem simple, but framing it can be difficult.

Presumably, your company is in the business of solving some type of problems for life scientists. They might not know what their problem is, but you do. Why should they care? Why is what you are doing compelling? You almost certainly have answers to these questions, but you likely have them framed in the context of your product. How can you take those answers and communicate them in a manner which is customer-centric instead of product-centric? Start by talking about your scientist-customers’ problem rather than your solution and you’ll be headed in the right direction.

There are times when a more product-focused white paper can be appropriate, however. For instance, you may have a new technology which is unfamiliar to your audience and you need to educate them about it. In this case, you have to talk about your solution to some extent. When that is the case, be sure to focus on providing information about the technology, not promotion for the product. You need to take care to ensure the information is objective, communicated in a unbiased manner, is well-referenced with independent sources, and uses independent voices (e.g. voice of the customer) wherever an opinion is necessary.

Formatting a White Paper Effectively

There is no particular length restriction on a life science white paper, but if you are calling it a white paper, your audience is likely expecting it to be somewhat in depth. A two-page minimum for a white paper is a good guideline to adhere to. For much longer white papers, you should consider yourselves constrained by your ability to maintain your audience’s attention. Demonstrating your expertise does not mean writing more than you need to. As is almost always the case, less is more. Be as concise as you can while fully communicating your point.

Avoid walls of text. Too many words and not enough visuals will make your audience less likely to get through your content. Use illustrations where possible, and don’t feel bad using relevant stock imagery to break things up. Ensure the document isn’t boring to the eyes by using brand-relevant colors, shapes, iconography, and other visuals. Ideally, you should have a generalized white paper format which you maintain throughout all of your documents to provide consistency. You want people who read your white paper to know it is your brand’s white paper, even if they didn’t see a logo.

Circling back on what a white paper is and isn’t, you’ll recall that we need a primarily informational document. However, you might not want an entirely informational document. Your job is to sell things, and purely informational things are generally not great at selling. You want to sprinkle some promotion in there. But how? Through creative use of formatting! You don’t want people to become skeptical of the information you are providing them in the body of the white paper, so don’t put promotional content in the body of the white paper! Use clearly-delineated sections to cordon off your promotional content. Help prevent skepticism of your promotional messages by using voice-of-customer (testimonials, etc.) whenever possible. You can also leave your promotional messages to when customers will most expect it – the end of the document. Like almost all effective marketing documents, you don’t want to leave out the call-to-action!

This is a stock image of life science brochure templates and doesn’t say anything meaningful at all, but you probably stopped to look at them because they’re visually appealing.

Deploy Your White Paper Effectively

Far too often, life science companies will write a really good white paper then tuck them off in some remote corner of their website. You have it, use it! Post about it on social media (more than once!), put it somewhere on your website which is relevant but readily findable by anyone looking for that kind of information, and blast it out in an email to a well-segmented section of your audience. If appropriate, use it as the hook for a well-targeted paid advertising campaign. The worst thing you can do after spending the time and resources to create a white paper is to only have a few dozen people ever read it.

Presumably you’ll be using your white paper to generate leads and will therefore have it gated with a download form (although you certainly don’t have to). If it is gated, create a compelling download page for your white paper which previews just enough of the content to make the audience want more but without giving up its most important lessons.

Recap on Effective Life Science White Papers

To write an effective white paper:

  • Understand where your white paper fits within the customer journey.
  • Maintain its primarily informational purpose.
  • Keep to one topic which will be of interest to your audience.
  • Focus on information which most of your audience likely will not know.
  • Allow what you have to communicate to dictate the length.
  • Don’t skimp on the visuals.
  • Clearly separate any promotional messages to avoid creating skepticism about the core topic.
  • Shout it from the rooftops to get attention to it!

White papers are centerpieces of many life science demand generation campaigns. By understanding and implementing these guidelines, they can help drive successful lead generation for your life science company as well.

"Not sure how to best deploy content to help fuel your marketing efforts? Experiencing writer’s block? Don’t spend time fretting, just contact BioBM. Our life science marketing experts are here to help innovative companies like yours craft purposeful, effective content to influence your scientist-customers and encourage them into action."

Stop Hosting Your Own Videos

I know this isn’t going to apply to 90% of you, and to anyone who is thinking “of course – why would anyone do that?” – I apologize for taking your time. Those people who see this as obvious can stop reading. What that 90% may not know, however, is that the other 10% still think, for some terrible reason, that hosting their own videos is a good idea. So, allow me to state conclusively:

Hosting your own videos is always a terrible decision. Let’s elaborate.

