Marketing Strategies

Why TikTok Should Be Included In Your Digital Marketing Strategy

TikTok calls itself thdestination for shortformobile videos.” The videos are often short, averaging 15 seconds, although the platform launched 10 min videos in Marc2022.

Despite only becoming mainstream everywhere in 2018, TikTok was created two years before, in 2016, by the Chinese company ByteDance. Although it was initially planned to be focused only on the Chinese audience, the founder of the company, Zhang Yiming acknowledged the need for international expansion.

According to him “China is home to only one-fifth of Internet users globally. If we don’t expand on a global scale, we are bound to lose to peers eyeing the four-fifths. So, going global is a must”.

In 2017, TikTok intelligently merged with another popular app,, which already allowed lip-syncing and had a rising fan base, especially amongst the younger audience, and consolidated into one app, adding the existing accounts to the platform.

About TikTok

TikTok has risen in popularity in the last years, especially throughout the pandemic and it has naturally been named the fastest-growing social media platform of all time. It is not difficult to understand why, as the numbers will show you.

The platform is used for entertaining content mostly – dancchallenges, and lipsynching, but also for educational subjects, where people discuss complex topics like investments, healthcare, etc.

As happens with all social media platforms, marketers soon discovered the ad reach potential of TikTok. In this article, we put together some data that shows why you should consider TikTok in your digital marketing strategies and which are the most popular formats to target audiences.

Before, let’s look at some curiosities about the app.

Interesting Facts​ About TikTok

1. TikTok started to skyrocket during the Covid19 outbreak and still is growing globally

In 2019, the time spent on TikTok was a mammoth​ 68 billion hours. However, in March 2020 and throughout the biggest part of that year and the year that followed, COVID-19 forced people to stay in confinement in their residences, isolated from family and friends. As many businesses struggled, TikTok soared.

Throughout March 2020,  the average U.S. visitor was using the TikTok app for 858 minutes (14 hours and 18 minutes)

And perhaps you might think the app was only used for infinite scrolling and to make silly comedy videos and dances, but it also had informational value.

According to Adam M. Ostrovsky and Joshua R. Chen, from the Pennsylvania State University, TikTok had a massive impact on spreading health advice regarding the COVID-19 pandemic. In fact, the researchers found that videos with the hashtags “covid-19,” “covid19,” and “coronavirus” have reached 4.4 billion, 33.3 billion, and 93.1 billion views, respectively.

Of these videos, 6.33% were produced by actual healthcare professionals, and 15.66% of videos communicated pragmatic health information. Only 0.66% were related to misleading or inaccurate medical advice.

It is estimated that by the end of 2022, the number of TikTok users worldwide will be 755 million people. Statista also predicts that by 2025 955.3 million people will be using TikTok.

TikTok recording

2. TikTok is expected to surpass Snapchat in 2023/2025 across Gen Z 

Being already the biggest age user base, Gen Z became even more into TikTok in 2020, due to schools and colleges being closed down. The Kantar “COVID-19 Barometer discovered that between March and April 2020, usage of TikTok was up 33% for Gen Z respondents and 27% for millennials.

Such rapid growth among Gen Z Tiktok is expected to surpass Snapchat, which is still the most popular social media platform across this segment, by 2025.

And it’s not only Gen Z, but also even younger audiences are now more and more on TikTok. In 2020kids aged between 415 spentan average of 75 minutes dailyglobally watchinTikTok videos.

In the US, this number is even higher —87 minutes. This means you can easily sharcontent with audiences younger than Gen Z.

Time kids spent on TikTok

3. TikTok pushed businesses and people to be more creative

According to the State of Mobile 2022 report from, “consumer spending on TikTok increased 77% in 2021. Overall, users spent $2.3 billion on the app, compared to $1.3 billion the year before.

Therefore, this platform can not be ignored by marketers, as it has enormous revenue potential. Though, with its very specific format and limited time span, TikTok is often misunderstood.

It forces businesses that wanted to market their products on their platform to come out of their comfort zones and condense information into a very minimalistic and straight-to-the-point concept.

The TikTok algorithm does favor quality content, so it will award businesses whose marketing ideas are engaging, likable, and relatable. Companies need to jump on trends before it’s too late but without trying to copy or take the spotlight of the actual content creators.

@cocacola #ad Sharing our magic through @jalaiahharmon dc!✨ #ShareTheMagic @yasminheroo @davidvooo ♬ Open – Khalid feat. Majid Jordan

4. TikTok influencers could be more costeffective to reach new people than the TikTok ads platform

Regards, Tik Tok influencer marketing, TikTok is the mostcosteffectivwayto spread awareness for businesses.

