The Standard Influencer Tier Classification System (SITCS) (2024) provides a new evidence-backed classification system for placing influencers into specific tiers based on their follower count.

To date, there was no widely agreed upon set of terms to classify influencers. By analyzing 1000s of definitions featured on over 600 websites the usage of the common terms for influencer tiers used within marketing has been evaluated and a unifying set of terms has been established based on that data.

It is the largest and most comprehensive analysis to date of the usage of influencer tier terminology.

The Standard Influencer Tier Classification System (SITCS) (2024):

Nano Influencers: 1,000-10,000 followers
Micro Influencers: 10,000 – 100,000 followers
Mid-Tier Influencers: 100,000 – 500,000 followers
Macro Influencers: 500,000 – 1,000,000 followers
Mega Influencers: 1,000,000+ followers

The following is the suggested reference: The Standard Influencer Tier Classification System (SITCS) (2024) dhpb.co/sitcs

You can find additional definitions for associated terms like Brand Advocate and Brand Ambassador here.

Evidence for The Standard Influencer Tier Classification System (SITCS) (2024)

Over 600 webpages in total were examined for their definitions of the various subsets of influencers that are in usage. The following classification names were examined: nano, micro, mid-tier, meso, power, rising, macro, mega, celebrity, and VIP.

The aim was to fit all the most commonly used terms into a holistic structure, and base the threshold of followers between tiers on the definitions most commonly used.

Each section analyses a specific term by usage and evaluates which are the most common usage of that term. Occasionally, definitions will only include a lower bound or an upper bound for a follower range (‘nano influencers start at 1000 followers…’), which is why there is an unequal number of pages analysed for both upper and lower bounds.

Nano Influencers

The follower count associated most commonly with Nano Influencers was established by analysing 244 pages for the lower bound range and 238 pages for the upper bound.

For the lower bound range, 65% (161 pages) of marketers listed 1,000 followers as the lower bound. The second most common follower count value was no value – that is – this page did not specify a lower bound threshold (28%, 69 pages). This is most likely to imply that the lower limit is 0.

1,000 followers was established as the lower bound based on the majority support from webpage definitions.

For the upper bound, 74% (176 pages) listed 10,000 as the upper bound. The second most common upper bound figure was 5,000 followers listed on 20% (47 pages) of the pages surveyed and third was 5% (12 Pages) with 1,000.

Therefore, 10,000 was established as the upper bound based on the majority support for that upper bound as the classification.

Nano Influencer Background & Context

Nano Influencer is a clear and well supported term for influencers with a very small following. In scientific terms, ‘nano’ refers to a unit of measurement that is one billionth (10^-9) of a specified unit, signifying an extremely small scale. This term is associated closely with ‘micro,’ which itself denotes a measurement that is one millionth (10^-6) of the same unit, hence larger than nano but still on a small scale. Following this logic of scale, a ‘Nano Influencer’ is considered a more diminutive category compared to a ‘Micro Influencer,’ without any intermediate classifications between them. Given the strong logic of Nano, there are no alternative classifications for this follower set.

The term ‘Nano Influencer’ was a term popularised by Sapna Maheshwari in her 2018 piece in The New York Times entitled ““Are You Ready for the Nano influencers?”. In her article, she defines these people as having as few as 1,000 followers. This was picked up soon after by various other publications like by Richard Godwin from The Guardian and Kate Talbot from Forbes. Both of these authors claim these influencers have anywhere between 1,000 and 5,000 followers.

Despite early classifications having a different upper bound threshold, the term has widespread support both in terms of the word chosen (“nano”) and the follower thresholds.

Alternative Terms for Nano Influencer Tier

While there is no direct alternative to Nano Influencer, it is worth noting that not all marketers use the nano term, choosing instead to use the ‘micro-influencer’ term as a term that reaches down to the 1,000 follower mark.

Micro Influencers

The common follower count of Micro Influencers was established by analysing 311 pages for the lower bound range and 323 pages for the upper bound.

For the lower bound range, 45% (140 pages) of pages listed 10,000 followers as the lower bound. The second most common follower count listed was 1,000, which is supported by 38% of the webpages (119 pages).