Reasons Why Hosting Your Own Videos Is A Terrible Decision:

  1. Your audience is not patient. If you think they’re going to wait through more than one or two (if you’re lucky) periods of buffering, you’re wrong. Videos are expensive to produce. If you’re putting in the resources to make a video, chances are you want as much of your audience as possible to see it. Buffering will ensure they don’t.
  2. Your servers are not built for this. Your website is most likely hosted on a server which is designed to serve up webpages. Streaming video content is probably not your host’s cup of tea. In fact, they’d probably rather you not do it (or tell you to buy a super-expensive hosting plan to accommodate the bandwidth requirements of streaming video).
  3. Your video compression is probably terrible. Your video editing software certainly will export your video into a compressed file. “Compressed,” in this sense, means not the giant, unwieldy raw data file that you would otherwise have. It does not mean “small enough to stream effectively.” You know whose video compression is next-level from anything else you’re going to find? YouTube, Vimeo, or probably most other major services that stream video on the internet as a business.
  4. There are companies that do this professionally. When I was in undergrad and majoring in chemical engineering, the other majors jokingly referred to us as “glorified plumbers,” but I don’t touch pipes. I don’t know the first thing about plumbing. So what do I do when I get a leak? I call a plumber, because they’ll definitely solve the problem far better than I would. Likewise, if you want to host video, why not get a professional video hosting service? There’s plenty of them out there, including some that are both very reputable and inexpensive.

An Example

I’m at my office on a reasonably fast internet connection. It’s cable, not fiber optic, but it’s also 11:30 in the morning – not prime “Netflix and chill” time when the intertubes are clogged up with people binge watching a full season of House of Cards. Just to show you that any bandwidth problems aren’t on my end, I did an Ookla Speedtest:

The internet is fast.

239 Mbps. Not tech school campus internet kind of fast, but more than fast enough to stream multiple YouTube videos at 4k if I wanted to.

And now for the example… I’m not going to tell you whose video this is, but they have an ~1 minute long video to show how easy their product is to use. Luckily for me, they don’t have a lot of branding on it so I can use them as an example without shaming them. The below screenshots are where the video stopped to buffer. Note that the video was not fullscreened and was about 1068 x 600. You can click the images to see them full size and see the progress bar and time at the bottom.

Made it 18 seconds! Off to a slightly less than disastrous start…

28 seconds. Getting there…

Well that didn’t go far. 32 seconds.

37 seconds. There’s no way I’d still be watching this if I wasn’t doing this for the purposes of demonstration.

42 seconds…

51 seconds! Almost there!

“Done” … or not quite done. 56 seconds. I don’t even know why it stopped to buffer here as almost the entire rest of the video was already downloaded.

The video stopped playing 7 times in the span of 64 seconds.

What To Do Instead

Perhaps the most well-known paid video hosting service, Vimeo has a pro subscription that will allow you to embed ad-free videos without their branding on it for $20 / month. There’s a bunch of other, similar services out there as well. Or, if you don’t want to spend anything and don’t mind the possibility of an ad being shown prior to your video, you can just embed YouTube videos. The recommended videos which show after playback can be easily turned off in the embed options. You can even turn off the video title and player controls if you don’t want your audience to be able to click through to YouTube or see the bar at the bottom (although the latter also makes them unable to navigate through your video).

Basically, if you want your videos to actually get watched, do anything other than hosting them yourself.

P.S. – If you’ve read all this and still think hosting your own videos is the correct solution, which it’s not, here’s a tip: upload them to YouTube, then download them using a tool like ClipConverter. This way you’ll at least get the benefit of YouTube’s video compression, which is probably the best in the world.

"Want marketing communications that truly captivate and engage your customers? It’s time to contact BioBM. Our life science marketing experts are here to help innovative companies better reach, influence, and convert scientists."

We Just Got Skyscrapered

Just yesterday, we got skyscrapered. No, we didn’t get an office in a giant building or fly an ad from one or anything like that, nor is that some weird pop-culture thing that teenagers are putting on YouTube. We were the target of an attempt at “skyscraper marketing” … and I’m talking about it, so I guess it worked in a sense.

I’ll talk more about this particular instance in a moment, but first I wanted to give an intro to skyscraper marketing for anyone who isn’t familiar with it.

