According to a recent study by The McCarthy Group, 84% of millennials don’t like advertising. The use of ad blockers is on the rise - 41% in the last year - and if you’re thinking sponsored content is the best way around them, you’re wrong. Young people are even skeptical of that. Just 24% of readers scroll down on native ad content on publisher sites, compared to 71% for normal content. The cynical little tykes.
In short, getting your message out there is now more challenging than ever, and marketers need every tool at their disposal. Social media is a tremendously effective tool for doing this and getting heard. According to estimates, in 2016, 78% of all Americans used social media - more than 200 million people. Worldwide, the number amounts to some 2 billion. In AdMedia Partners’ ‘23rd Annual Market Survey’, 71% of marketers who responded said they will buy and spend more on social media in 2017, and it shows no sign of dropping any time soon.
To ensure all of this money is well spent, social media activity can now be leveraged to generate a vast amount of data and insight. Data is vital to understanding the audience - who it is, the kind of creative that will best appeal to them, which channels will yield the most engagement and ROI, when to post, and so forth. It is, however, not easily done and many marketers fail to use it to its full potential, rendering their campaigns ineffective and wasting valuable opportunities.
Which Metrics Matter Most
Firstly, it is important to establish the metrics that you are going to measure. What data will best show whether you are achieving the goals you’ve set for your social campaign?
Wes Finley, Global Operations Lead of Social Connections at Coca-Cola, notes: ’A successful strategy needs to have well-defined goals and expectations before the project or campaign begins. Leaders need to avoid falling in love with flashy pitches. Instead, they should be investigating the meat of any proposal: the data. What are baseline performance expectations? How have previous campaigns in this space performed? And what metrics will identify this project as a success or a failure?’
According to the 2015 Social Media Marketing Industry Report, the top two benefits of social media marketing are increased exposure and traffic — with 90% of marketers citing an increase in exposure and 77% saying increased traffic. These are fairly simple to measure. But while a large number of followers on Twitter and Facebook may mean a certain degree of exposure, it can be misleading. A post marked as 1 impression may have been barely looked at as someone scrolls through their feed. Klout is an online influence gauge that enters several data points across your various social platforms - such as followers, retweets, clicks on links - into its algorithm and gives you a score as to how successfully you are building your social media presence. Tools like Google analytics and Bit.ly are easy ways of seeing if people are clicking the links you’re putting out on different platforms to make sure they are actually increasing traffic to a place where they can buy something. UTMs are also important. UTM codes are bits of text you can add to a URL that give Google Analytics and other analytics tools more information about each link. You can use the UTM variables within the link to track general information, like how much traffic you’re getting from social media, as well as more granular details such as how much revenue you get from your Twitter bio.
The biggest challenge facing social media analytics now is looking at deeper metrics. In terms of engagement, it’s far more important to look at clickthrough rates and audience retention than how many likes and comments your posts get. Your social media marketing goals also require data that aids your decision-making and correlates with your company’s KPIs. In a survey conducted by Altimeter, it was found that only 34% of businesses feel there is a crossover between their social objectives and their company’s overall targets. You need to know that your social media efforts are generating leads and translating into customers. To do this, you have to be able to plug your marketing analytics into a contact database or CRM. Doing so allows you to connect marketing activity directly to sales activity and provides a full-funnel view of your efforts, as well as balance the right ratio of inbound and outbound methods.
It’s not just a case of directly measuring how well marketing and sales are linking up. You can also look at how customer action translates to sales over time. For example, does someone becoming a ‘fan’ on Facebook translate into increasing the amount they spend with you? If you put a ‘Buy’ button next to the Like and Message buttons, will more people click it? There is data to measure every aspect of your social media campaign that can be useful in some way, but ultimately the main question you have to ask yourself when looking at a metric is whether you know what action to take based on it? If the answer is ‘no action’, then the metric you’re looking at is probably pointless.
Many higher quality ‘second-layer’ statistics are inaccessible. Context is particularly important to measure, but exceptionally difficult. Words are often ambiguous, and semantics and syntax are difficult enough for humans to get to grips with, let alone machines. Noam Chomsky’s famous example of ‘colorless green ideas sleep furiously’ illustrates the problem of semantics. He compared that sentence with ‘furiously sleep ideas green colorless.’ Chomsky noted that the first sentence, though nonsensical, is grammatical, while the second is not. A good Natural Language Processing system will learn that the word ‘hot’, when used in close proximity to the word ‘curry’, will usually mean ‘spicy’, as opposed to ‘hot’ in the more literal sense of burning. However, there is also every chance that it could also mean hot as in burning. Marketers need to be aware that this could be misleading and be careful to check samples of the data themselves to ensure they are not being misled. Troy Janisch, VP of Social Intelligence at US Bank, says, ‘Sentiment and Emotion algorithms are accurate, but struggle with social data because messages don't provide enough text for analysis. This means that marketers need to audit sentiment for accuracy and move beyond percentages for sentiment analysis and reporting. Net sentiment is better. It's even better with a few adjustments based on influence and the ratio of mentions to unique authors.’