In 2021 the total number of views of sponsored videos was over 1.3 billion, with the average views per video being 508k.

There are TikTok creators that have up to100 million followersanearn around $5 million a year.

Currently, the most followed Tik Tok account is Khaby Lame. The Italian-Senegalese comedian became the most followed account in September 2022, with 149.1 million followers, surpassing Charli D’Amelio (with 145.3 mln). Right now, Khaby has an estimated net worth of $13 million USD.


How popular is TikTok?

  • 1 Billion Monthly Active Users Worldwide

There are 8 new customers are joining TikTok every second,​ with an average of 650,00 new users joining daily.​

  • 6th​ most popular social media network

Currently, TikTok sits at number 6th as the most popular social media network worldwide. Facebook sits at the top, followed by YouTube, Whatsapp, Instagram, and WeChat. TikTok has already surpassed Facebook Messenger in the number of monthly active users.

  • It took TikTok only years​ to reach 1 billion users 

It is no surprise the app is known to be the fastest-growing ever, as it achieved 1 billion users in just 3 years. The bookmark number was celebrated in September 2021. For comparison, it took Instagram 8 years to achieve the same number of users.

@tiktok✨ 1 billion✨ people on TikTok! Thank you to our global community ♬ original sound – TikTok

  • A user spends 52 minutes a day on average on TikTok

Not only that, but according to TheSocialShepherd, the average person opens TikTok 19 times per day.

TikTok Usability And Demographics

TikTok has been downloaded 3 billion times, with 383 million installs in the first half of 2020. The app was downloaded 693 million times in 2019 & 850 million times in 2020.

TikTok downloads

With 1 billion active userby the third quarter of 2021, TikTok became the fastest-growing app ever. Right now, out of 4.8 billion internet userworldwide, 20.83% use TikTok.

Plus, TikToks had a1157.76% increasein its global user base in 2 years (between Jan 2018 and July 2020).

TikTok Users

​Most TikTok users also use other social media platforms. In fact, only 0.1% of users are unique to the app and do not use other social media networks. 85% of TikTok users also use Facebook, while 84% use Instagram and 81% on Youtube.

This data underlines the importance of spreading the message on different social media platforms, in order to increase its reach.

Social Media Platform Audience Overlaps


Available in 154 countries, the highest usability of TikTok is in Asia. Right now, the app is available in 85% of countries around the globe.

While the app’s largest audience lives in the USA, Indonesia is surprisingly second, followed by Brazil and Russia.

Country Users TikTok

Concerning the age of TikTokers, 47% of the users​ have in the age group of 10 to 29 (2021).

There are more women than men in TikTok: 56% are female and 44% are male. In fact, 24% of TikToks global audience arwomen 182years, whilst males same age are at 18%.

Distribution of TikTok users worldwide

TikTok Users By Age

​What Is TikTok Marketing?

So, how to choose a social media marketing agency that includes TikTok in its agenda? And why you should do that?

TikTok started running Ads in early 2019 and since then brands like Nike and Apple Music have used the platform to promote their products with unique entertaining ads.

The launch in-app shopping feature of TikTok, added in 2021, became essential to brands looking to connect directly to customers.

TikTok Ads start at $10 CPM,$1 Cost-Per-Click and a $500 minimum monthly spend.

TikTok Revenue

How Do TikTok Ads CompartOther Platforms?

As we can see from the data below, TikTok has an average CPC that is less expensive than Instagram and Linkedin. It is also one of the cheapest mainstream social media networks regarding the average CPM, with half the price of Facebook and also behind Instagram, Linkedin and Twitter.

How Do TikTok Ads Compare to Other Platforms

The app also has a tremendous reach and therefore it can not be overlooked or frowned upon by marketers. Professionals in this area can reachroughly 12.2%of all the peopleon Earth using ads on TikTok today.

TikTok Ad reach ranking

Most popular industries

Based on a investigation the most popular sector for businesses across TikTok is beauty, following fashion.

TikTok Likes Small Businesses

Looking at TikTok content categories overall we can see that entertainment, dance, and pranks are taking the top positions. Therefore, it is common to see brands adapting to these formats or collaborating with influencers that are known for being popular in these categories.

Top 10 TikTok Content Categories

New Feature To Look Forward

Being fully aware of its potential, TikTok is launching a new tool for advertising. TikTok Pulse  is  a  new  contextual  advertising  solution  that  ensures brands’ ads are placed next to the top 4% of all videos on TikTok”

TikTok Pulse was launched in June 2022 for US advertisers. Creators and publishers with at least 100,000 followers on TikTok are eligible for the 50/50 split revenue share program.