This evidence alone points to strongest support for 10,000, but with good support for 1,000 followers too. Within the context of the overall classification, however, the nano-influencer term’s upper bound had strong support for 10,000. Therefore, it makes sense to accept 10,000 as the lower bound term for Micro-Influencers.

For the upper bound, 37% (119 pages) of pages listed 100,000 as the upper bound. The second was 26% (84 pages) for 10,000 followers and third was 24% (77 pages) for 50,000 followers.

Therefore, based on the stronger support 100,000 followers is chosen as the upper bound threshold.

Micro Influencer Background & Context

As explained above in the Nano Influencer section, Micro-Influencer is a logical choice for a classification that sits just above ‘nano influencers’. Like the “nano” modifier, “micro” has generally wide support as a term that refers to small influencers, larger than the ‘very small’ nano influencers, although the exact thresholds are not perfectly clear cut.

Historically, while the micro-influencer label had been used sporadically before – it was first mentioned by a prominent publication in 2015, with the term coming into common usage in 2016. The initial mention appeared in Ad Age during the summer of 2015, where author Jack Neff quoted Lyle Stevens, the co-founder of a technology platform dedicated to working with influencers, as using the phrase about one of their campaigns. In this context, the phrase was used to denote between 1,000 – 5,000 followers.

When other prominent sources started picking up the phrase in 2016, they referenced a very wide net of different follower counts as denoting these influencers. For example, an article in Digiday of that year refers to micro-influencers as having between 10,000 – 100,000, while an article that appeared on Forbes indicated that micro-influencers have a maximum of 10,000 followers. Ad Age, similar to Digiday, suggested a follower count of between 10,000 – 90,000.

The large variation in those early conceptions partly helps to explain the wide dispersal of definitions online. While there is agreement that nano influencers are smaller than micro influencers, there is clearly a cohort that believes that micro-influencers can have as few as 1000 followers, with nano influencers having less than 1,000. This cohort, however, is much smaller than the cohort that believes the threshold is 10,000 – 100,000.

Alternative Terms for Micro Influencer

Micro-Influencer is by far the most common label for any influencer population that starts at 10,000 followers. However, from 50,000 followers it begins to overlap with other terms demarking influencers with a larger audience.

Mid-Tier Influencers

The common follower count of Micro Influencers was established by analyzing 97 pages for both the lower bound range and for the upper bound.

For the lower bound range, 79% (77 pages) of marketers listed 50,000 followers as the lower bound. The second most common follower count was 100,000, which is supported by 10% of the webpages (9 pages).

Given the relationship to Micro-influencer then, which is a far more commonly used term (as indicated by the number of pages that use the classification vs those that use the Micro classification) and would be otherwise squashed into only referring to influencers with 10,000 – 50,000 followers – the lower bound of 100,000 is chosen. This respects the overall classification of mid-influencer while appreciating that ‘mid influencer’ was introduced after and is less common than micro-influencer. This is further discussed in the discussion below.

For the upper bound, 76% (74 pages) listed 500,000 as the upper bound. The second was 8% (8 pages) for 100,000 followers.

Therefore, based on the strong support 500,000 is chosen as the upper bound context.

Mid Tier Influencers Background & Context

Mid Tier Influencers as a term was popularised in the Mediakix (now defunct) classification system entitled “Standard Terminology of Influencer Marketing” (STIM). To date, the term hasn’t been mentioned in any top tier publications like The Times, FT or NYT. This is in stark contrast to “Nano-Influencers” and “Micro-Influencers” which have both been featured repeatedly in multiple top tier publications.

Although the online publications are overwhelmingly in support of a lower threshold of 50,000 followers – the amount of support for this definition can be accredited to the STIM system which popularized the ‘mid influencer term’ in 2019. This explains both the high percentage of support for the follower term and the difficulty in finding references to it compared to ‘micro influencers’.

The lack of cohesion in this area can also be seen in the alternative titles listed below. Many agencies and companies have tried to popularize their own term for classifying influencers with more followers than micro influencers but less than macro influencers. It is probably the case that most marketers would simply use ‘influencer’ when describing these individuals – instead of appealing to ‘mid-tier’ or an alternative.

Therefore, as the term originated after Micro-Influencers, and is less common, the micro-influencer follower threshold is used to dictate the bottom. The Mid-Tier label, however, is still included as the long list of alternatives show that marketers find having a classification for this follower threshold useful.

