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Types of Social Media Analytics

When we talk about social media analytics, there are several types of metrics we should be considering. Each type of social media analytics is vital. We need to be tracking more than just likes and comments — in fact, those don’t even tell the entire story. 

There are many types of social media analytics. Each is used by companies with a unique strategy to measure the success of their social media marketing campaigns. It’s best to understand the different kinds of analytics and how you can use each kind so that you can choose the one that works for you.

What is social media analytics?

Social media analytics is the collection and analysis of data points that help you measure the performance of your social media accounts.

These are the metrics that will help you assess your social media marketing strategy on both macro and micro levels. Besides helping you see how social media is contributing to your larger business goals, they can also help you gauge customer sentiment, spot trends, and avoid PR crises before they happen.

To track social media analytics, you’ll look at likes, comments, shares and saves, but you might also monitor mentions and discussion of your brand or consumer insights by practising social listening.

Social media analytics tools help you do all this math, while also creating performance reports to share with your team, stakeholders, and boss — to figure out where you’re succeeding and where you’re struggling.

Why you need social media analytics tools

Social media analytics tools help you create performance reports to share with your team, stakeholders, and boss — to figure out what’s working and what’s not. They should also provide the data you need to assess your social media marketing strategy on both macro and micro levels.

They can help you answer questions like:

  • Is it worth it for my business to keep posting on Pinterest?
  • What were our top posts on LinkedIn this year?
  • Should we post more on Instagram next month?
  • Which network drove the most brand awareness for our product launch?
  • What kind of posts do my followers like to comment on?
  • And many more.

Types of Social Media Analytics

Depending on the business objectives, social media analytics can take four different forms, namely, descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.

1. Descriptive Analytics (Is Reactive in Nature)

Descriptive SMA tackles the questions of “what happened and/or what is happening?” Descriptive analytics gather and describe social media data in the form of reports, visualizations, and clustering to understand a well-defined business problem or opportunity. Social media user comments analysis, for instance, falls into the descriptive analytics category. Comment analysis can be used to understand users’ sentiments or identify emerging trends by clustering themes and topics. Currently, descriptive analytics accounts for the majority of social media analytics landscape.

Descriptive analytics answer the question “what is happening”? “What happened”? These types of analytics cluster similar types of data together in order to produce a cohesive view. Comments and posts can be grouped together, for instance, for the purpose of sentiment analysis (as offered, for instance, by our very own SentiOne platform). Any time you gather a lot of similar data points in order to analyse them looking for patterns, sentiments, and/or trends, you’re dealing with descriptive analytics.

2. Diagnostic Analytics (Is also Reactive in Nature)

Diagnostic SMA analytics looks into the questions of “why something happened?” For example, while descriptive analytics can provide an overview of your social media marketing campaign’s performances (posts, mentions, followers, fans, page views, reviews, pins, etc); diagnostic analytics can distill this data into a single view to see what worked in your past campaigns and what didn’t. Enablers of diagnostics analytics include inferential statistics, behavioural analytics, correlations & retrospective analysis and outcome being cause and effect analysis of a business issues.

Diagnostic analysis focuses on the numbers: like counts, follower numbers, pageviews, reviews, shares, what have you. This type of analytics focuses on the performance of posts and campaigns and attempts to discern what made them successful. By comparing the performance of different campaigns, trends and consumer preferences can be discerned. Both diagnostic and descriptive analytics are reactive – that is, they are concerned with events that have already happened.

4. Predictive Analytics (Is Proactive in Nature)

Predictive analytics involves analyzing large amounts of accumulated social media data to predict a future event. Thus, it deals with the question of “what will happen and/or why will it happen?” For example, an intention expressed over social media (such as buy, sell, recommend, quit, desire, or wish) can be mined to predict a future event (such as a purchase). Alternatively, businesses can predict sales figures based on historical visits (or in-links) to a corporate website.

In contrast, both predictive and prescriptive analytics are proactive – as in, they attempt to predict trends, events, and shifts based on existing data. It can range from simple things, like predicting possible visits to a location based upon posts expressing that intention, to forecasting entire trends and phenomena based upon mentions. Social listening tools can help identify upcoming trends and shifts in consumer behaviour by analysing large volumes of social media data and indicating the shifting popularity of keywords.

5. Prescriptive Analytics (Is also Proactive in Nature)

While predictive analytics help to predict the future, prescriptive analytics suggest the best action to take when handling a scenario (Lustig, Dietrich, et al. 2010). For example, if you have groups of social media users that display certain patterns of buying behavior, how can you optimize your offering to each group? Like predictive analytics, prescriptive analytics has not yet found its way into social media data. The main enablers of prescriptive analytics include optimization and simulation modeling, multi-criteria decision modeling, expert systems, and group support systems.

Finally, prescriptive analysis is the analysis of data with the intention of providing the best way to proceed at any given moment. This can be applied to situations ranging from handling social media crises and incidents (“how well does this type of apology track with our target audience?”) to purchase preferences (“we’ve identified this group of customers – how do we optimise our sales process to their habits?”). Although it’s an incredibly useful form of analysis, it requires a lot of data in order to truly show its potential.

How to track social media analytics

It may seem like a daunting task, but tracking your social media analytics isn’t difficult. It just requires a little bit of planning and a lot of consistency. You’ve got this!

We’ve even made a template for you to plug your social media analytics report into at the end of this post.

Set S.M.A.R.T. Goals

It’s pretty much impossible to measure your success if you don’t actually know what success looks like. So great social media tracking begins with setting a goal for your brand.

To be clear: a social media goal is not the same thing as a social media strategy (although both are important).

A social media goal is a statement about something specific you want to achieve with your marketing activity. Your goal can be applied to something short-term and small (for instance, a single ad buy) or can be bigger picture (like a goal for your overall social media campaign).

Either way, we recommend using the S.M.A.R.T. framework for your social media goals to set yourself up for maximum success.

S.M.A.R.T. stands for specific, measurable, attainable, relevant, and time-bound.

  • Specific: Your goal should be as precise as possible. What exactly do you want to achieve? “Improve our Instagram account” is too vague. “Build Instagram engagement by 500%” is far clearer.
  • Measurable: Set some quantifiable indicators (a.k.a. hard numbers) to make success clear. For instance, “increase our TikTok followers by 1,000 this month.” Without having a goal that’s measurable, you’ll never know if you’ve achieved it.
  • Attainable: Listen, it’s great to want to reach for the stars, but setting the bar a little lower is going to make it much more likely that you’ll actually achieve it. Think baby steps here. If your goal is to push a million views to your website this week, but you just launched it yesterday, you’re only setting yourself up for failure.
  • Relevant: How does this goal fit into your overall plan? Go ahead and strive to get Rhianna to follow you back on Twitter, but make sure it’s clear why pursuing that goal is going to benefit your big-picture brand strategy.
  • Time-bound: Deadlines are key. When do you want to achieve your goal? If you can’t come up with a timeline, that might be an indicator that your goal just isn’t specific or attainable enough.

Conclusion

Social media analytics is the collection of tools, features, and data that help you to interpret social media statistics. Social media analytics are a great way for companies to measure the impact of their social media efforts and gain valuable insights into what their audiences like and don’t like.

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