The Truth Behind Social Media Verification: Is It Worth the Price?

The History of Social Media Verification

Many long-time social media users may be aware that social media verification was historically free. 

In those earlier days, the guidelines for verification were simpler:

The Switch to Paid Social Media Verification

For the most part, many of these perks still highlight the primary benefits of social media verification today, perhaps even more so with the significant surge of bots across all channels over the last decade. However, with the added paywalls behind these verifications, essentially mimicking subscription services, verification is no longer limited to just viral accounts. 

As a result, some new features are now being promised. Here are a few of the newer features:

Oftentimes, social media users want to evaluate if this monetary investment in verification will result in an increase in their post performance. We’re here to evaluate a couple major platforms practicing this system, specifically Meta and X, and give our evaluations of the services accompanying the blue checkmark. 

Meta Verified (Facebook and Instagram):

It's rather appropriate to begin our critique with Facebook and Instagram, as they are likely the most prominent platforms for their massive user base and seemingly high verification rates. 

Meta introduced its “Meta Verified” program which introduces the following plans as of August 2024: 

Chart showing the 5 different levels of Meta Verified along with their pricing and benefits

Of the two verification systems we are evaluating today, the Meta Verified program comes at a steeper price point, ranging up to $349.99 a month for the Business Max plan. The Standard plan would be the bare minimum price point to receive the blue checkmark, the very same checkmark that social media enthusiasts would try to seek over decades. 

Based on our observations and data from accounts across various verification plans, we found no convincing or statistically significant evidence that any of these plans impact post performance, such as reach. 

Numerous individual case studies by users across Meta have consistently shown that performance changes are due to normal fluctuations in social media rather than any immediate benefits from the verification plans.

This may not fully reflect the overall value of the verification program or the potential for strong performance on the respective platforms. 

For example, starting from the Business Plus tier, your account gains the added functionality of including links in your reel content. Reels are a content type we've found to consistently perform better across all platforms that feature them, including Meta. 

You also gain advantages in search optimization, where your profile can appear higher in search results. Although, this does not materialistically correlate to an increase in post performance as a significant majority in post reach is skewed towards feeds. 

X (Formerly Twitter)

X has undergone significant changes in recent years, most notably its rebranding. These changes also include an overhaul of the criteria for verification, along with the introduction of a tier-based subscription model similar to what we’ve seen with Meta. 

During its time as Twitter, the verification process was generally free and focused on identifying accounts that were notable, authentic, and active:

The criteria for verification on X (formerly Twitter) have changed significantly. Now, only accounts subscribed to X Premium can receive the blue checkmark. The current price points for X Premium are as follows: 

Chart showing the three options of X Premium subscriptions, including their prices and benefits

The minimum price point for achieving verification status on X under these plans is $7 a month with X Premium. Comparatively, this makes the verification entry about half the cost of Meta Verified when evaluating the cost of the piece alone. 

Along with the checkmark, you are also granted pretty significant functionalities within the account such as analytics. Feel free to read more about that here. 

Mimicking our observations with verified accounts, X also shows no convincing evidence that verification status affects post performance. 

Since the verification overhaul is still relatively new, more data needs to be analyzed to determine if this will change in the future. 

However, general impressions of the platform have seen a steady decrease, as expected during its transitional period away from Twitter. Unlike Meta, it's generally more difficult to pinpoint a clear correlation during that offset of the platform. But we are not too optimistic that paying for verification will benefit your post performance for either platform at this time. 

Is Verification Worth the Price?

Based on our observations shared with industry experts and social media enthusiasts, it is clear that the significance of the checkmarks has diminished over the years. 

We strongly believe that the allure of having a profile checkmark itself should not serve as the basis for investing in the subscription options presented today. Additionally, investment in these plans for the purposes of post performance should also not be a consideration as evidence is lackluster at best. 

The decision to pay for verification should consider a more holistic view of the benefits to your social account. You might receive enhanced authenticity, improved customer support, and possible access to early platform features. The return on investment may vary based on the account.

Need to know if any of these verifications are worthwhile for your business? Send us a message! 

Is X Premium Worth Paying For?

If you're a social media enthusiast like us, you’ve likely noticed some changes involving X and its subscription services. Although the platform has consistently hinted at various premium services since Elon Musk acquired it a few years ago, a covert update to restrict basic access to your page's analytics took effect in mid-June.