The purpose is to attract more creators to the app and to ensure a more “brand-safe” environment.

TikTok Pulse

Final Thoughts

As an emerging platform that is captivating and easily accessible, TikTok should not be neglected by digital and content marketers. This social media network does bring some additional challenges to advertisers in order to get their message through, but since it cuts to the chase it is easier to get the message across to the customer.

According to TikTok itself, 63% of all successful ads with the highest click-through rate (CTR) highlight their key message or product within the first 3 seconds. Therefore, it is crucial to produce content that immediately catches the attention and puts the client’s product on center stage.

Plus, for marketers, it is important to know that the platform is aware of its ad potential and trying to find directions to facilitate, content creation, development, and targeting for marketers. We can see that through initiatives like TikTok Pulse, but also by the variety of options when it comes to ad creation.

The creative marketer can work with in-feed ads, brand takeover ads, and branded hashtag ads. With a plethora of options, there is no excuse to not enter the TikTok boom.

SEO Tips

Which SEO Factors Have the Highest Impact on Ranking?

Many brands and digital marketers seek to know which factors have the highest impact on search engine rankings. In fact, it’s very difficult to come up with a specific list of key factors, as they keep varying and are influenced by updates from Google and social media networks.

Besides, it always depends on which industry you are aiming to rank for. If you are seeking for higher rankings in casino SEO the factors that are going to be of relevance to your strategy might be different if you have a clothing brand website.

With Fortis Media’s expertise and the tools we have within our reach we conducted an in-depth analysis to come up with the best ranking factors and also show some data that can help you take decisions when choosing in which direction to take your SEO strategy.

In January 2021 we conducted an analysis for a leading US horse racing company to identify, which factors have the highest impact on Google rankings. Therefore, the data presented in this article is immensely more relevant if you represent a horse racing brand or you are interested in learning the ranking aspects that define this industry.

We used a novel approach using Machine Learning and decided to share some of our findings in this report. As part of the research, we also did Exploratory Data Analysis, T-test, Cohen’s D, and Regression Analysis (Linear and Logistic).

This was done by collecting top 100 ranking positions for 2907 keywords, which resulted in 274 952 data points. The data was further enriched by third-party suppliers such as Majestic and Semrush for domain-level metrics. We started with 95 variables: 79 from data suppliers and 16 new variables as part of this analysis.

The following is some important advice to digital marketing specialists and also to companies curious about how SEO works, which comes from the extensive research and analysis performed.

Note: Analyzing one company in a particular industry and time frame shows results most relevant to those limitations. This means that if we change the company and the industry, the results may vary.

1. Article Titles Should Be Similar To Search Queries You’re Trying To Rank For

Assuming causality Search Query and Title Similarity is an important ranking factor, having more targeted articles with Titles that are close to the target queries could improve ranking, rather than having broad titles and articles.

All analysis methods agree that Search Query and Title Similarity Index are most closely associated with the ranking position. Search Query and Title Similarity Index as a variable penalizes Titles that are long but contain all the Search Query terms, meaning that Titles that are succinct and on the topic tend to be associated with higher ranking values.

This does not consider semantic mapping that might be happening on Google’s side where different words are treated as synonyms.

2. Search Query and Description Relevance Rank Are Not So Important

Search Query and Description Relevance Rank were the least impactful ranking factors (while statistically significant in the regression analysis).

It could be that Google in some cases chooses a description based on the user query, which might distort the results, it’s also possible that the method to catch relevance used in this analysis is deficient.

3. Building a strong portfolio of external backlinks is essential

Both Regression analyses and ML approaches showed External Backlinks to be an important ranking factor.

Before modeling in regression analyses, External Backlinks had to be log-transformed for any meaningful association could be discerned, meaning that for External Backlinks – one unit of increase in ranking is a factor of 10 increase External Backlinks.

For example, assuming for one increase in rank from the current rank it requires 100 extra External Backlinks, the subsequent increase from the new level – it would require 1000 extra External Backlinks.

There is a decrease in the effectiveness of each new External Backlink creates as the total portfolio of External Backlinks increases.

Having the largest External Backlinks portfolio does not guarantee top ranking as shown in the Bivariate Analysis section, one way to look at it could be that having a good External Backlinks portfolio is a necessary but not sufficient condition for high ranking.

4. Social media domains tend to cluster around the 10th position

This analysis found that the largest mean External Backlinks values are at position ~10. After a deeper investigation, it was identified that this pattern is mostly driven by social media websites.

They tend to cluster around the 10th position, especially, which on its’ own takes ~10% of all results for position 10.

