Above you see the massive increase in time spent by users on social media in the last decade. Although the growth seemed to slow starting in 2020, users still spend around 2.5 hours per day on a variety of social media platforms. For many mobile users, the first thing they do in the morning is check social media notifications and it’s the last thing they do at night. Many users even wake up to check notifications sent while they were asleep. The situation is getting worse as smartwatch users feel a vibration in their wrist each time a new notification arrives and, even if they don’t read the notification, it disrupts their day.
As a business, how do you take advantage of users’ addiction to social media? The first step is to monitor the performance of your social media marketing efforts, how those efforts translate into improved market performance, and how you can adapt your strategy to further improve performance based on insights provided through your metrics.
Social Media Metrics that Matter
Which social media metrics matter? No one can answer that because your goals determine which social media metrics affect marketing success. So, if your goal is to increase ROI, you use metrics such as conversion rate. If your goal is to build a community, one metric you’ll likely use is the number of returning visitors from your Google Analytics. If SEO is your goal, the Google Webmaster Tool displays where you rank for each keyword you use.
Simply collecting huge amounts of data because it’s there doesn’t make any sense. It takes time away from other important tasks and makes it harder to see problems in time to make corrections. You can’t see the forest for the trees. Thus, the task involves identifying which metrics to monitor rather than getting swamped with the vast number of metrics available on Google Analytics and the analytics available through the social media platforms you use.
How do you know which social media metrics matter? You need to list KPI — Key Performance Indicators (also called Key Predictive Indicators).
A Performance Indicator or Key Performance Indicator (KPI) is an industry jargon term for a type of Measure of Performance. KPIs are commonly used by an organization to evaluate its success or the success of a particular activity in which it is engaged. Sometimes success is defined in terms of making progress toward strategic goals. Accordingly, choosing the right KPIs is reliant upon having a good understanding of what is important to the organization. (Wikipedia)
KPIs are a function of your industry and your goals. As you can see in the image above, KPIs, and the metrics used to assess them, come from your mission, goals, and objectives. They should reflect controllable elements most likely to affect whether you achieve your goals. Once you identified your KPIs, you need to figure out how to measure them. Then, you systematically collect relevant data.
An example might help.
Let’s say, like many businesses, you use social media as a way to engage your target market. We first need to identify what we mean by engagement. Commenting and sharing are two common elements of engagement and make suitable KPIs. Measurement data on comments and sharing come from various sources, such as your website, your Facebook Fan Page, and other social platforms such as Twitter.
You need a process for systematically collecting this social media metric. Maybe you set up a spreadsheet where you enter data on commenting and sharing from each social network on a daily basis so it’s all in one place. That’s a little time-consuming and a number of companies offer this as a service using software to integrate insights from various social platforms.
As I said, the actual metrics you collect are a function of your business, but I put together a list of common KPIs that fit most businesses regardless of industry. This list is interactive. You can vote for the ones you find most valuable to move them up on the list (thus helping others who don’t know which metrics they should monitor) or add new metrics to the list if your favorites aren’t there, making the list more valuable for everyone.
Visualize Your Social Media Metric
People have a really hard time handling lots of numbers — they’re hard to interpret and compare across time. Transforming numeric data into a data visualization, such as a pie chart, makes it much easier for us mere mortals to handle massive amounts of data. Or, if you graph the data as a line graph or histogram, you more easily see changes over time.
Using a little creativity, you can add qualitative dimensions to your data visualization or add geographic components, such as using a word cloud that shows words based on their frequency of usage, such as the one below. Pretty soon, it’s easy to see how things change over time and pinpoint factors and develop insights that make some actions better at producing desired results than others.
Or, for managing big data, you might need something like the network analysis visualization shown below. This visualization is called network analysis and it’s a great way to map the spread of your content or map linkages between your community.
Returning to our example above, after collecting social media metrics and displaying them, we might find increasing or decreasing engagement over time and identify which platforms are generating increasing engagement. If we map other changes over time, such as a contest we ran on Facebook or a change they made to the interface, we can determine which actions created the change.