Phone screen shows a folder of social media apps, including Facebook, Messenger, Instagram, WhatsApp, and X

Where Did X Analytics Go?

Social media analytics provide critical knowledge about your audience, content performance, and the overall effectiveness of your social media strategy. They empower data-driven decisions, benchmarking, and continuous improvement (something we exercise every day at Random). 

Previously, you could access the necessary information and data through one of the basic profile features or a direct link, such as www.analytics.twitter.com. Since transitioning to X, there has been a gradual reduction in methods to access this interface and instances of missing data points - likely a byproduct of the constant construction of the new analytics environment.

Accessing the provided link on a regular account will now prompt a message suggesting an upgrade to one of X's subscription services. The once-free analytics feature is now locked behind their mid-tier Premium paywall, requiring users to purchase this tier to access it through the direct analytics link.

Screenshot displaying the three different tiers of X Premium, with the $7 per month plan highlighted

Is X Premium Worth It?

So, is this Premium service worth the monthly cost? We're here to dive deeper into the new analytics feature, highlight what's new and what's missing, and ultimately help you decide if it's right for you or your business.

One of the more immediate and obvious changes we noticed is the navigation path to accessing the analytics feature. If the account is logged in as a X Premium account, you will notice the slightly bolder 'Premium' text towards the left where all the platform features are listed. If subscribed, it will then introduce additional features in the main portion of the interface, including a direct path to your profile’s analytics.

Screenshot showing the X Premium tab on the X interface, with the Analytics line circled in red.

Upon accessing the X Premium analytics feature, you will be introduced to the overview tab. Much like other social media platforms providing analytics, the overview is typically the central interface. You will usually see line graphs summarizing data (typically in days by default) as well as totals for those accumulated metrics, along with percentage changes relative to the preceding period.

Screenshot showing the analytics tab overview, with a line graph and boxes displaying Impressions, Engagement rate, Profile visits, and more.

Here is a summary of differences we have noticed having closely worked with the older, previously free, interface over the years: 

Improvements

What’s Missing

The new analytics interface includes an overview and a tab for content performance. Clicking on this tab will show your recent posts and their corresponding metrics.

Screenshot showing analytics tab where each post is displayed along with date, impressions, likes, bookmarks, and quotes.

Similar to the overview portion of the analytics, we were able to pinpoint some key differences:

Improvements

What’s Missing

So the million-dollar (or really, seven-dollar) question remains: Is X Premium worth it for your business? It depends.

If your business heavily relies on X as its primary platform, this subscription may be a necessary investment. Social media analytics will always be better than having no analytics at all, but you should also consider the other premium features, such as claims of increased post performance, creator controls, early access to new features, and more. However, the return on investment may vary if X serves as a secondary platform for your brand. 

Feel free to contact us if you want our expert recommendations on whether X Premium is right for your business or if you need help navigating this ever-changing social media landscape!

Here Are The Latest TikTok Benchmarks for 2024

Still wondering if TikTok is worth your brand's attention? The latest Rival IQ report reveals the platform's impressive growth and continued user engagement, making it a prime target for social media strategies in 2024.

We’re jumping into the key points from their report and how you can use them to analyze your own TikTok metrics. 

Firstly: What is Benchmarking?

Benchmarking is a part of the analytics process that compares your social media results to industry and competitor standards. How do your campaign results - like engagements, engagement rate, clicks, and more - hold up against other brands’ campaigns on the same channel?

Benchmarking is a key component of strategy. Understanding how your brand stacks up against competitors and industry leaders on TikTok can be useful for optimizing content strategy, setting realistic goals and expectations, and identifying areas for improvement. 

Many benchmarking reports emphasize the importance of analyzing your own data alongside industry benchmarks. This allows you to track your progress and measure the effectiveness of your specific social media strategy. 

What Does the TikTok Benchmark Report Cover?

Recently, Rival IQ released their 2024 TikTok Benchmark Report, analyzing key metrics commonly observed across many of today's social media platforms. Some of these key benchmarks, such as engagement rate, are frequently used as key performance indicators (KPIs) for many brands. 

