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:
- A female scientist performing cell culture at a biosafety cabinet.
- An Indian male scientist working with an LC-MS instrument.
- An ethnically diverse group of scientists in a conference room holding a lab meeting. One scientist presents their work.
- A close up of liquid dripping from pipette tips on a high-throughput automated liquid handling system.
- An NGS instrument on a bench in a genomics lab.
- 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.
- 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 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
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…
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.
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.
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.
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:
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.
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.
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.
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:
- 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.
- 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).
- 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.
- 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.
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:
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.
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.
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
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.
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.
There’s a lot of noise coming from some fairly reputable sources extolling the virtues of publishing as the next generation of content marketing (I’m sure you’ll be very familiar with this if you follow the Content Marketing Institute at all). For instance, let’s take a look at a recent article from the Harvard Business Review website – “Content Is Crap, and Other Rules for Marketers” – which makes some great points, but misses some equally if not more important points.
To begin, let’s summarize his 4 rules, which are all extremely valid points…
Rule 1 – Recognize that content is crap. This is best highlighted by the author: “We never call anything that’s good ‘content.’ Nobody walks out of a movie they loved and says, ‘Wow! What great content!’ Nobody listens to ‘content’ on their way to work in the morning. Do you think anybody ever called Ernest Hemingway a ‘content creator’? If they did, I bet he would punch ‘em in the nose.” He goes on to state that marketers need to be more like publishers.
A bit of a side note before we move on. The author is appealing to emotion a bit and is forgetting that content is a somewhat technical term – no one says they drink “dihydrogen monoxide” either. What this is more illustrative of is the mentality of many content marketers. What’s important isn’t, for example, that the people who watch great movies don’t refer to it as “content” but that the producers, writers, directors, and actors who set out to make a great movie don’t refer to it as content. It’s the mentality of content – making “stuff” that begs for attention – which gets people stuck in a losing paradigm and it’s a paradigm that needs to be dropped.
Rule 2 – Hold attention, don’t just grab it. “Marketers need to build an ongoing relationship with consumers and that means holding attention, not just grabbing it. To get people to subscribe to a blog, YouTube channel, or social media feed, you need to offer more than a catchy slogan or a clever stunt. You need to offer real value, and offer it consistently.” The author argues that publishing solves this problem.
Rule 3 – Don’t over-optimize metrics. It’s too easy to confuse measurement with meaning. He uses the example of Buzzfeed, who no longer uses clickbait titles as they’ve realized that they optimize for pageviews, which are just clicks, but betray the reader’s trust. By under-promising and over-delivering, you create more engagement with the content and make it more likely that the reader will return to read another article later. It’s the long game vs. short game conundrum. You can make the numbers look good if you pretend not to care about your numbers a year from now.
Rule 4 – Understand that publishing is a product, not a campaign. In brief, the author makes the point that one of the keys to being successful in being more like a publisher is to treat it with more permanence and seriousness.
There are some great points here… Content is not enough. You can’t simply interrupt your way to success; you need a way to build an audience. Ensure your metrics are effectively measuring value creation. And publishing has serious merits, but the answer is bigger than publishing.
The Inherent Problems With Publishing
Yes, publishing is often superior to more basic forms of content marketing, but it’s not for everyone. Not every company has some amazing, inherently compelling story to tell, and not every company has the resources to continually deliver pieces of that story through carefully crafted content consistently over a long period of time. That’s a massive effort. Assuming publishing is a magic bullet ignores reality and ultimately falls victim to the same problems plaguing other iterations of content marketing: if it becomes well adopted, it’s very quickly going to become much more difficult to do effectively.
The audience’s attention is inherently limited, and while publishing tries to occupy more of that attention, it doesn’t solve the attention problem and it falls into the same trap as more “generic” forms of content marketing. It’s actually a natural response to the lack of supply of customer attention which follows basic economic principles: If the supply of something is limited and demand increases the result is an increasing cost. As more and more content competes for limited attention the “cost” of the customers’ attention increases, meaning you need higher quality content to obtain it. Treating content marketing more like publishing doesn’t change that fact, it simply throws more resources at the problem so higher quality content can be produced – a necessity to continue to compete for customers’ attention in an environment where it is in ever-increasing demand. It’s not like audiences couldn’t do things such as subscribe to blogs almost two decades ago, it’s simply that it takes a better content effort to grab and hold attention than it used to.
Should You Be a Publisher?