Using social media metrics strategically
Managing your social media metrics involves 3 steps:
- Measure what matters, KPIs
- Visualize data, using the correct visualization makes developing insights easy, while the wrong visualization might obscure insights
- Make decisions based on your insights
Having all this data and creating pretty pictures with it is useless unless you can use the data to make better decisions. In our example, let’s say you see a large increase in engagement after certain types of status updates (like announcements of a discount). You know what’s working for you and should use similar status updates frequently.
Sometimes, even with your social media metrics appropriately visualized, it’s still hard to figure out what’s going on. For instance, if you see a steady decline in engagement on Facebook it may not be clear what’s causing it. But, knowing the decline is there signals the need for more information and may guide a study of the problem.
Translating metrics into returns
One of the first steps, after completing the steps above, is to bring all your metrics together on a dashboard. I love IBM’s Cognos as it’s easy to use and allows you to bring data visualizations from a variety of sources into one place, such as your Google Analytics data, internal data, and social media data. But, this is easier said than done.
Setting up Google Analytics
GA4 is a vast improvement over Universal Analytics, which the company is retiring soon. Data is collected around Events controlled by the user. By setting up events you can more easily monitor performance relative to the event. For instance, if you set up an event of subscribing to your newsletter, you can track subscriptions as a percentage of visits and collect other data. You can track events sent to third parties to determine performance based on the number of users who clicked the external link and the results from that external link.
Here are just a few of the actions you can track using events:
Adding tracking codes
When using Google Analytics, you get information about visits from social media, but you don’t know which posts generated the click-through to your website. The same goes for your email messages. You can easily fix this by adding a tracking code to the URL used on these platforms. As long as the URL includes a #, ?, or &, browsers ignore the tracking code and deliver the user to the address that precedes the symbol. You can configure your tracking code in any way you want, however the UTM (urchin tracking module) standard makes it easy to track common elements such as source and campaign. Here’s an example of a UTM tracking code attached to a URL.
Each code is unique and refers to a specific post. When you visit Google Analytics, you’ll see visits generated by each tracking code listed in the pages section of the report as if they were different pages, even though the visit was to the same URL. You don’t have to use all the UTM parameters and you can name them anything you want. For instance, you might have a source of Facebook and a campaign of post-season sale on sweaters. A spreadsheet helps you make sense of your tracking codes if you create a lot of them, so you know exactly what’s working and what isn’t.
The best part of tracking and events is you can follow the customer’s journey from the first visit through to making a purchase and everywhere in between. Thus, you can determine that a particular link in your email message sent on Feb 25 generated a lot of visits but most visitors dropped off after adding a product to their shopping cart rather than finishing the purchase process. That’s valuable information you can use to test various hypotheses as to why visitors dropped out of the purchase funnel. Armed with the results, you can improve performance.
The customer journey
Speaking of customer journeys, recognize that visitors may not make a purchase on their first visit. They may return a few times before they’re ready to buy. Multichannel attribution modeling is a tool to help apportion the value of a sale across all the campaigns that helped generate the sale. Also, don’t settle for losing prospective customers along the journey. Use remarketing to target past visitors with ads on social media to bring them back to make a purchase. Similarly, use your email marketing data to selectively message subscribers who visited your website by clicking a link but didn’t make a purchase by offering them an inducement to make the purchase.
Social media disasters
In a recent series of posts for an upcoming book, I discussed the importance of Social Media Metrics for helping you run your business. But, in the wrong hands, those social media metrics can prove disastrous. Here are a few of my favorite social media metrics disasters and some suggestions for how to avoid them:
Social media metrics disaster #1 – Developing a false sense of security
Recently, I interviewed a reputation management agency representative (who will remain nameless for obvious reasons). During our conversation, he casually mentioned a client who ended up in hot water because their system didn’t detect negative comments. Instead, the negative comments reached the FDA (the client was in the drug business — the legal kind) and the first hint of trouble came from an official letter informing of an official FDA inquiry about the drug.