The “What” and “Why” of Skyscraper Marketing

Skyscraper marketing was one method which was popularized after Google’s 2013 Hummingbird algorithm update. To summarize the implications of that in brief: there was once a time when you could “trick” Google into thinking that your website was more important than it was by posting links around the internet pointing to your website. Hummingbird was the Google update that put an end to that once and for all and penalized websites that did not comply. From then on, if you wanted to prove your website’s importance (and thereby improve your search ranks), you needed to earn your backlinks organically.

That’s about the time when content marketing became more important. From that point, not only was it the validation that showed prospects you knew what you were talking about, but it was the primary tool at your disposal to influence your search rankings (beyond the basic on-site optimization, such as optimized URLs and title tags, that everyone does and therefore isn’t a real source of competitive advantage). The more shareable the content, the more backlinks it would likely get, and therefore the better it was for SEO.

Thus, Skyscraper Marketing was devised. At its most basic, I can break it down into a three step process:

  1. Find successful content.
  2. Improve upon it.*
  3. Share it with people who would be interested in it and, in turn, share it themselves.

*The necessity for improvement is debatable, but you do have to do something to it. More on that in a moment…

The “How” of Skyscraper Marketing

Skyscraper marketing is, essentially, a type of influencer marketing in that the important part is the last step – getting people with engaged audiences to share it. That being the case, there are two primary approaches (and you don’t have to choose between them – you can do both at the same time).

The first approach is the incremental improvement approach. You find some good content which you have something to add to / make better / pose a counterpoint to / etc., then distribute it to a bunch of people who would find it relevant and potentially want to share it. In this approach, you’re adding something to the general body of knowledge in the hope that your contributed insight is enough to make it a worthwhile share – especially from people who have large audiences themselves. Again, the goal is to get as many backlinks and as many eyeballs as possible (those goals do overlap) so the more people you reach out to the better.

The second approach is the “stroking one’s ego” approach. In this approach, your goal isn’t necessarily to improve upon good pieces of content, but rather to act as an aggregator. You take really good tidbits from the thinking of a number of different influencers, and repackage them into a single, easily digestible, and readily shareable piece of content, being sure to reference and link to the authors / posts whose thinking you aggregated. You then reach back out to those people and let them know that you published something which referenced them. People, being generally inclined towards things that make themselves seem important, will share your article which highlights their own thinking.

BioBM’s Skyscraper Marketing Tips

As with influencer marketing, you want to take care to do it correctly. If you don’t, you’ll not only waste your time and effort, but you’ll also get a reputation among the influencers in your market as a peddler of junk content. If that happens, skyscraper marketing or other forms of influencer marketing will be more difficult for you in the future. Just as poor quality content can reflect badly upon your brand, asking people to share poor quality content will erode your relationships with those influencers.

To not be “that guy,” here are some useful tips:

  • Don’t spam your network. Only send out good content and only send it to people who would find it genuinely relevant.
  • Don’t plagiarize copy … or ideas. If people realize they’ve heard it all before elsewhere, they probably won’t share it.
  • Note that “improved content” does not mean “longer content.” A lot of people have a habit of focusing on expanding upon an idea rather than improving upon it. Improvement is far more important than expansion. If you make something better or take a novel perspective on an idea, that’s far more worthy of sharing than simply adding more of the same.
  • “Improved content” also doesn’t mean that you need to improve on the idea itself. Communicating it more effectively – for instance, using illustration to more clearly demonstrate a complex point – can be just as valuable.
  • Always remember: your content behaves like a product and must be differentiated!
  • If you’re going to take an ego-driven approach, be sure you show that you have taken the time to fully understand and eloquently explain the idea, and give some praise to the original author without coming of as a flatterer.

So to finish the story…

Upon checking our social media dashboards this morning, I saw this tweet:

I’ve been published more than the average person, but that’s still enough to get my attention so I gave it a quick read through. I ended up not sharing it on our @BioBM twitter account (and I don’t use my personal @CHoytPhD twitter anymore) for a few reasons. Primarily, we have very high standards for what BioBM publishes through our channels. We generally require there to be some element of newness, and we didn’t find there to be any particularly fresh thinking. (Sorry, Joe! No offense intended.) Secondarily, it was a really obvious skyscraper attempt, especially since our idea which was shared wasn’t strongly relevant to the body of the article and was simply one of many listed in bullet point format towards the end. On the other hand, Joe did well not to plagiarize the ideas which he referenced, but rather offered a tidbit of them with a link to the source. That was nice of him. (Thanks, Joe!)