Alternative Terms for Mid Influencer

While ‘mid-influencers’ has the most support there are many different classifications for this section of the 50,000 – 500,000 range. Other terms were evaluated and discarded for various reasons.

Power Influencers: A few pages referenced power influencers but there was no agreement about what the term meant. Furthermore, the other classification refers to size (Nano, Micro) and power does not refer to size – making it a less good choice of word as a defining term.
Rising Influencers: There was good agreement that this term meant between 50,000 – 100,000 followers (with 100% agreement on the lower bound and 90% on the higher bound). It was, however, much less utilized than ‘mid-tier influencer’ and the naming again doesn’t refer to size. ‘Rising Influencers’ suggests a movement in follower count which is unhelpful when the thresholds are static.
Meso Influencers: A few pages used ‘meso influencer’ as an alternative to ‘mid influencer’. This was again infrequently used, but the title was considered as it fits a bit more neatly into the pseudo scientific terms already used (meso itself is a scientific term). Ultimately, Mid-Tier Influencers were chosen for their wider use.
Regular Influencers: Many pages either labeled these influencers as ‘influencers’, ‘standard’ or ‘regular’ influencers. As these terms both relate to the parent category of ‘influencer’ it was impossible to evaluate the follower amounts related to the keywords. For example, one page might say an influencer can have between 1,000 and 5,000,000 followers or between 100,000 and 500,000 and it would be hard to differentiate when they were talking about the parent category of ‘influencers’ vs a subset within an overarching influencer tier classification.

I also considered creating a new name which fits into the pattern of naming influencers aligned with the International System of Units (SI). Within that system, Nano and Micro are directly next to each other, with the next term up being “Milli”. I chose not to change the title as few in the industry would understand the term, as well as ‘Milli’ having confusing associations with numbers in the ‘thousands’ too.

Macro-Influencer

The common follower count of Micro Influencers was established by analysing 151 pages for the lower bound range and 109 pages for the upper bound.

For the lower bound range, 49% (74 pages) of marketers listed 100,000 followers as the lower bound. The second most common follower count was 500,000, which is supported by 39% of the webpages (59 pages).

Given the wider context, it is clear that the lower bound depends on if there is a classification between micro and macro influencers. Although ‘mid influencer’ has less support, the fact that there were so many classifications suggested it’s useful to have a classification term in that range. Given that, although the most support was for 100,000 followers, we will use the second most supported 500,000 so that we can retain the mid-influencer classification.

For the upper bound, 89% (119 pages) listed 1,000,000 as the upper bound. The second was 500,000 flowers with just 7% (8 pages).

Therefore, based on the strong support 1,000,000 is chosen as the upper bound term.

Macro Influencer Background & Context

The need for the term ‘Macro-Influencer’ arose as a comparative category to ‘Micro-Influencer’ to differentiate between influencers with larger versus smaller followings. Trying to indicate that a ‘micro-influencer has less followers than an influencer’ is confusing as it could suggest that a micro-influencer isn’t an influencer. Writers needed a comparative term for this delineation. For instance, the 2016 article in Ad Age titled “Micro, Not Macro: Rethinking Influencer Marketing”, their first mention of ‘macro-influencer’ being a turn of phrase in the title highlights the reason for the term. Similarly, the company Mavrk introduced the term ‘Macro-Influencer’ on their website, specifically on a section about micro-influencers, to clarify this hierarchy within influencer marketing.

Unfortunately, while Nano, Micro and Mega are standard units within the International System of Units (SI), Macro is not – instead being a kind of amorphous term that is used phenomena at large scales. However, it is still a pseudo scientific term and so fits into the general theme of the classification system.

Alternative Terms for Macro Influencer

There are no alternative terms for Macro Influencer, except perhaps just to refer to them as ‘influencers’ which as stated above is hard to quantify.

Mega Influencers

The common follower count of Mega Influencers was established by analysing 56 pages for the lower bound range and 8 pages for the upper bound. The reason for the small number of upper bound references was that often no upper bound was given.

For the lower bound range, 73% (41 pages) of marketers listed 1,000,000 followers as the lower bound. The second most common follower count was 500,000, which is supported by 20% of the webpages (11 pages).