5. Pay extra attention to the Trust Flow to Citation Flow Ratio

Trust Flow to Citation Flow Ratio showed up as an important ranking factor in the ML model section. The ideal value should be between 0.5 and 1.5.

Investigating the variable’s relationship with ranking in isolation, the mean value tends to shoot up for top ranking positions, while staying rather steady for the rest of the positions.

The ML modeling section displayed a penalty for domains with low Trust Flow to Citation Flow Ratio, while hovering around zero above a certain threshold.

As with other variables, there could be some other confounding factors – one way to interpret the results is that domains with a higher Trust Flow to Citation Flow Ratio tend to have a better External Backlink portfolio.

Ranking Overview

This section reviews the patterns between ranking and a selected set of potential explanatory variables, investigating the type of relationship (linear or non-linear) and outliers.

Selected on-site variables, like Average Search Query and Title Similarity Index, tend to display a more linear pattern than off-site variables in relation to ranking.

Off-site variables such as External Backlinks, Citation Flow, and Referring Domains display a non-linear relationship, where the mean value for a given rank increases until ~10th position after which the trend reverses.

Part of this pattern for off-site variables is driven by social media websites. Removing social media websites changes the pattern.

Two hypotheses could be considered to explain the pattern for social media websites peaking at the ~10th position.

1. Users are downvoting social media websites

By clicking on them less often and as such Google simply down-ranking them as a result.

2. Social media websites have less relevant content.

Overall the relationships between ranking and other variables are weak due to the wide variance of each SEO metric for any given rank.

So, in conclusion, off-site metrics tend to display a piecewise pattern with a transition point at position 10, as can be seen below.

Disclaimer: The data displayed in this section is from January 16th 2021, and does not represent the current context but is included to show the overall context of the analyzed data.

Average External Backlinks Value Per Rank

Average External Backlinks Value Without Social Media
The red line marks the 10th position ranking on Google.

The average number of external backlinks tends to grow until the 10th SERP, then we see the drop.

However, removing social media domains changes the distribution for external backlinks with higher ranking positions tend to have more backlinks.

Social Media Listings are Potentially Skewing Results

Social media websites comprise only 0.003% of all unique domains but represent ~3 percent of all listings and ~ 70 percent of off-site metric value mass.

This means that social media listings exhibit a pattern of possibly skewing results. According to Investopedia “skewing means a distortion or asymmetry that deviates from the symmetrical bell curve, or normal distribution, in a set of data’.

Social Media Websites Proportion

Social Media Pages As A Proportion Of All Rankings

Machine Learning Analysis

Examining SHAP patterns showed that Search Query and Title Similarity Index seem to display a linear pattern in relation to ranking. While having higher Index values is associated with a higher likelihood to be ranked on the 1st page or rank higher on the first page. If you are not familiar with the concept of SHAP in Machine Learning, learn more in this article from Towards Data Science.

Trust Flow to Citation Flow Ratio is another important ranking factor according to the model, tends to show a penalty for low ratio values and being neutral past a certain threshold, this could be because domains with low Ratio values, tend to have a poor quality external backlink portfolios.

Highest External Backlinks (or Citation Flow) values are not necessarily associated with top ranking positions, but having lower values tends to be associated with lower rankings.

Ranked variables when all ranking factors are included:

Classification ML Model
The graph shows a list of ranked variables when all numeric variables are included.

After Organic Traffic, Search Query, and Title Similarity Index is the second most important variable for model performance, Trust Flow to Citation Flow Ratio is in the top 5, but neither Citation Flow, Trust Flow nor External Backlinks is in the top 20.

This is partly because the model uses other variables, such as Referring IPs or Referring Domains type HTTPS, which are highly correlated with the before mentioned ones, and being highly correlated among themselves would split the difference further diluting their contribution.

Importance of Ranking Factors According to ML

The graphs show variables in descending order as considered important in predicting rank. After extensive internal discussions, we decided to focus only on those factors we have the strongest influence.

Trust Flow to Citation Flow Ratio came out as the strongest predictor. However, the graph doesn’t show whether there is a linear relationship between the rank and variable value. Later in this article, we will present a deeper look into potential emerging patterns.

Classification ML Model 2
Data relevant to (|SHAP value|)

Low Trust Flow to Citation Flow Ratio is associated with lower rankings

Trust Flow To Citation Flow Ratio
Trust Flow To Citation Flow Ratio

By visually inspecting the ML models it can be seen that a lower TF/CF ratio is associated with lower rank predictions. Both Machine Learning models learned that lower Trust Flow to Citation Flow Ratios is associated with lower rankings.