That said, it did engage a discussion on twitter and his post did end up being linked to on our blog, so I suppose Joe can claim victory after all. He’s also welcome to follow this shameless promotion for our “Marketing of Life Science Tools & Services” LinkedIn group and post it there as well. 2262 members and counting!

Just for fun, and because who doesn’t love architecture, here’s a few more images of skyscrapers. All images are courtesy of Unsplash, which in an amazing feat of generosity allows their beautiful, high-resolution images to be used for any purpose and without attribution. I find that so awesome that I’m giving them attribution anyway.


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Why People Are Loyal … to ANYTHING

I was reading the MarketingCharts newsletter today and saw a headline: “What Brings Website Visitors Back for More?” The data was based on a survey of 1000 people, and they found the top 4 reasons were, in order:
1) They find it valuable
2) It’s easy to use
3) There is no better alternative for the function it serves
4) They like it’s mission / vision

Website Loyalty Data from MarketingCharts.com

I thought about it for a second and had a realization – this is why people are loyal to ANYTHING! And achieving these 4 things should be precisely our goal as marketers:
1) Clearly demonstrate value
2) Make your offerings – and your marketing – accessible
3) Show why your particular thing is the best. (Hint: If it’s not the best you probably need to refine your positioning to find the market segment that it is the best for.)
4) Tell your audiences WHY. Get them to buy into it. Don’t just drone on about the what, but sell them on an idea. Captivate them with a belief!

Do those 4 things well, you win.

BTW, the MarketingCharts newsletter is a really good, easy to digest newsletter – mostly B2C focused but there’s some great stuff in there even for a B2B audience and you can get most of the key points in each day’s newsletter under a minute.

"Captivate your customers’ loyalty. Contact BioBM and let’s turn your marketing program into a strategic advantage."

Personalization Can Backfire

Marketers are used to seeing a lot of data showing that improving personalization leads to improved demand generation. The more you tailor your message to the customer, the more relevant that message will be and the more likely the customer will choose your solution. Sounds reasonable, right?

In most cases personalization is great, but what those aforementioned studies and all the “10,000-foot view” data misses is that there are a subset of customers for whom personalization doesn’t help. There are times when personalization can actually hurt you.

When Personalization Backfires

Stressing the points which are most important to an individual works great … when that individual has sole responsibility for the purchasing decision. For large or complex purchases, however, that is often not the case. When different individuals involved in a purchasing decision have different priorities and are receiving different messages tailored to their individual needs, personalization can act as a catalyst for divergence within the group, leading different members to reinforce their own needs and prevent consensus-building.

Marketers are poor at addressing the problems in group purchasing. A CEB study of 5000 B2B purchasers found that the likelihood of any purchase being made decreases dramatically as the size of the group making the decision increases; from an 81% likelihood of purchase for an individual, to just 31% for a group of six.

For group purchases, marketers need to focus less on personalization and more on creating consensus.

Building Consensus for Group Purchases

Personalization reinforces each individual’s perspective. In order to more effectively sell to groups, marketers need to reinforce shared perspectives of the problem and the solution. Highlight areas of common agreement. Use common language. Develop learning experiences which are relevant to the entire group and can be shared among them.

Personalization focuses on convincing individuals that your solution is the best. In order to better build consensus, equip individuals with the tools and information they need to provide perspective about the problem to their group. While most marketers spend their time pushing their solution, the CEB found that the sticking point in most groups is agreeing upon the nature of the solution that should be sought. By providing individuals within the groups who may favor your solution with the ability to frame the nature of the problem to others in their group, you’ll help those who have a nascent desire to advocate for you advocates get past this sticking point and guide the group to be receptive of your type of solution. Having helped them clear that critical barrier, you’ll be better positioned for the fight against solely your direct competitors.

Winning a sale requires more than just understanding the individual. We’ve been trained to believe that personalization is universally good, but that doesn’t align with reality. For group decisions, ensure your marketing isn’t reinforcing the individual, but rather building consensus within the group. Only then can you be reliably successful at not only overcoming competing companies, but overcoming the greatest alternative of all: a decision not to purchase anything.