Bad social media metrics can be worse than none at all. Placing too much faith in your social media listening deafens you to other sources and tactics for hearing negative comments about your brand. And, if your listening post only provides assessments of sentiment or changes in sentiment, you may be missing a lot of negative comments until it’s too late.
Social media metrics disaster #2 – Acting without enough information
I was at a social media analytics meetup during Social Media Week DC a few weeks ago and learned of some major disasters based on incomplete social media metrics. Feastie Analytics showed social media managers for food manufacturers and restaurants that approached bloggers and other influencers, only to find they promoted vegan, vegetarian, or other lifestyles inconsistent with the food brand. Or, the blogger only blogged about Italian, Indian, or Asian cooking that had nothing to do with the food brand or restaurant.
That’s because the social media manager didn’t have enough information. Their social media metrics showed the influencer was influential about food, but failed to gather information regarding the types of food promoted or their food preferences.
Of course, this happens in other content areas — not just food. Using ineffective tools might identify influencers who actually post negative comments about your brand. Now, that might not be a bad thing if you’re trying to reduce the negative comments, but it can be deadly if you use the wrong approach because your social media metrics only identified them as an influencer, not that they were an antagonist rather than an advocate for your brand. Now you’ve just given the influencer vitriol for their next blog post!
Social media metrics disaster #3 – Automating your decision-making
A number of firms are popping up offering to automate your decision-making by using algorithms based on your social media metrics. Now, this may sound like a great time saver and a way to handle the massive amounts of information many firms generate. Now, don’t get me wrong. Some types of automation are necessary. Otherwise, firms would drown under the massive amount of information generated every day. But, letting a machine make decisions for your firm can result in a multi-billion dollar disaster. Even supercomputers, such as Watson (you know, the one who beat humans at Jeopardy) or asking ChatGPT (which is equally likely to deliver incorrect results), shouldn’t make decisions on their own.
Sure, these computers are REALLY good at retrieving information (ie. Jeopardy) and making bounded decisions (decisions with known, stable variables — such as medical diagnosis), but they’re only as good as the algorithms fed into them.
And, in business, we don’t often deal with bounded decisions. That’s especially true when we talk about consumer behavior. Consumer behavior is based on a dynamic set of variables (that change all the time). Hence, an algorithm that works today, might not work tomorrow. If you have a human reviewing the data and making informed judgments based on social media metrics, that’s about the best you can do with handling the dynamic nature of consumer behavior. But, using a machine to make decisions is just plain dumb.
Social media metrics disaster #4 – relying on big data
I once saw a visual depicting the dangers of using consumer data to build an image of the consumer. Using data mining techniques doesn’t give you a clear image of the consumer because consumer behavior is complex. With only behavioral variables, such as credit card transactions, magazine subscriptions, or even online clickstream data, we lack an understanding of WHY consumers behave as they do. Without this why, we lack the ability to predict future behaviors.
In the image I saw, a man carried a fishing pole and spatula while wearing a tutu and other things that just didn’t fit together. That’s because data may not depict our consumption behavior alone, but things we buy for others, when we loan our computer to a friend or even fraudulent charges.
Big data is great, but supplement it with some WHY-focused market research.
Solutions to social media metrics disasters
- Intelligently decide what information is necessary to make good business decisions. Don’t get dazzled by all the metrics available through a particular vendor. Look for vendors offering a solution that matches your information needs.
- Test your data collection tools before relying on them. Do some manual spot-checking to ensure you’re collecting ALL the information you need.
- Invest in tools that provide adequate data visualization. This is the intelligent place to use automation and this area is sorely lacking in many existing tools that rely on Excel spreadsheets and simple charts. As an example, look at perceptual mapping where the colors and sizes of circles contain critical information about competing brand attitudes. People don’t see the hidden meaning in numbers well, but visuals are compelling.
- Use big data with caution.
- Don’t stop. Using social media metrics is a moving target because the environment and consumers change all the time. Something that works today, might fail miserably tomorrow. Test assumptions.
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