Are you able to run a social media account solely utilizing AI?
I just lately took a while off work and thought I’d use the chance to reply this query.
See, it’s arduous developing with new content material persistently, however I’m a little bit of a purist with regards to what I share on social media and the way I share it. We’ve even coated this earlier than whereas addressing how the Buffer Content material workforce makes use of AI in our artistic course of.
However I believed, for science, I’d do that experiment and reply one of many AI questions that creators is likely to be deeply interested in: how does AI content material on LinkedIn carry out?
What I did and the experiment parameters
I centered on a weeklong interval, scheduling all content material by means of Buffer. This strategy allowed me to research the efficiency of AI-generated content material, which I did utilizing the stats LinkedIn makes out there.
For this experiment, I centered my efforts solely on LinkedIn, the place I’ve been constructing my private model, over a one-week interval from November sixth to November twelfth, 2023, and I used LinkedIn’s platform analytics to trace and compile the efficiency information of the AI-written content material.
To generate the posts, I used three main AI instruments to not appear biased – Buffer’s AI Assistant, Claude AI, and ChatGPT. Additionally, these instruments particularly gave me the most effective probability of making content material to a sure stage of high quality I hoped for.
To maintain the experiment parameters strict, 100% of the content material was AI-generated based mostly on varied prompts. The one exception was some mild proofreading earlier than publishing.
I assigned each the duty of drafting content material for a set variety of days:
- Buffer’s AI Assistant – three days of LinkedIn posts
- Claude AI – two days of LinkedIn posts
- ChatGPT – two days of LinkedIn posts
Whereas I attempted to ensure every device created a set quantity of content material for the times I used to be assigned, I used a mixture of all of the instruments to refine the content material. So, there’s nobody device I’d say is best, however one frequent thread throughout all of the instruments was that the extra context I supplied, the higher the content material I acquired.
By way of prompts, I provided the AI instruments with:
- Authentic prompts I crafted particularly for my target market and messaging targets.
- An present high-performing content material immediate shared by Mike Cardona’s 90-Day Content material Library immediate.
- Prompts I had beforehand created for Buffer’s Assistant (these are normally featured in our Social Media publication).
I made certain all of the prompts mirrored my content material pillars of private model constructing, profession development and AI, so I may keep on model.
Whereas there was no true scientific or data-driven foundation for this experiment (I really simply needed to see if my viewers would discover), listed here are a few of the boundaries I set for myself:
- All content material needs to be completely AI-generated – solely proofreading will be finished manually
- I can tweak prompts infinitely to get higher outcomes, however nothing extra
- I can hop in to have interaction with feedback
- All content material is scheduled in Buffer (*wink wink*) and sorted with our new Tags function.
These boundaries helped me drive myself away from perfectionism, which allowed me to save time and work shortly. However it additionally restricted the creativity and private perspective I may put into the content material, a serious limitation of AI-only content material.
The aim with these managed parameters was to check how an viewers would reply in the event that they acquired every week of content material from my account written fully by AI, with minimal human oversight. Listed below are the outcomes.
The outcomes
Now, I must share one factor about me: my information evaluation abilities aren’t the strongest. So I’ve needed to flip to AI at this stage of the experiment as properly.
Upfront evaluation of content material efficiency
Listed below are the outcomes of the AI-generated content material from November sixth to twelfth, 2023.
- Whole impressions for that week: 9,624
- Common each day impressions: 1,375
- Whole engagements for that week: 151
- Common each day engagements: 22
General, whereas the engagement charges may probably be increased, the AI-written posts fared properly, and all my objections are extra concerning the high quality of the content material.
The impressions and complete engagement numbers point out an engaged viewers for content material written mechanically with minimal oversight. Monitoring this over an extended interval may present perception into actual efficiency tendencies. However for the week I centered on, the posts achieved strong metrics.
Now, let’s dive deeper into the info.
Impressions and engagements
Over the 7-day experiment, the AI-generated content material garnered vital visibility totaling 9,624 impressions, producing 151 consumer interactions by means of likes, feedback, and shares registered as engagements.
On a mean each day foundation, this broke right down to:
- 1,375 impressions
- 22 engagements
In comparison with my general LinkedIn averages, this week massively over-indexed for visibility and response:
- Common impressions for Nov 6-12 are about 11% increased than a typical week over the earlier 3-month interval.
- Common engagements for Nov 6-12 are over 75% increased than a typical week.
Primarily based on this, we will assume that the AI-generated content material resonated when it comes to sheer attain and uncooked interactions generated based mostly on elevated volumes from historic baselines.
At an combination weekly stage, attaining almost 10,000 impressions demonstrates a significant scale of discovery. And whereas I want engagement was increased (don’t all of us), crossing 150 actions or almost 25 per day is a powerful baseline response indicating the AI-produced posts intrigued my viewers.
Engagement charges
We will additionally look at consumer habits by means of the engagement charges, also referred to as the ratio of interactions to impressions.
Over the seven-day stretch, the posts achieved a mean 1.57 p.c engagement charge, which is taken from the 151 complete engagements generated divided by the 9,624 combination impressions.