Publishing cannot be the answer for everyone. It is literally impossible for 100% of brands to be successful publishers because the audience does not have enough attention to go around. How can you tell if you should be a publisher? Answer these two questions:
- How interesting are you? Take a good honest look at your brand and figure out how interesting you are. Some have great stories to tell. Some do amazing things. Some would make highly impactful thought leaders. Others simply aren’t so captivating. If your brand simply isn’t all that interesting compared to others in your space, you might want to consider something else.
- Can you – and will you – sufficiently resource the effort? Putting out top-quality content on a regular basis is no easy job by itself, and publishing requires more than that. The amount of time and resources that will need to go into planning, editing, graphic design, etc., will be significantly greater. At the same time, publishing still won’t provide a short-term payoff. Do you have the resources and the necessary leadership buy-in to be a publisher?
The Real Focus
If you’re not in the upper echelon of brands with regards to your ability and willingness to be a publisher, all is not lost. After all, being a publisher is not the goal. The reason that taking on the role of publisher is being touted as superior to content marketing is because it’s more effective at delivering meaningful value to customers. That’s also the underlying reason why it better holds the audience’s attention. At the end of the day customers gravitate to value, and there’s a lot more ways to provide value than just being a publisher.
Shift your paradigm from thinking about content to developing actual resources that solve genuine customer problems. Ask yourself what problems customers are having that they might not pay for a solution to, but are readily solvable with a bit of time and effort. Analyze them, prioritize them, and solve the most critical ones that provide the best opportunity for long-term value creation and evolving the customer relationship beyond a transactional one.
Double down on customer experience. Make it easier, faster, and simpler for customers to obtain value from you. Look at some of the juggernauts of tech – Google, Facebook, Uber, Amazon – they didn’t get to where they are because of content marketing. Most of their content marketing efforts aren’t even on people’s radar. What they do is solve problems quickly and simply. You know what’s a great experience? When you can type a question and an answer appears, when you press a button and a cab simply shows up, or when you can instantly be connected to any of your friends. There’s are myriad examples out there, and while it may be easier to do in tech than in the life sciences, it’s certainly not impossible in any industry.
If you’re existing content marketing efforts are becoming less effective, one option is certainly to hunker down, take it more seriously, and spend the resources to become a highly effective publisher. But that’s expensive, difficult, and only delays the onset of many of the underlying problems plaguing content marketing. Publishing treats the symptoms, not the disease. Rid yourself of all paradigms but the one which relies on this one fundamental truth: customers will favor those brands which contribute the most value to their lives. Let that reality guide your actions and you’ll soon find your audiences flocking to you.
The fear of loss is stronger than the desire for gain.
This is a scientific fact. Here’s the first paper that describes it, but there are a lot more which confirm it. It’s known as loss aversion, and it makes both us and our customers irrational.
Loss aversion is, for instance, why challenger marketing works so well. Lots of companies talk about benefits – what customers have to gain by using your product or service – but customers respond better if you can convince them that the way they are currently doing things is wrong. Tell them that they are currently experiencing loss and they’ll more likely act in your favor. (Don’t just take it from me – you can ask the Corporate Executive Board.)
Challenger marketing is underutilized, however. Why? Simple. Loss aversion. Most marketers are scared of being negative. They think – without any proof to support it – that communicating a thought which could be perceived as negative will turn customers off and cause a blowback on their brands. They are afraid of making people upset more than they desire gains. This persists and directs action even in spite of evidence that being negative at times can provide positive results.
An even more critical and fundamental area where loss aversion cripples marketers is in positioning. Marketers, and the corporate honchos that preside over them, love to cast wide nets. They just love to pretend that everyone is a potential customer. When that becomes the default scenario, we find ourselves in a dangerous position. Loss aversion makes us scared to cut out pieces of the market, that’s not what makes positioning an effective tool. Wide nets don’t win.
Positioning is about defining who is and who isn’t a target customer. We want to maximize the chance that we’re going to close opportunities. We do that not by casting the widest net, but by resonating with those our net is designed to catch. Those are the people we should want to sell to – not the masses who will suck up our marketing dollars and sales efforts but have little chance of converting. That requires putting your loss aversion aside and cutting out your true piece of the market – that which you are realistically and effectively able to capture.
Loss aversion is a powerful tool for marketers, but the same thing that makes it so useful can be harmful when it manifests in ourselves. Don’t just understand the psychology of your scientist-customers, but understand your own psychology as well. You’ll make better decisions as a result.