To better understand the one way intersection between metrics and KPIs, or to have a baseline understanding of reading benchmark reports, check out this blog! 

In this 2024 iteration of the TikTok Benchmark Report, the following metrics are assessed:

Engagement

Reach

Other

What are the 2024 TikTok Benchmarks?

Knowing that engagement rate is one of the most important metrics to track - more on that later - here are the industry benchmarks for engagement rate by view. 

Sports Teams: 9.2%

Nonprofits: 5.2%

Influencers: 4.9%

Alcohol: 4.3%

Higher Education: 4.1%

Media: 3.8% 

Travel: 3.0%

Tech & Software: 2.9%

Fashion: 2.7% 

Health & Beauty: 2.7% 

Retail: 2.6% 

Food & Beverage: 2.6% 

Home Decor: 2.5% 

Financial Services: 1.9% 

You can check out the full list of benchmarks in the report here. 

What Metrics Should My Brand Track on TikTok?

After considering this long list of metrics, brands should evaluate which ones are most suitable as KPIs to fulfill their overall TikTok strategies. 

Rival IQ emphasizes engagement rate - a metric used to measure how much a social media audience interacts with a piece of content - as one of the most prevalent metrics for them when benchmarking brands across many industries. Engagement rate is a versatile KPI, ideal for brands with diverse objectives like boosting brand affinity, awareness, and even sales conversions.

TikTok is a highly engaged platform in general, so it is also important to understand the context of how this percentage is shifting non-linearly when it comes to both engagement rate by followers and engagement rate by views. 

The data shown in Rival IQ’s articles suggests that engagement rate by follower count skews higher for accounts with smaller follower buckets, left-skewed distribution in statistics terms. As Rival IQ suggests, high-follower accounts might have a lower average views per follower compared to smaller accounts. 

This means a smaller portion of their total followers actually see their content (due to the algorithm or other factors). This can also be attributed from basic mathematical effects: if engagement is still happening (likes, comments, shares) but on a smaller subset of viewers (lower views per follower), the engagement rate per follower will naturally be lower since the denominator (total followers) is much larger. 

Many brands might be tempted to prioritize follower growth as their main KPI, but in reality, we often categorize this as a primary vanity metric. A vanity metric is a statistic that looks impressive at first glance but doesn't necessarily translate to any meaningful business results when observed in isolation. 

Our Perspective on Vanity Metrics:

We typically observe follower growth as a byproduct of high engagement across many different platforms, and TikTok definitely follows that trend. 

On TikTok, prioritize engagement rate as your key performance indicator (KPI). 

Track follower growth as a secondary metric, as a strong correlation often exists between the two. 

This allows you to focus on creating high-quality content that fosters engagement, which will likely lead to organic follower growth over time. This can further trickle down to utilizing the other metrics such as videos per week, hashtags per video, and videos with mentions, which can also contribute to overall optimization strategies. 

Newly arriving brands into the platform, or newer accounts in general, may want to consider engagement rates per view over followers because of the mathematical inflation that is likely to occur when adding follower counts into the mix. A high engagement rate by followers with a very small audience might not be a true reflection of your content's overall effectiveness. It simply means a small group of followers is highly engaged, but it doesn't necessarily indicate broad appeal. 

As your follower base grows, you can start tracking both engagement rate by view and engagement rate by follower. This provides a more balanced view of your content's effectiveness. You can also start comparing your engagement rate by follower to industry averages - but it is also important to consider research on follower buckets for accounts as we have discussed the impact of follower counts on these percentages.

As a disclaimer, nothing will outweigh the importance of leveraging your own benchmarks. Industry reports offer a starting point and can help you understand your performance on a broader TikTok landscape. Focusing solely on industry benchmarks can be discouraging if your numbers fall short initially. Tracking your own progress allows you to celebrate improvements and measure your success based on your own growth trajectory.

Want us to bring both personalized and industry benchmarking into your brand? Send us a message!

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Are All Social Media Metrics Created Equal?

There is more to analyzing social media posts than just counting the number of likes a post might have or seeing how many views your video may have received. The significant growth of the data analysis industry has extended to social media, and the demand for its implementation is crucial for executing digital marketing strategies. Optimization techniques in social media marketing are frequently implemented through the use of metrics.