Breaking down each day engagement charge supplies further context:
- Finest Performing Day: November sixth at 3.5 p.c charge
- Worst Performing Day: November twelfth at 1 p.c charge
- Remaining days ranged between one to a few p.c
One of the best performing day was a Monday, and the worst was a Sunday, so the downward pattern isn’t worrying and matches with expectations of LinkedIn content material efficiency.
From this evaluation, I can inform that scheduling posts earlier within the week might be higher for engagement.
Precise content material efficiency
Now, transferring on from the averages and aggregates of the entire week, one main observe from the efficiency of content material throughout the week is that actionable recommendation immediately serving to readers succeed at one thing carried out dramatically higher.
Digging deeper, the very best traction submit from November sixth overlaying actionable on-line writing suggestions noticed 60 consumer interactions measured towards 1,699 impressions for a 3.5 p.c engagement charge.
Comparatively, November twelfth’s lower-performing submit was extra conceptual/philosophical as an outline of AI branding fundamentals and noticed solely 10 engagements from 967 views – a one p.c charge.
Analyzing essentially the most and least partaking items by matter reveals that my viewers seems to strongly choose instantly relevant “how-to” enhancements. Regardless of its informational worth, forward-looking thought management typically overwhelms or loses parts of audiences.
This pattern recurred all through the week, with sensible skill-building content material considerably outperforming subtle however extra passive consumption items.
The clear takeaway facilities on bite-sized, tactical content material higher commanding viewers funding – aligning rationally with their fast development wants.
Time Collection Evaluation
I acquired ChatGPT to make a chart plotting each day impressions all through the week.
Some key observations and takeaways from this evaluation:
- Peak days: There was a big uptick within the engagement at the beginning of the week, with Mondays and Tuesdays exhibiting the very best ranges of interplay.
- Mid-week tendencies: A noticeable dip occurred mid-week, significantly on Wednesday and Thursday, indicating much less viewers exercise throughout as of late.
- Weekend insights: Regardless of a common notion of weekends being much less favorable for engagement, our Saturday posts carried out comparatively properly, though a drop was noticed on Sunday.
What went properly
So, let’s speak about the great things from this AI content material experiment. Once I dove into the numbers and the ups and downs of the week, just a few cool issues actually stood out.
First off, the AI was working the present right here with only a trace of a human contact by means of prompts and context sharing. This gave me a contemporary take a look at how content material lands with out the added private perspective. And shock, shock, it seems that AI can churn out stuff that not solely grabs consideration but in addition will get folks speaking and interesting. Fairly cool.
After we stack this week’s numbers towards earlier ones, it is clear that AI is not only a one-hit marvel as a artistic assistant. We’re speaking constant influence, pulling in views and interactions past what we normally see. Nevertheless, this is not simply random luck however a mix of some issues:
- The belief I’ve constructed with my authentic content material performed a giant position within the efficiency of the AI content material. I sometimes don’t publish daily of the week, however once I do, I get engagement. That’s a results of belief constructed over time with my viewers. My recommendation: give attention to constructing that belief.
- A deep understanding of what’s prone to resonate with my viewers by means of content material pillars. I didn’t simply choose the random concepts I acquired from the AI instruments, I made certain to refine the content material until it matched what I knew folks would anticipate from me.
Now, let’s speak matters. The most well-liked submit supplied sensible recommendation centered immediately on the reader – enhance their writing abilities as a creator. The least common took a special, broader angle discussing AI functions for private branding, ending up extra conceptual and summary for the typical reader. Some key takeaways:
- Posts offering tangible suggestions, tips or recommendation for readers scored a lot increased engagement than big-picture assume items
- Actionable content material serving to customers make progress resonated greater than thought leadership-style concepts
- Practicality over philosophy when aiming to drive interactions
This means focusing content material on bite-sized, sensible takeaways readers can instantly apply will reliably yield increased engagement. Whereas extra conceptual or forward-looking themes might lose or overwhelm some customers regardless of being intellectually attention-grabbing.
What didn’t go properly
Prompting AI instruments is extra an artwork than a science, which suggests there’s no exact option to get it to really “sound human” until you intervene and edit the content material it generates.
For instance, once I would share a immediate, the primary reply would virtually at all times be extraordinarily flawed. Some frequent errors had been repetition and pointless lists. AI instruments even have a bizarre behavior of capitalizing in bizarre locations – and I don’t write like that. I may at all times share further prompts to get the outcomes nearer to sounding like me, but it surely wasn’t good.
Conclusion
So, sure, I revealed AI-generated content material for every week straight, and nobody observed. In reality, my engagement stayed the identical and was even higher in some circumstances.
My subsequent transfer is all about fine-tuning. Listed below are some subsequent steps I’d take away from this experiment:
- My content material pillars work finest after they observe the actionable recommendation route, so I’ll prioritize that content material on LinkedIn any longer.
- Lengthy-form content material is a winner – all of the posts had been over 350 phrases, and the efficiency wasn’t harm by size.
- That is extra of a private factor, however I’ll at all times tweak the AI voice and elegance to match mine. It was uncomfortable to note issues I’d have eliminated/modified if I hadn’t set such construction parameters.
When you’re like me and have constructed up belief together with your viewers, wrestle with consistency, or simply need extra methods to border your concepts, letting AI take a swing at increasing your attain looks like a no brainer.