1,000,000 was therefore chosen as the lower bound.

For the upper bound, 87% (7 pages) of pages listed 1,000,000 as the upper bound. The second was 5,000,000 followers with 13% (1 page). In most instances, the pages discussing Mega Influencers did not include an upper bound, which suggested they believed that there was no upper bound limit on this term.

Alternative Terms for Mega Influencer

The term ‘Celebrity Influencer’ was included in the STIM and is used a significant amount, but much less than ‘Mega Influencers’. Of the 28 pages analysed, 78% (22 pages) of marketers listed 1,000,000 followers as the lower bound. The second most common follower count was 5,000,000, which is supported by 7% of the webpages (2 pages). None included an upper bound.

Because the lower bound was generally the same as the Mega Influencer tier, I chose to see these as alternative terms. Furthermore, ‘Mega’ makes more sense than ‘Celebrity’ for a variety of reasons.

Firstly, ‘Mega’ fits the rest of the pattern as it is a scientific construct (106) like nano and micro. ‘Celebrity’ in contrast is a much more convoluted term. Secondly, at some point it stops making sense to classify an influencer based on their follower count. There are only a few thousand profiles with more than 1,000,000 followers and probably only a few hundred with over 5,000,000 followers. At this stage, it’s very unlikely it would make sense to classify them as ‘an influencer’ as opposed to the activities they are taking part in that propelled them to this height of fame.

Therefore, Celebrity is seen as an alternative to Mega, and is not included in the overall criteria.

Discussion

Historical Discussion of Influencer Tier Terms

Why are there so many terms for the subset of influencers and why are they not agreed upon? There are two fundamental reasons:

The people defining these terms have distinct agendas
The classification developed slowly over a period of a few years.

Marketers often define these terms to serve their own current self interest. As X says, “[intermediary influencer platforms … arbitrarily set these numbers based on brands’ requirements for collaborations]”. These marketers might have an agenda, which having a certain classification might play into. For example, they have be operating an influencer agency with 90% of their database having between 10,000 – 50,000. It would be convenient if they had a definition that neatly fit their database, so that term could be ‘their thing’.

Alternatively they might be responsible for selling a platform that for any logical, illogical or historical reason has certain thresholds within their infrastructure. Again, marketers are motivated to use the standard terms within whatever the infrastructure dictates.

Secondly, these labels became prominent in different years as they became required. When social media became popular, marketers quickly found that they could use those accounts with high follower counts as an alternative to advertising. At this point, the term ‘influencer’ would have just meant ‘whoever had the most followers on that social media platform’ and there would be no way to know what the various thresholds would be.

This trend was accelerated by market pressures. High prices in advertising pushed adoption to so called ‘Influencer Marketing”. This term influencer became prominent both on Google trends and within academic papers in the social media context starting in 2014.

As more marketers tried to corral influencers into selling their product, and as influencers became smart to their audience’s value, the price for a sponsored post on social media increased. Looking to retain their ROI on influencer marketing, they looked for influencers with smaller followings. The need for influencers with smaller follower counts lead to the term ‘micro-influencer’ emerging in 2016, with Ad Age described Micro influencers as “2017’s “It girl.”. As already explained, the term Macro-influencer emerged in opposition to micro-influencer. It wasn’t as if Influencers were looking for larger followings – that is broadly where they had started – but that term was required as an opposition term to contextualise ‘macro’ influencers.

Over time, the trend continued. Micro-influencers were now under a cost pressure and marketers were looking for other alternatives. Nano influencer emerged in 2019 with the New York Article, and became widely used in the years after. The following are the dates the various terms emerged:

Influencer – 2014
Micro-Influencer – 2016
Macro-Influencer – 2016
Nano-Influencer – 2019

The pattern is clear. Eventually marketers will look for even smaller audiences of influencers. Indeed a 2022 publication called ‘Future Human Behavior’ includes a chapter on ‘Pico Influencers’, which is the Standard Scientific term below Nano.