Before diving deeper into the application of metrics in the world of social media, it would be appropriate to define what a metric is. 

A metric can be generally defined as measurements used to evaluate performance. In the context of social media, metrics can give us insight to how well a particular post, channel, or campaign is performing. Some of the most commonly observed ones in measuring social media content are as follows:

EngagementThe number of interactions in a post. These interactions can come in the form of likes, comments, shares, etc. 
ImpressionsThe number of times the content has appeared to users. 
ReachThe number of unique users that saw the content. 
Click-Through Rate (CTR)Percentage of users who clicked on a link compared to its audience exposure. Usually divided over impressions.  

The overall pool of metrics used in social media is vast and highly dependent on objectives, platforms, and the level of analysis. Today, we aim to answer the specific question of which social media metrics carry more weight when it comes to analyzing performance.

We will take a look at each of the most prominent metrics used to analyze social media performance and analyze them on a platform-by-platform basis.

How Social Platforms Weigh Metrics Differently 

Engagement is likely the most commonly used metric to measure performance on every social platform. This metric generally accumulates all the different interactions that are unique to their respective platform to create a holistic measurement of content relevancy and how well the audiences are resonating with a post.

On Meta platforms, specifically Facebook and Instagram, engagement is considered a significant variable when it comes to prioritizing a post's ability to spread to a broader audience. Among different types of engagement, both platforms heavily prioritizes likes, comments, and shares when it comes to fueling this visibility. Engagement on Facebook is also thought to be more personal, considering the network's tendency for personal connections with friends and family. 

On Instagram, as a platform with a greater emphasis on visuals and a robust influencer culture, engagement takes a similar form to that on Facebook (likes, comments, and shares). However, on this platform, engagement may be derived more from what the viewer finds visually appealing.

The metrics themselves, when it comes to engagement, may start to take a different form when we start comparing platforms such as LinkedIn and X (formerly Twitter). 

For example, LinkedIn includes 'Post Clicks' and 'Follows' as additional metrics when calculating the total engagement for each piece of content. These 'Post Clicks' differ from Meta's 'Link Clicks' as they record not only direct user interactions with any given links but also interactions with any clickable elements within a post (such as images or profiles). The trade-off with this method of recording clicks is that it can inflate the click-through rate (CTR) and complicate efforts to standardize clicks across different platforms. This is an important factor to consider when understanding why total engagement may appear in larger quantities for this platform. When it comes to weighing various forms of engagement metrics, LinkedIn's 'Follows' can indicate a higher level of engagement, as they demonstrate users' intent to see more of your content in their feeds.

At its current stage of rebranding, X may be the most challenging platform to analyze. Formerly known as Twitter, the platform's users have become accustomed to its posts, which were normalized as 'Tweets,' aligning well with the platform's name. Due to its ongoing changes, X might undergo a renaming process that incorporates metrics along with post names. Content on X consistently prioritizes trending topics and timely information, resulting in a high level of engagement through the 'Retweet' metric. They are often used as a measure of how viral or shareable a tweet is, and they contribute to the overall engagement level of a tweet. Clicks on X may carry less weight compared to the previously mentioned platforms, as recent studies indicate that this platform has the lowest average percentage in click-through rate (CTR). This could be a result of the skewness towards higher impressions per tweet. Additionally, differences in ad performance between platforms might also contribute to this phenomenon.

Navigating this sea of metrics is a complex yet necessary task. With each platform having its unique metrics landscape, understanding the significance and weight of these metrics can provide valuable insights tailored to the context of each platform.

Which Metrics Should I Prioritize? 

When evaluating which metrics to prioritize in regards to measuring success for a campaign or post, it's highly important to factor in key performance indicators (KPIs). KPIs help measure the success of content as they are quantifiable metrics that provide a clear understanding of how well a campaign or post is performing in relation to those goals. It's important to note that all KPIs are metrics, but not all metrics are KPIs. 

When thinking about a social media post, metrics can cover a wide range of engagements and impressions. Let's say we're creating a promotional post with the goal of attracting more users to visit a website through the link in the description. A good KPI to measure the success of our objective would be clicks (or CTR). Other forms of engagement, such as likes or comments, might not be weighted as heavily as they are usually not going to correlate to an increase in website traffic. Being able to align social media posts with an objective allows you to transform any of these metrics into a measurable KPI. These will greatly enhance your understanding of how effective your social media strategy is and whether you're reaching your goals.