For the classification model, the threshold seems to be at ~0.5, where the majority of the values are below the 0 value (less likely to be on the first page) and stabilizing beyond that point – having both positive and negative values. Take note of the color gradient – pages with lower Citation Flow Values and Trust Flow to Citation Flow Ratio seem to be especially penalized.

Similar pattern holds for the Learn-to-Rank model, except for the ratio value where most of the SHAP values lie below the 0 lines being slightly below 1.

Learn To Rank ML Model

Higher Search Query and Title Similarity Index Values Are Associated With Higher Ranks

Search Query and Title Similarity Index display a rather straightforward linear relationship – higher values are associated with a higher likelihood of being on the 1st page. This is noteworthy as the ML model can learn any pattern to fit the data, yet the pattern is linear.

This pattern holds for both the classification and Learn-to-Rank models, meaning that both models find higher Search Query and Title Similarity Index values to be associated with higher ranks.

It corroborates the findings from the Linear Modeling and the Variable Relationships sections where it was shown to have the strongest association with our target variable – Ranking.

Search Query And Title Similarity Index

Learn To Rank Search Query

Citation Flow Displays A Non-Linear Pattern

The Classification ML model consistently displays a pattern where the highest contribution towards being classified on the 1st Page is in the range between 40-50. It holds, even if we switch out Citation Flow for External Backlinks, Trust Flow, Referring Domains, and sample different queries for model building. The pattern persists when we build an ML model with only a single input variable – Citation Flow.

Note that the data points with high SHAP Values (contribution towards being classified as being on the 1st page) tend to also have high Trust Flow to Citation Flow Ratios. This is consistent with the findings investigating two-way variable relationships, which showed that the 1st page does not have on average the highest External Backlinks values – the 2nd page does.

SHAP Value For Citation Flow

SHAP Learn To Rank

Description Relevance Rank Is The Least Predictive

In comparison to other variables in the model, Description Relevance Rank seems to be the most weakly associated with the target variable and as such not very predictive. The variable is the rank ordering of the Search Result Description Text and the Search Query bm25 Score (an information retrieval metric) ranked for every query – the best matching description starting at 1 and increasing as the metric value decreases.

In the linear modeling section, the variable was statistically significant but with the smallest effect from selected variables, the same holds when using ML models. The variable displays a linear trend when classifying for the 1st-page ranking or ranking the full list (100 rankings per query), but no discernable pattern is visible when ranking the results of the first page.

SHAP Description To Query Learn To Rank SHAP Description To Query

Client Ranking Profile for Classification (Ranking on the 1st Page)

The red dots show where on the contribution scale (SHAP) our client‘s results lie. Due to them being one of the most ranking domains and have the highest mean and median rankings in the data set, it’s reasonable to assume that the results are biased towards their domain, assuming causality.

Citation Flow SHAP SHAP Search Query SHAP Description To Query CLIENT SHAP Trust Flow To Citation Flow Ratio

From off-site metrics in the model, the client seems to have rather good values, maybe having some improvement in the Trust Flow to Citation Flow Ratio. On-site metrics are query by query basis, but it seems, that there would be some gains to be had in those cases where the Search Query and Title Similarity Index value is low.

Further Analysis

The analysis as is found some significant factors associated with Ranking, but other potential factors could be relevant.

Online literature, like Cognitive SEO, as well as SME input, suggests that Recency (time since the article was published) is a potentially important variable, this analysis tried including the variable, but due to formatting inconsistency from the data supplier it was not feasible to do, with better quality data, it could be worthwhile including this variable in future research.

Another aspect worthwhile investigating is page-level content, such as text length, text relevance to query (BM25, TF-IDF), and others. An effort was put in to gather the HTML documents for all 274 952 ranking results (91 522 unique URLs), but too much data was missing, with non-random gaps in data (lower ranked results tended to have more data missing), which could have led to bias in the analysis.

This was partly because the scraping was done a few months after initial data gathering and because the ranking scraping and page scraping were done from different locales.

Plus, several sources, like the Search Engine Journal, suggest Page Load Speed is an important factor, this data was unavailable for this analysis, but future analyses could consider including it.

Another area of investigation which came up during this analysis was Content Accessibility Metrics as explanatory variables, but due to the same reason other page-level content metrics were not included, future research should consider including them.

Lastly, a more generic model would be warranted, this analysis was based on 2907 selected keywords where is already a well-ranking domain, as such the patterns learned from the data will be biased towards the domain (and other top-ranking domains).

Creating a model with other keywords (randomly selected or from other subject areas) could improve the generalizability of insight.

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