The question of what to name ‘influencers’ with between 0 – 1000 followers will probably become more pressing over time. There are three approaches I will comment on:

Customers or Normal People: In various discourse you hear influencers talking about these individuals as ‘customers’ or ‘normal people’. My view is that neither of these classifications are logical. Customers could have no social following, or a massive one. They do not suddenly lose their ‘customer’ status once they reach a certain follower count. Beyond the ‘cringe factor’ of demarking influencers as somehow ‘not normal’, ‘Normal People’ has a similar issue as a classification to customers. Influencers may not feel they have become ‘not normal’ once hitting 1000 followers.
Peso: Peso, as already stated, is a good term for it. If you need a definition, this is probably the one to go for. It fits the pseudo scientific trend, and is appropriately linked to Micro and Nano.
No Classification: The most obvious candidate is just accept that there must be some lower limit underneath which the individual is not an influencer. The advantages of this is that the term ‘influencer’ still maintains as a definition which accepts this is a subset of the population and doesn’t include everyone. The disadvantage is that there might be some times when you want a ‘complete’ classification, say when you’re analysing a population or you might find a social media characterised by very high trust, and high connection, but with low ‘follower counts’ where this categorisation is appropriate.

I have not included this term, but there are certainly good reasons why you would argue it should have been included. I might include it in an updated model based on feedback in the future.

Therefore, the major terms in influencer marketing then came out of an underlying trend. The other terms like Mid-Tier and Mega influencers were a response to filling in the gaps and providing a holistic classification for writers when they needed them. This ‘as and when requirement’ compared to a top down classification you might find in science is what leads to such a sprawling and undefined class of definitions.

Below I cover a few other classification systems, the limitations to my classification system and methodology in three sections called ‘Other Classifications’, ‘Limitations’ and ‘Methodology’.

Other Classifications

As with this current attempt, there have been a few attempts to provide a concrete classification for influencers both in Academia and online. The model with the largest adhesion is the Standard Terminology in Influencer Marketing by Kixmedia, which I will briefly describe.

What is STIM?

Stim was a classification created by Kixmedia (now defunct) and is not available online although is still occasionally referenced. The authors were looking into four questions, two of which are relevant for definitional purposes.

What is the standard definition of “nano-influencer,” “micro-influencer,” or “macro-influencer?”
Do these tiers apply across all social media platforms?

They proposed the following classification for each Instagram and Youtube.

Instagram:

Nano-influencers: 1,000 – 10,000 followers
Micro-influencers: 10,000 – 50,000 followers
Mid-tier influencers: 50,000 – 500,000 followers
Macro-influencers: 500,000 – 1,000,000 followers
Mega-influencers: 1,000,000+ followers

Youtube:

Nano-creator: 1,000 – 10,000 average views
Micro-creator: 10,000 – 25,000 average views
Mid-tier creator: 25,000 – 100,000 average views
Macro-creator: 100,000 – 1,000,000 average views
Elite creator: 1,000,000 – 5,000,000+ average views

You can find a PDF I created of this original page here. Mediakix does not provide context about why they have decided on either the tier names or the follower thresholds.

Limitations in Classification Systems of Influencers

Social Channels

There is a good argument that an influencer would have a different number of followers for each social channel. For example, STIM suggested a slightly different number of followers for some classifications for Instagram and Youtube. Intuitively, this seems to tally with the experience marketers have – nano influencers need fewer followers on Instagram than on Facebook to achieve similar reach.

I initially considered a multiplier that would be used in order to ‘adjust’ follower counts for each social channel, but have opted to avoid doing that. This was primarily because in the study of 1000 pages used of definitions, only a small minority of pages referenced that this was different for different social channels.

Furthermore, even within social channels marketers might feel like it might vary. For example, when Facebook changed their algorithm to reduce how many posts from ‘pages’ were seen – it destroyed the value of the Facebook page audience almost overnight. Similarly, when Youtube stopped alerting subscribers of new channels – it changed the impact and relevance of follower count marketers. Follower count classifications should always be understood inside the context of social media platforms which evolve quickly over time .

Additionally, the simplicity from having an agreed follower count the same for each platform has its own advantages – such as simplicity and easier communication. Given these limitations though, this classification should not be seen an ‘causal’, for example, that a micro influencer will always have a better engagement rate than a macro influencer. It also would be consistent for specific projects to adjust the terminology.