Below are a few more examples of common social media objectives, along with some of their corresponding KPIs:

Objective: Website Traffic KPIs: Clicks, Click-Through Rate (CTR)
Objective: Brand AwarenessKPIs: Reach, Impressions
Objective: Showcase New ProductKPIs: Likes, Comments, Shares
Objective: Build CommunityKPIs: Page Likes, Follower Increase

Our experts at Random are ready to create data-backed digital campaigns for your business! Reach out to us below to learn more about how we can work together. 

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Random Chart of the Month - What Makes THE Top World University Rankings?

Brandon Kim

June 2023

With graduation season in full swing, this was the perfect opportunity to bring out our Random Chart of the Month to discuss the Times Higher Education (THE) World University Rankings! 

The Data

THE is known for annually providing a comprehensive list of university rankings, which ranks institutions based on different factors. The formula for the ranking given to schools from all over the world in this list featured multiple factors such as teaching quality, research influence, international outlook, staff, and more. 

Based on the recent scores provided by THE, the top three schools by rank include the University of Oxford at first, Harvard University at second, and a tie for third between the University of Cambridge and Stanford University. 

This month's analysis will dig deeper into some significant scoring factors contributing to these schools climbing the ranking ladder. 

We will use a similar modeling method from our previous blog last month when we analyzed CO2 emissions from vehicles. For the context of this university dataset, we will want to find some significant predictors that propel a university's overall score. 

The Analysis

Contrary to the previous iterations of our Random Chart of the Month, we wanted to initiate our analysis portion before the visualization to contextualize the backbone of our modeling method. 

When we worked on a multiple linear regression model from our last blog, we introduced independent and dependent variables, which both construct the contents of this data mining technique. As a refresher, independent variables are our set of predictors that influence the value we are trying to predict, THE's overall university score, also known as our dependent variable. We call it a Multiple Linear Regression because we have more than one independent variable, but any simple or multiple linear regression models will generally have one dependent variable. 

When fitting the most optimal model, which sets THE's overall university score used for ranking the top schools as the target of our prediction, we can construct this index to define our variables:

Independent Variable(s)
ResearchDerived from various factors reflecting the university's strength of research. 
CitationsMeasures impact of citations received by research publications affiliated with that university. 
Industry IncomeA higher value represents stronger connections and financial support from the industry (applied research, knowledge exchange, and technology transfer) for the university. 
Student:Staff RatioRatio of the number of students to academic staff or faculty members. 
% International StudentsThe proportion of students that come from countries other than the country in which that university is located.
Dependent Variable
Overall University ScoreTHE's complete university score to rank their top schools annually. 

As we progress with our analysis, we will be able to conclude the statistical impact that these sets of independent variables have in contributing to a high overall university score and what component allows schools such as Oxford, Harvard, Cambridge, and Stanford to score at its peak. 

The Chart

Having utilized a ranked bar chart in our previous blog, which used a similar modeling method, we wanted to showcase another visualization that can help understand the level of impact a set of predictors can have on what we are trying to predict. This time, we constructed another bar chart that targets the significant, independent variables we mentioned in our analysis and their per-point impact on the university score:

After verifying the necessary assumptions required to run a multiple linear regression model on this data, we can estimate coefficients for each of the predictors we used for modeling THE's university scores.

The values at the apex of each bar represents the coefficients we estimated for the predictors. One interpretation in our example would be: for every percent increase in a school's international students proportion, we expect that school's overall score to increase by 4.921 (while keeping other variables constant). If we were to write out the full estimated model as an equation, we would construct this:

From this equation, it becomes a matter of plugging in values for these variables to estimate where a school might end up in its overall score. 

Based on the magnitude of each coefficient, we can see that a change in the proportion of international students in a school impacts the overall score the highest. Internationalization appears to matter a great deal when considering an increase to a school's score. This aligned with schools ranked in the top ten, which averaged about 30% of their student base coming from out-of-country. 

Another variable worth noting is the school's student-to-staff ratio, which seems to carry a negative coefficient value in our model. This indicates an inverse effect on a higher overall score when that ratio increases - signifying that a smaller student-to-staff ratio is better for a more significant score. A smaller ratio could indicate an increased individualized learning experience, mentorships, and more. 