"Looking to improve how you communicate with your market? There are only so many minutes in the day and effective communications must first successfully fight for those minutes, then deliver a message that resonates. The power to captivate is what will bring you a greater share of attention, and you can only win the customers who are paying attention to you. BioBM is here to help you win – at every step. We ensure that you win market share through winning and maintaining another important share: share of attention. The days of marketing by interruption are fading away. The days of marketing by captivation have arrived. These days can be yours. Seize them."

The End Is Not Nigh (now let’s get serious…)

People love to decry the end of marketing. It’s a good attention-getter. While those who shout about the coming of the end of marketing from their soapboxes are usually guilty of lacking realism or using poor logic, they do make us think about the future and that can be a learning experience. Let’s take an example…

Knowledge @ Wharton recently published an interesting, albeit narrow-sighted and overly apocalyptic article about the end of marketing and what, according to the author, will be the very narrow opportunities to engage audiences that will remain in the future. The author does a very good job of identifying trends but a very bad job of predicting what the future will likely look like, but both the good and the bad provide important lessons and highlight valuable opportunities.

First, the trends. No reason to discuss these much because most should be more or less obvious to anyone reading this.

  1. People would rather listen to other people than brands.
  2. People are going to greater lengths to avoid the onslaught of advertisement.
  3. Marketing technology “cannot truly understand the complexities of consumer intent” and therefore hitting the trifecta of the right message on the right channel at the right time is exceedingly difficult. (This I would actually say is up for debate. It’s a gray area. A discussion for another time, perhaps…)
  4. Marketers are overwhelming digital channels, further driving users to avoid marketing out of simple necessity. See point #2.

And here are the author’s four corresponding points of how he envisions the future:

  1. “As consumers bypass media with greater ease, the social feed is the wormhole to the entire online experience.”
  2. “As consumers outcompete marketers for each other’s attention, every piece of media contained in the feed is not only shareable, but shoppable.” – basically, he’s arguing that social channels become capable of performing transactions.
  3. “As the individual controls the marketing experience, communication shifts from public to semi-private.” In other words, people move from things like Facebook to things like Snapchat, where there are fewer ads and more privacy.
  4. Only two types of marketing will remain: discounts / sales and transparent sponsored content.

These predictions amount to a wild fantasy.

The most obvious flaw in the author’s reasoning is that somehow a completely shoppable social media ecosystem would evade the rules that everyone else has to play by – namely that when marketing becomes overwhelming, the audience will block it out or leave. This also ignores the plain fact that the large majority of the things that people buy are not found organically via social media. There is no shortage of people who shop. Decisions may be influenced in the social sphere, and perhaps some impulse decisions both begin and end there, but those are the exception; the overwhelming majority of purchasing decisions do not occur entirely within the social sphere and that would not change if social channels were empowered with transactability.

The real world contains a great deal of equilibrium. The ability to target people and their ability to tune it out is a balancing act. It is a cat and mouse game. Technology works both ways, and as new channels and technologies are born there become more ways to reach customers. However, as channels are flooded, the impact of each individual effort diminishes. Marketing self-regulates by decreasing its own ROI as utilization of any particular channel increases.

So What Will the Future of Marketing Look Like?

There are definitely many channels that will continue their trend towards ineffectiveness. It’s increasingly likely that audiences, fed up with maddening digital display advertising techniques, continue to adopt ad blocking technology and erode the potential of that channel. Email, while still rated as a high-ROI channel, is looking like it may have a perilous future as email service providers become better at filtering out promotions. Social media will certainly take on a larger share of permission-based marketing, but it will remain a risky business to rely too much on “rented” audiences. Increasing utilization of content marketing will continue to add noise and, in turn, increase its own cost by requiring better and better content to obtain the inherently limited resource it seeks to obtain: the audience’s attention. Increased use of social media may, if adoption increases as we project, fall victim to a similar effect, limiting brands’ ability to market effectively using social channels.

Not all developments will be bad. A decline in interruption tactics will lead to a fundamental shift in how marketing is viewed from a tool to generate demand to a mechanism to deliver value to audiences and a source of strategic advantage. Customer-centric resources and other owned platforms will proliferate as companies seek new ways to deliver value to customers while increasing the affinity level between customer and brand. These companies with strong brand affinities will create sustainable advantage for themselves as they shortcut and compress the customer decision journeys. Additionally, new and yet unknown channels will develop, and at increasingly rapid pace. Consider that until about 20 years ago, no digital channels existed at all. Accelerating technology development will continue this trend and also enable more personalized, coordinated, and targeted marketing in a manner which is more accessible and usable by companies of all sizes, budgets and capabilities.

