Organic and paid social on LinkedIn, Instagram and Facebook, with engagement and follow rate as the one comparable signal.
This account is a global B2B portfolio spanning four umbrella brands, run across the Americas, Europe, and Asia. The work covers both organic and paid social on LinkedIn, Instagram and Facebook, for all four brands at once rather than one company page in isolation.
Running four brands across three continents on two very different platforms creates a specific measurement problem. LinkedIn, Instagram and Facebook don't behave the same way, audiences and content formats differ by platform, and by region. Without a shared way to compare performance, it's easy to end up judging brands and regions against the wrong benchmark, or missing where attention should actually shift next.
Looking at a 28 day window across the account, one thing stood out: despite very different audiences, formats, and absolute volumes, Meta and LinkedIn converted impressions into new followers at almost the same rate, roughly 6,200 impressions per new follower on Meta and 6,300 on LinkedIn. Two platforms that don't otherwise look comparable were, in fact, producing a nearly identical underlying efficiency signal.
That consistency mattered, because it meant engagement and follow rate could be used as a shared, channel agnostic signal across all four brands and every region, rather than comparing raw impressions or follower counts directly, which would have been misleading given how differently each platform and market performs at the surface level.
Right now, that signal is actively being used to decide where to shift focus next: pulling attention and content investment out of areas that are underperforming on engagement and follow rate, and putting it into the areas producing the strongest response, across brands and regions rather than treating each one in isolation.
This is an active, ongoing account, not a closed chapter, so the honest takeaway right now is about the method rather than a final outcome. At genuine multi-brand, multi-region scale, comparing platforms or brands on raw volume alone doesn't tell you much, the numbers are too different by nature. Finding a consistent, comparable signal, in this case engagement and follow rate, is what actually makes it possible to decide where to reallocate focus across four brands and three continents with any confidence.
This work sits under Creative & Content: content planning and channel management across brands, supported by the multi-market coordination covered in Industry Growth.
See also: Google Ads revenue growth, the same evidence first approach applied to a paid search account.
Raw impressions or follower counts aren't directly comparable across platforms, brands, or regions, since audience size and behavior vary too much. A more reliable approach is finding a shared efficiency signal, such as the rate at which impressions convert into new followers, and comparing that ratio instead of the raw numbers.
It depends on the account. In this case, both platforms converted impressions into new followers at a nearly identical rate, roughly 6,200 to 6,300 impressions per new follower, despite very different audiences, formats, and absolute volumes. That made them directly comparable on efficiency, even though their raw numbers looked very different.
By using a consistent, shared performance signal, such as engagement and follow rate, to identify which brands or regions are underperforming and which are producing the strongest response, then shifting focus and investment accordingly, rather than applying a flat, equal approach across every brand or region.
On a rolling basis. This account is actively monitored and reallocated in near real time based on engagement and follow rate signals, rather than reviewed only at fixed quarterly or annual checkpoints.
It's expected at this scale, and isn't necessarily a problem on its own. A very small percentage of impressions converting into new followers is normal for large B2B accounts, what matters more is whether that rate is consistent and comparable across channels, which is what makes it useful for guiding decisions.