28 Key SMM Metrics: How to Track and Calculate [cheat sheet] #1
Disclaimer: This article has gotten incredibly large, but I believe it will be useful enough for you, so I don’t want to cut it up and turn it into a mash-up. Yes, it’s not the most popular position, but it seems right to me that the reader should not consume a lot of simple content, but keep the skill of chewing. Yes, you’re unlikely to find anything new in this article if you already have a mature product with an SMM strategy lined up and fine-tuned, but if you’re just starting to build these processes in your company, you’ll learn how to work with basic metrics and data to truly understand what you’re doing with your social media. In Part 1, we’ll talk about how to track key metrics, and in Part 2, you’ll get an actual list of the metrics with definitions and formulas.
In 2023, it was estimated to be over 4.89 billion users worldwide who actively interact with each other on social media. Meaning, this channel becomes a “big fish” for marketers and businesses in general. Imagine: by 2027, social media ad spending is projected to total $130.5 billion. The density of advertisers is getting higher, and in this competitive race, it is doubly important to pay attention to tracking key SMM metrics.
The choice of users for monitoring depends on the tasks outlined and the channels you choose for social marketing strategy. Everything boils down to a clear understanding of your goals for social media promotion and the skills of monitoring, calculating, and interpreting your key SMM metrics.
In this article, you will find the selection of the most basic social metrics, their purpose and formula (where applicable). You can use it as a cheat sheet 😉
How to monitor chosen metrics
I can outline several approaches to forming high-level analytics on social media.
In-built analytics. No doubt, if you have not so many social channels and metrics you monitor available within the basic analytics of the social media itself, there is no need to reinvent the wheel. Such popular social networks like Instagram, Twitter, Facebook, have their own in-built analytic tools. It is highly convenient.
Manually. You can always use Google Spreadsheet and fix data there. It seems a lot of hard work, however, there are numerous formulae available to automatically parse and collect relevant data from most socials. From my personal experience, I know it works with:
- YouTube
- Alexa rank
- Quora
- Spotify
- Soundcloud
- GTmetrix
- Bitly
And this is not a complete list. For example, below is the formula to export likes from Facebook:
Even if you prefer inputting data manually or some formula does not work (it also occurs, and you need to look for working alternatives), you can automate data visualization with the help of charts and(or) discover the force of AI for Google Spreadsheet. So I would not call manual data collecting an outdated thing to do ;)
Specialized services. There exist specialized analytic tools to monitor certain metrics. There is no universal solution, and the choice of tools mainly depends on the data you want to see. I can roughly name: Sprout Social, HubSpot, IZEA, BuzzSumo, Google Analytics (however, I would now cautiously recommend this instrument), Followerwonk, Rival IQ, Iconosquare, Tailwind, Audiense, quintly, SparkToro, Brandwatch, Agorapulse, Brand24, Mention… There are lots of them. I prefer ecosystem solutions, meaning trying to collect maximum analytics from various channels within one tool for data consistency. So, the choice of a specific tool directly depends on the metrics you monitor and the channels you work with.
Custom solution. There are currently quite many no-code builders to work with analytics on the market. I can recall Simplified, Retool, SparkToro, Altinity. They have various customizations and approaches to data collection and visualization. The key value of such tools is that you can integrate and draw data from various channels, create dashboards, and see all the info within one tool. However, there is also a problem (in fact, common for any service): you must know how to calculate metrics, compare them with database, and check if the information is correct (and you understand how the number was received). Data reliability is the foundation of a realistic analysis and interpretation.
We could separately mention working with pure data, for example, the Big Query and Looker (Google Data Studio) bundle. This, too, is essentially a solution that can be customized to your needs. “Raw” data is easier to combine with each other the way you want and use exactly the counting mechanisms you need.
What to consider? You should choose an approach based on your needs. Do not try to overcomplicate where it is not required. Choose the tool or tools in your weight class: if you need as many as 5–6 metrics, there is no need to make a BQ dashboard or buy an expensive box solution. I find it essential to keep a resource-effective approach and not burden the business with redundant software solutions if it is not required.
Here I wrap up the first part of this article, but very soon we’ll talk about key SMM metrics, how to define and how to calculate them.
