Are you able to run a social media account solely utilizing AI?
I not too long ago took a while off work and thought I’d use the chance to reply this query.
See, it’s exhausting arising with new content material persistently, however I’m a little bit of a purist in terms of what I share on social media and the way I share it. We’ve even lined 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 perhaps deeply interested by: how does AI content material on LinkedIn carry out?
What I did and the experiment parameters
I targeted 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 targeted my efforts completely 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 one of the best likelihood of making content material to a sure stage of high quality I hoped for.
To maintain the experiment parameters strict, one hundred pc of the content material was AI-generated based mostly on varied prompts. The one exception was some gentle 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 software 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 software I’d say is best, however one widespread thread throughout all of the instruments was that the extra context I supplied, the higher the content material I received.
When it comes to prompts, I equipped the AI instruments with:
- Unique prompts I crafted particularly for my target market and messaging objectives.
- 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 often featured in our Social Media publication).
I made positive all of the prompts mirrored my content material pillars of private model constructing, profession development and AI, so I might 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 below are a number of the boundaries I set for myself:
- All content material must be completely AI-generated – solely proofreading will be carried out manually
- I can tweak prompts infinitely to get higher outcomes, however nothing extra
- I can hop in to interact with feedback
- All content material is scheduled in Buffer (*wink wink*) and sorted with our new Tags characteristic.
These boundaries helped me drive myself away from perfectionism, which allowed me to save time and work shortly. But it surely additionally restricted the creativity and private perspective I might put into the content material, a significant limitation of AI-only content material.
The purpose with these managed parameters was to check how an viewers would reply in the event that they obtained per week of content material from my account written fully by AI, with minimal human oversight. Listed here are the outcomes.
The outcomes
Now, I must share one factor about me: my information evaluation expertise aren’t the strongest. So I’ve needed to flip to AI at this stage of the experiment as effectively.
Upfront evaluation of content material efficiency
Listed here are the outcomes of the AI-generated content material from November sixth to twelfth, 2023.
- Complete impressions for that week: 9,624
- Common day by day impressions: 1,375
- Complete engagements for that week: 151
- Common day by day engagements: 22
General, whereas the engagement charges might doubtlessly be larger, the AI-written posts fared effectively, 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 robotically with minimal oversight. Monitoring this over an extended interval might present perception into actual efficiency developments. However for the week I targeted on, the posts achieved strong metrics.
Now, let’s dive deeper into the information.
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 median day by day foundation, this broke all the way 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% larger than a typical week over the earlier 3-month interval.
- Common engagements for Nov 6-12 are over 75% larger than a typical week.
Based mostly 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 practically 10,000 impressions demonstrates a significant scale of discovery. And whereas I want engagement was larger (don’t all of us), crossing 150 actions or practically 25 per day is a robust baseline response indicating the AI-produced posts intrigued my viewers.
Engagement charges
We will additionally study consumer conduct by means of the engagement charges, often known as the ratio of interactions to impressions.
Over the seven-day stretch, the posts achieved a median 1.57 p.c engagement charge, which is taken from the 151 complete engagements generated divided by the 9,624 combination impressions.
Breaking down day by day engagement charge supplies further context:
- Greatest 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 3 p.c
The perfect performing day was a Monday, and the worst was a Sunday, so the downward development 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 may very well be higher for engagement.
Precise content material efficiency
Now, transferring on from the averages and aggregates of the entire week, one main word from the efficiency of content material throughout the week is that actionable recommendation instantly serving to readers succeed at one thing carried out dramatically higher.
Digging deeper, the very best traction submit from November sixth masking actionable on-line writing ideas noticed 60 consumer interactions measured in opposition to 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 desire instantly relevant “how-to” enhancements. Regardless of its informational worth, forward-looking thought management usually overwhelms or loses parts of audiences.
This development recurred all through the week, with sensible skill-building content material considerably outperforming refined however extra passive consumption items.
The clear takeaway facilities on bite-sized, tactical content material higher commanding viewers funding – aligning rationally with their instant development wants.
Time Collection Evaluation
I received ChatGPT to make a chart plotting day by day impressions all through the week.
Some key observations and takeaways from this evaluation:
- Peak days: There was a major uptick within the engagement in the beginning of the week, with Mondays and Tuesdays exhibiting the very best ranges of interplay.
- Mid-week developments: A noticeable dip occurred mid-week, notably on Wednesday and Thursday, indicating much less viewers exercise throughout nowadays.
- Weekend insights: Regardless of a common notion of weekends being much less favorable for engagement, our Saturday posts carried out comparatively effectively, though a drop was noticed on Sunday.
What went effectively
So, let’s speak about the great things from this AI content material experiment. After 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 recent have 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 additionally will get folks speaking and interesting. Fairly cool.
Once we stack this week’s numbers in opposition to earlier ones, it is clear that AI is not only a one-hit surprise as a artistic assistant. We’re speaking constant impression, pulling in views and interactions past what we often see. Nevertheless, this is not simply random luck however a mixture of some issues:
- The belief I’ve constructed with my unique content material performed a giant function within the efficiency of the AI content material. I sometimes don’t publish day-after-day of the week, however after 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 more likely to resonate with my viewers by means of content material pillars. I didn’t simply choose the random concepts I received from the AI instruments, I made positive to refine the content material until it matched what I knew folks would anticipate from me.
Now, let’s discuss matters. The most well-liked submit supplied sensible recommendation targeted instantly on the reader – methods to enhance their writing expertise as a creator. The least in style took a distinct, broader angle discussing AI functions for private branding, ending up extra conceptual and summary for the typical reader. Some key takeaways:
- Posts offering tangible ideas, tips or recommendation for readers scored a lot larger 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 implies focusing content material on bite-sized, sensible takeaways readers can instantly apply will reliably yield larger engagement. Whereas extra conceptual or forward-looking themes might lose or overwhelm some customers regardless of being intellectually fascinating.
What didn’t go effectively
Prompting AI instruments is extra an artwork than a science, which implies there’s no exact strategy to get it to actually “sound human” except you intrude and edit the content material it generates.
For instance, after I would share a immediate, the primary reply would nearly at all times be extraordinarily flawed. Some widespread errors have 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 might at all times share further prompts to get the outcomes nearer to sounding like me, but it surely wasn’t excellent.
Conclusion
So, sure, I printed AI-generated content material for per week straight, and nobody seen. In actual fact, my engagement stayed the identical and was even higher in some circumstances.
My subsequent transfer is all about fine-tuning. Listed here are some subsequent steps I’d take away from this experiment:
- My content material pillars work finest once they observe the actionable recommendation route, so I’ll prioritize that content material on LinkedIn to any extent further.
- Lengthy-form content material is a winner – all of the posts have 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 magnificence to match mine. It was uncomfortable to note issues I’d have eliminated/modified if I hadn’t set such construction parameters.
Should you’re like me and have constructed up belief along 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.