The Best Platforms For Measurement
One major obstacle for social media marketers looking to analyze their campaigns is the declining popularity of Twitter. Matt Kautz, Head of Business Intelligence, Analytics & Research at Machinima, notes: ‘I think the biggest challenge to social media measurement is the declining popularity of Twitter. Because all Twitter data is publicly available, it has been the most prominent, useful data set for social analytics. As Twitter becomes less mainstream, more niche, that data set becomes less valuable, and there aren't any obvious sources to take its place.’
Facebook is also an excellent resource, offering in—depth analytics around your audience’s response, however, the newer, more youthful Instagram and Snapchat don’t lend themselves immediately so well to analytics, only making limited amounts of analytics available to users and paying businesses - Snaps sent and received, the number of times your sponsored filter was viewed, and completed story views. Marketers must work even harder to scrape all the data they can from these. Sharing is all but removed as a feature on both platforms, meaning users post only user generated content. They are improving, though. With the power of Facebook behind it, Instagram launched its Insights in 2016, a powerful analytics tool that details metrics like impressions, reach, and engagement, all of which is presented accessibly within the app. Many companies are also creating analytics solutions for Snapchat, such as Snaplytics, although it is likely Snapchat will almost start producing their own official analytics product soon enough. For the time being, however, these tools do go some way to providing you with an understanding of how successful your Snapchat campaigns are.
Build A Data-Driven Culture
There are a number of challenges moving forward for marketers looking to analyze their social data. Taresh Mullick, Senior Audience Analyst at TEGNA Media, believes that the, ’The barriers to access and usage of web and social data are lower than ever before meaning various departments have access to data without the knowledge to effectively understand and act upon the data. These low barriers also create pressure on limited existing resources within organizations. As the world of content distribution becomes more intertwined, off-platform distribution channels such as Facebook become a vital part of our ecosystem and impact our performance. The industry needs clarity and independent confirmation on how off-platform data is collected and disseminated.’
This requires training and instilling a data culture. A recent study by MIT Sloan Management Review and SAS ‘The Analytics Mandate’ concluded that an ‘analytics culture’ is the driving factor in achieving competitive advantage from data. David Kiron, executive editor for MIT Sloan Management Review, noted: ’We found that in companies with a strong analytics culture, decision-making norms include the use of analytics, even if the results challenge views held by senior management. This differentiates those companies from others, where often management experience overrides insights from data.’ You need to make sure that everything everyone throughout the organization does on social media is done with data in mind. This needs to start at the highest levels, where employees see that executives won’t commit to a position without the analysis of alternatives and the data that drives it. Garry Ma, Technical Product Analyst at Facebook, recommends that companies create a culture of accountability. ‘When metrics drop or increase, call people out,’ he says. ‘Give them props for increasing a metric and, without scolding them, make people realise when they make a mistake and the metric drops. A key component here is reviewing your metrics in a weekly meeting. Sit down with the entire team and review the goals to review the changes.’
All of the effort invested in measuring data and discovering insights is for nothing, however, if action is not taken. The primary advantage of data is that companies can get insights in real time, so they are wasting its potential if they are not then reacting to events at the same speed.
Wes Finley recounts a story from his time at Coca-Cola. ’Flexibility is extremely important. I often work with great campaigns that fail to achieve their potential because of unforeseen difficulties with execution. Large campaigns require many integrated internal and external resources to be successful. And brands don't always have full control over all these touch points. Being flexible though execution allows campaign managers to pivot and adjust to challenges. Flexibility also means that adjustments can be made based on real-time data and results. Several years ago Coca-Cola US changed its cans from the iconic red to a silver-white design to highlight its work with Arctic Home, a charity foundation aimed at protecting polar bears. Social listening quickly identified issues with the new design, including some confusion with the Diet Coke cans. The campaign was adjusted in real-time, can designs were changed to incorporate more red, and a challenge was quickly overcome.’
Much of the analytics work in future will be automated, as machine learning tools improve rapidly. There are now thousands of channels across multiple devices, making it practically impossible to measure user engagement manually. Every minute, social media users submit over 347,000 updates to Twitter and ‘like’ more than 4 million things on Facebook. No one person could visit every single social media site and manually count likes, retweets, and shares, monitor comments for sentiment, and record various data from hundreds of other variables - and nor would they want to. That’s to say nothing of influencer analysis, trend monitoring, deep filtering, and data segmentation. However, it is currently not ready to take control completely yet. Marketers are still needed to audit sentiment for accuracy and to feed in data to help machines get better at building a comprehensive view of language so that it can be analyzed. They need to know the basics, and stay on top of developments in the field in order sustain an edge over their rivals.