Although these variables proved to be rather significant in deciphering how THE constructs their university scoring, plenty of other factors likely establish the top schools' tier. They are still great indicators of why you would expect some of these institutions to make the top in various rankings consistently. 

Continue to tune into our ˜Random Chart of the Month' each month! We're experts at analyzing all kinds of data, but especially social media. Let us help you build a social media campaign backed by data and results. Reach out to us below! 

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Random Chart of the Month - Which 2022 Vehicles had the Most CO2 Emissions?

Brandon Kim

April 2023

As we begin to close the curtains on Earth Month, this is an excellent opportunity to bring in April's iteration of Random Chart of the Month. This time, we will analyze the publicly known Fuel Economy data for vehicle manufacturers in 2022 provided by the Environmental Protection Agency (EPA).

The Data

Since the mid-1970s, the United States government has been collecting and publishing Fuel Economy data to discern the availability/cost of oil and the environmental impact on emissions. The EPA measures and tests fuel economy and emissions, which is then publicly available on their government website. 

We will be looking specifically at the 2022 iteration of the Fuel Economy data. It covers a wide range of variables for each recorded vehicle for that year, including CO2 (carbon dioxide) emissions, which we are highly interested in this analysis. We can visualize this data relating to how much vehicles emit based on various features such as brand and fuel type. We can then perform some data mining techniques to see what other vehicle components play significant roles in how much CO2 a car emits. 

The Chart

When evaluating this extensive dataset, three different variables measured CO2 emissions by each vehicle: city, highway, and combined. We also had a categorical variable naming which manufacturer belonged to that vehicle. With this information, we were able to discover, by a multiple bar chart, which manufacturer averaged the most CO2 emissions (for both city and highway): 

In 2022, Rolls-Royce averaged the highest among all manufacturers in this data based on these averages for combined CO2 emissions for both city and highway driving cycles. On the lower end, we had Mitsubishi Motors Corporation averaging the least combined CO2 emissions. 

The Analysis

Although it is fun to see how different manufacturers compare in a plot for CO2 emissions, there are further questions we can answer when we evaluate this large government data set. Now that we have a general quantification of where different manufacturers averaged CO2 emissions for their 2022 models, we can also look into other factors about those vehicles contributing to more significant emissions. 

This is where we utilized some data mining techniques to fit a statistical model, specifically a multiple linear regression. Multiple linear regression is a statistical method that models relationships between a dependent variable and two or more independent variables. It is essential throughout the entire modeling process and interpretation to understand what these variables mean: 

Tying into the Fuel Economy data, our dependent variable in question would be the combined CO2 emissions by the vehicle. We want to analyze and find out which other features in a vehicle included in the data play a statistical significance in contributing to how much a car pollutes. After meeting certain statistical assumptions for this modeling technique (we will likely cover these techniques in a future analysis blog, so stay tuned!), we can fit a multiple linear regression model to determine those predictors. 

After running this model, we concluded a handful of predictors that impact combined CO2 emissions. One significant feature in a vehicle that strongly correlated with CO2 emission was the vehicle engine's method for air aspiration. Across all vehicle brands, cars with the supercharged process for air aspiration seemed to average higher levels of combined CO2 emission. This is likely related to how this engine type requires more fuel for its combustion process, increasing the overall amount of pollutants. Turbocharged engines averaged the lowest combined CO2 emissions among the different air aspiration descriptions. 

Another significant predictor for CO2 emissions, to no one's surprise, is the vehicle's class description. This feature directly relates to the size of the vehicle, which should broadly impact mass and, ultimately, how much power is required to move the car. Among all class descriptions, vans averaged the highest combined CO2 emission, while compact cars emitted the least. 

Although we could name some significant predictors for CO2 emissions, some factors outside the Fuel Economy data can contribute to substantial emissions. Some non-recordable variables include a person's driving style, overall traffic conditions, vehicle maintenance, etc. A special shoutout to vehicles marked with the ˜electricity' fuel usage, which averaged zero CO2 emissions. #SustainabilityRocks

Continue to tune into our ˜Random Chart of the Month' each month! We're experts at analyzing all kinds of data, but especially social media. Let us help you build a social media campaign backed by data and results. Reach out to us below! 

