Navigating C-Suite and Social Follower Data Access
- OneFifty Consultancy
- Sep 11
- 5 min read
The data that social media platforms are sharing with both users and advertisers are evolving rapidly.. While some are giving users greater insight into the impact of their own and their network’s activity, others are placing this insight behind paywalls or using it to fuel their own AI and machine learning capabilities
This means that while in some places you may be better placed to make data-driven decisions about an exec comms programme, in another you’ve an increasingly limited understanding of your media’s performance. For the C-suite and marketing teams, these changing dynamics impact the feasibility and scope of audience analysis. Understanding of these metrics is key to identify how to iterate and improve.
Here we explore some of the recent evolutions in across platforms, exploring two key questions:
What’s in it for the platforms?
What does it mean for audience-led decision-making?
How the moment unfolded
Post 1: “Thinking about when she said ‘See you next era…’ ❤️🔥” — 12 images, alluding to her 12th studio album, shared by @taylornation
Post 2: “92%ers, we’re coming back early for a special episode with a VERY special guest” — from Travis and Jason Kelce and their @newheightshow podcast
Post 3: A teaser clip posted at 12:12 on the 12th, revealing the album name The Life of a Showgirl in a collaboration post between @taylorswift and @newheightshow
For casual observers, these might seem like unrelated drops. For Swifties, they were easter eggs - the hidden clues they’ve been trained to hunt through years of meticulously seeded details, from nail polish colours to music video props. One post was enough to put them on high alert; by the time the podcast teaser hit, the theories were in overdrive.
The result?
The podcast teaser clip became the most-viewed video ever by an individual on any social media platform with 167M views in a day
The two New Heights’ teaser videos pulled in 2.5M engagements on TikTok — over 1,800% above their per post average
The New Heights Podcast gained 300K Instagram followers and 160K YouTube subscribers before the full episode was even released
And here’s the kicker: on paper, it shouldn’t have worked. A male-oriented, sports-focused podcast + a female-centric global pop star = minimal audience overlap. But that’s exactly why it did work - because the mechanics weren’t about the existing overlap. They were about engineering a reason for one audience to cross into another entirely.
LinkedIn - Democratising Data
Comment Impressions
Early this year LinkedIn announced that comments made on posts would soon show impressions data to all users (1). This potentially gives authors positive reinforcement when posting and their wider network richer insights into the themes and styles of content which are popular among other users.
From our own work in executive comms, we know the potential value of LinkedIn comments for building meaningful interactions. LinkedIn also acknowledge that commenting can “do wonders for amplification”(2) - that is, boost a post (and therefore the author’s and commenter’s profiles) in their algorithm. It’s the first time this level of insight has been accessible for the average user, bringing them closer in-line with the views metric on TikTok and Meta reels content.
Personal LinkedIn Analytics
They then expanded access to their personal profile API, enabling executive communications to similarly be more accessible than ever before to marketers seeking to consolidate their analysis time (3). While this still requires the expertise to retrieve the data, it marks another step towards enhanced behavioural data which can enhance the standard of content across the platform.
(3) https://www.prnewsonline.com/why-linkedin-opening-the-api-is-a-game-changer-for-executive-influence/
TikTok- Pixel Pioneering
Engaged Sessions
A key evolution in TikTok’s analytics this year has been the launch of their ‘Engaged Session’ tool, which allows advertisers to track off-platform user behaviour as a substitute for pixels (4). This brings TikTok closer in-line with Google and Adobe, via metrics such as Total Engaged Sessions and Cost per Engaged Session, and marks a move away from the traditional social media platform analytics metrics
Given the turbulence in their operation, which stifled ad reach growth over the last year relative to other platforms (2.3% YoY to January 2025, versus 4.3% for both LinkedIn and Facebook), AdWeek’s observation that Gen Z increasingly replace Google with TikTok for search is the likely driver of this innovation. On the user side, there’s been very little development in the insights available - likely based on the lack of visible long-term impact of the proposed US ban early this year, beyond the dip in ad reach.
Meta- Optimisation Overdrive
Incremental Attribution
Meta’s largest focus this year has been implementing automation via machine learning and AI, in a longer-term shift towards allowing the company to analyse performance on behalf of advertisers. One such feature has been Incremental Attribution (5), which uses machine learning models to predict whether a conversion is caused by an ad (6).
In behavioural terms, it’s adding insight into the outcomes of campaign settings - helping to understand what creative and formats are resonating with an audience. However, it’s a learning model and that means it’s starting from a 0 base in building the view of optimisation, and Meta itself will likely have a stronger understanding of what’s driving results than the advertiser.
Value Rules
A similar introduction has been Value Rules (7), which adjusts ad delivery towards criteria and outcomes defined by the advertiser (currently age, gender, location, OS; and select placements and conversion locations). Again, the insight into what advertisers are placing greater value on across these metrics is likely to be more beneficial for Meta than the end user, however it’s a development in analytics that comes built into their offering (unlike TikTok’s Engaged Session tool).
In both examples, we see Meta focused on improving outcomes for advertisers, with the role of analysis remaining firmly with advertisers in order to gain the same level of insight into audience behaviours as Meta will be accumulating across their users.
What does it mean?
As we can see from these examples, platforms are taking very different approaches to data-sharing for performance insights. While LinkedIn are moving towards a greater level of openness in what’s driving content performance, TikTok is focusing more on transitioning out of traditional social metrics, and Meta are developing their own methods of gaining insight (rather than passing these to the advertiser directly).
These changes are a response to the evolving use of social for search, as well as changing dynamics beyond social itself - particularly regulatory and economic. The consequences of this span end users and marketers - potentially driving improvements in content quality, while in others doing the opposite for those not willing to pay for the privilege
The onus of conducting analysis remains largely on advertisers and those in insight functions, but this may come at a cost for platforms which restrict the access to audience behaviour indicators
In this context, it’s increasingly important to:
Monitor how people’s interaction with platforms is changing - AI/ machine learning is huge source of disruption on the horizon to the direction of travel we’ve seen in the last 18 months
Remain aware of the latest developments in what platforms are opening and restricting access to - it’s likely to continue diversifying in the months ahead
Ensure analysis frameworks for audience behaviour incorporate the new metrics available, while establishing the best way to compensate for those which are becoming less visible
Place a renewed focus on the conversation itself - as we’ve shifted from the text-heavy era of X to the video focus of today, it’s an often overlooked source of what’s resonating with audiences
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