Geography

Those that marketing professionals consider being ‘influencers’ varies by Geography. From my experience, this seems partly correlated to the maturity of the influencer marketing in the country and partly to the population size. For example, in the UK there is a relatively small population and influencer marketing has been common for many years. Because influencer marketing has been common for so long (a mature influencer market), marketers try harder to get positive ROI from campaigns by going to nano and micro influencers. I haven’t studied this in detail, but other authors like Hung and colleagues mentioned that they adjusted the STIM model in order to correct for China’s larger population.

Given that this research was conducted in English, it is likely that the classification is most useful for English speaking countries such as the USA, Canada, and the UK.

Time Period & Growth Rate

Followers on any social platform tend to increase over time. This is partly due to the number of people on the platform growing, but also due to bots and duplicate accounts providing artificial inflation. The mathematics of this is beyond the scope of this paper, but certainly its easy to tell that social media platforms and the internet in general tends to avoid deleting accounts, or at least does so at a much smaller rate than individuals join.

With that in mind, it might make sense to reclassify the terms as time progresses (perhaps they could increase at a rate of 2% per year). However, I decided not to do this as it seemed like too complex a way of classifying influencers.

Methodology

The research aimed to analyze how marketers utilize various subclassification terms within Influencer Marketing. Therefore, the study aimed to cast a broad net, capturing a large array of these terms’ applications by reviewing a wide spectrum of definitions available online. The exact process was the following:

Term Classification and Shortlisting: Initially, an evaluation of terms used to categorize influencers was conducted to identify all the terms used by marketers. This step was essential to establish a condensed list of prevalent terms recognized across the industry regardless of how common the usage.
Search Protocol: For each term, an automated online search was executed, targeting exact matches of the term in conjunction with references to follower counts. The search protocol did not distinguish between hyphenated and non-hyphenated forms of the terms (e.g., ‘nano influencer’ versus ‘nano-influencer’). This search was facilitated through the Bing Search API, covering up to the first 500 pages returned (although most terms did not reach that threshold). As the results were in the order that a human would be returned them, this was designed to capture all the relevant uses in high quality content. (It should be noted, however, that results returned are not always identical to a human search for various technical reasons).
Duplicate Content Filtering: Subsequent to the search, an automated process in Google Sheets was employed to identify and eliminate duplicate pages. This included instances where identical content was disseminated across different platforms, ensuring the uniqueness of the data.
Data Extraction and Compilation: A Python script was used to systematically extract the definitions from each website page and compile them into a spreadsheet. This process involved the script copying the relevant definitions into a structured format for further analysis, as well as extracting the ‘upper bound’ and ‘lower bound’ where possible automatically.
Manual Verification and Correction: The last phase of the data extraction and sorting involved a thorough manual review of the extracted data. I personally examined the compiled definitions, making corrections or amendments as necessary to the extracted follower counts. This step was used to ensure the accuracy and reliability of the data.
Analysis of Data: The data was then analysed, focusing on the aggregation of follower count frequencies associated with each subclassification term. This involved calculating the total instances of follower counts linked to each term (both for a lower and upper bound) and determining their proportional representation relative to the aggregate of ‘lower bound’ definitions across all terms. For instance, if the term ‘nano influencers’ was associated with a ‘lower bound’ of 1,000 followers in 40 instances out of a total of 400 ‘lower bound’ definitions, it would imply that 10% of the dataset endorses this lower threshold for ‘nano influencers’. I tried to always give the number of pages alongside the percentage for clarity and precision in reporting.
Classification Creation: From the analysis, there were many instances where there was only a small group of people using certain terms. These were included in the ‘alternative terms’ section, and in each case the reasoning for excluding them from the overall classification was offered. The main reason was a lack of usage by the community. Secondly, there were some ‘boundary issues’ where the boundary between two adjacent classifications was unclear. This was resolved on a case by case basis on the basis of which terms had a greater usage and how large the majority was that supported each of the boundaries.

The methodology aimed to capture the various usages of influencer marketing terms, as well as go onto delineate the most widely supported framework for definitions.

Conclusion

The above is a classification system based on the usage online of these terms as well as some basic logic associated with what makes a good classification system and which terms are clear. It is supported by the evaluation of over 600 pages and over 1000 definitions analyzed. Due to reasons highlighted in the discussion, it may not be useful to overly rely on this classification – but when necessary it provides a strongly evidenced classification system that was previously lacking.

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