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Random Chart of the Month - Top Instant Noodles by the Ramen Expert

As we all prepare to look back on the passing colder season (or perhaps our not-too-distant college days), there's no better time to introduce our first Random Chart of the Month, featuring instant noodles! 

Specifically, we are looking to visualize and analyze a large dataset provided by The Ramen Rater, a seasoned expert who has reviewed instant noodle brands from around the world for over 20 years. His dataset, known as The Big List, contains over 4,000 entries of the instant noodles' variety, brand, style, country of origin, as well as a personal rating on a numerical scale of 0 through 5. This dataset is ongoing and continually updated, so ramen enthusiasts should go check out The Ramen Rater and all his cool content! 

The Chart

With the sheer amount of instant noodles entries alongside the categorization in the dataset, a prominent analysis question was: which countries do it better? Taking the variables on the expert rating of the instant noodles as well as their origin, we were able to generate the following geographical heat map:

This geographical chart measures, by country, the average ratings of all instant noodle brands and codes them into a color scale. The darker green signifies the higher end of the 0-5 rating scale, while the light yellow is the lower. 

The Analysis

At first glance, we can see a significant increase in the amount of countries coded with darker green when we evaluate the eastern side of the world map. It probably doesn't surprise most of us to see more of that darker green populate around Southeast Asia, as that area marks the origin of instant noodles (Japan, in 1958). 

When ranking the average ratings among countries with at least 30 reviews, instant noodles branded from Malaysia came out on top with the highest average rating of 4.21. This also aligns with a YouTube video by The Ramen Rater, where he gave a 2022 ranking of the top 10 instant noodles of all time, with Malaysian-branded instant noodles appearing five times. South Korea and Japan closely follow with 3.88 and 3.86, respectively. 

The three most prevalent countries regarding total reviews in this list were Japan, South Korea, and the United States. These three countries combined branded about half of the 4,298 instant noodle entries in this iteration of The Big List. Japan, the inventor of it all, leads all countries with the most entries with 845 instant noodle ratings. The large variability by the sheer number of instant noodles branded in Japan makes their average rating of 3.86 rather impressive. 

Knowing the powerhouse that is Southeast Asia when it comes to instant noodles, there's another analysis question we can consider: what are the best packaging types for instant noodles? 

This same dataset categorized each instant noodle with a variable relating to its packaging type, for instance, whether they came in a pack, box, bowl, etc. The three most dominant types were instant noodles packaged in packs, cups, and bowls (about 93% of all instant noodles in this list were from these categories). Averaging out the same rating scale by packaging type, instant noodles in packs average a rating of 3.82, followed by bowls at 3.69, then cups at 3.47. We also constructed a 95% confidence interval for the ratings of each packaging type to really compare the three means: 

Confidence intervals are a way to show some accuracy in our estimation. In statistics, we often use these intervals to make an educated guess about a larger population or, for relevancy, a larger population of noodles. A practical interpretation of a 95% confidence interval is: if we ask our ramen expert to rate the same number of instant noodles (falling in the Pack category) a hundred times, we expect about 95 of our average ratings to fall between 3.78 and 3.86. 

When deciding on the differences in averages with this method, we generally look for any overlaps between each interval. If such overlaps existed, we could conclude that one packaging type wasn't too significantly different from the other based on average ratings by our ramen expert. For our data, we can see averages for the packaging type of packs were slightly higher than bowls and even more so than cups. We also have a smaller margin of error for the ratings of instant noodle packs, considering almost half of the noodles populated this packaging type. 

Using our ramen expert's list, we visualized and learned of higher review ratings from Southeast Asian-brand instant noodles. We also supplemented these findings by concluding that the packaging type of packs is generally favorable among the same metric. To our delight, it is clear that the world of instant noodles is varied, and there is something for everyone on the globe to try. But hardcore instant noodle enthusiasts may consider at least trying some of those Malaysian-brand noodle packs or even seek the veteran opinions of The Ramen Rater. 

Continue to tune into our ˜Random Chart of the Month' each month! We're experts at analyzing all kinds of data, but especially social media. Let us help you build a social media campaign backed by data and results. Reach out to us below! 

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