I’m not going to try to pinpoint detailed specifics – I’m not claiming to be a psychic and it would be a waste of your time to read simple conjecture – but there are things that we can be fairly certain of given current trends, a bit of logic, and a hint of foresight. Marketing isn’t going anywhere, and while in the future it may not look quite like it does today, it will still be something that Philip Kotler would distinctly recognize.

"Marketing is a race, but unlike the 200 meter sprint there aren’t any referees that will call you for a false start. Get a jump on your competition, charge forward on the path to market domination, and start leveraging the next generation of marketing strategies today. Work with BioBM."

If You Don’t Own Your Channels, You Don’t Own Your Audience

A very telling thing happened in October. YouTube, in preparation to release it’s paid subscription service, Red, told its top content creators that “any ‘partner’ creator who earns a cut of ad revenue but doesn’t agree to sign its revenue share deal for its new YouTube Red $9.99 ad-free subscription will have their videos hidden from public view on both the ad-supported and ad-free tiers.” (ref: TechCrunch). In other words, if content creators who are getting revenue from their YouTube videos don’t agree to Red, their channel will go dark. All those subscribers will mean nothing if they can’t access your content.

This should be something of a reality check for marketers. On YouTube or any third party social channel, your audience doesn’t belong to you; it belongs to the channel. Those Twitter followers? Twitter owns them. All those Facebook likes? They’re not your property, they’re Facebook’s. Any one of those channels can do anything they want with them at any time. Feel insecure? It is.

What if Facebook removed access to people who have liked your page unless you pay for engagement? There’s no reason they couldn’t. Or what if the social network that you’ve poured so many resources into in order to develop a large following were to fade away – perhaps people start abandoning Twitter en masse for Snapchat (or whatever comes after Snapchat)?

You don’t own your social media audiences. In many cases, you don’t even own the content you’ve shared on that social channel. You definitely don’t own your advertising audiences or any other audience which is rented. Any and all of these audiences can be taken away. If you’re looking to develop an attentive and loyal audience that’s both engaged and secure, what can you do?

Building an Owned Audience

Building an owned audience requires that you create a platform for audience growth which is under your full control. Any audience on a “rented” channel belongs to the channel and not to you.

Building an owned audience also requires that the channel you create offer sufficient value such that people want to engage with it and return to it. Getting someone to hit the “follow” button on a social platform is very non-committal. Getting someone to sign up for an entirely new platform is a higher bar. You need to ensure that you sufficiently understand and address genuine audience needs in order to for them to commit. Furthermore, unlike social media, you need to provide enough value that the audience will go back just for the value your owned platform provides; there won’t [necessarily] be many other people and brands drawing them back into it, giving them reasons to return and engage. While yours may be just one of 100 liked pages and 500 friends competing for space on a Facebook user’s feed, that may be enough to provide you with the opportunity to grab for their attention. There may be a lot of competition for that attention, but there are also many reasons for the users to continuously return to the channel; the burden of reeling the audience back in is widely distributed among their many connections and the platform itself. On an owned channel, you must make it entirely your responsibility to entice to engage and continue to reengage over time, but each time they do you own that attention. You write the rules.

That begs the question: What do you need to do to build an owned channel?

The form that the owned channel takes is irrelevant. The form should simply be a response to audience needs. It can be as simple as a blog or as complex as anything you or I could imagine. There are only three requirements:

  1. It has to provide genuine value to the target audience. That’s what is going to attract their attention. Understand what problems your customers are having and focus on helping to solve them. Your platform has to be primarily about your customers.
  2. The value has to be sustained over time. An audience that only pays attention once doesn’t do you much good. While the audience itself can sometimes be leveraged to add value to the platform, don’t plan on it happening. Expect that you’re going to have to be the one to continue to add value to the platform over time. If that seems like an unsustainable effort, it may be time to go back to the drawing board.
  3. It has to meaningfully connect the audience with your brand.

By creating a platform which enriches the lives of members of your target market, you’ll find yourself growing a willing, captive, and secure audience – on your terms.

"Future-proof your marketing and create a strategic advantage by having “owned” audiences that turn the tides of customer sentiment to your advantage. Scientists are slow to change their loyalties, but if you can consistently provide the best value – the best experiences – your brand will become the default selection above which others must prove their worth.

Own the incumbent advantage. Contact us."