The rise of Instagram, Snapchat, and TikTok influencers has changed the game, especially for campaign activation. However, don’t get blinded by the glitz and glamour of a billion TikTok views.
Why this matters:
Influencer data and analytics still play a critical role in your programs, specifically when identifying influencers and measuring the success of your influencer marketing programs.
Let’s start with influencer measurement first.
How to measure the success of influencer marketing
To measure the success of your programs, you’ll need to start by setting up campaign-specific objectives and key performance indicators (KPIs). The objectives must be aligned with your marketing goals.
For example, you may have a new product launch, and your goals are to increase sales by 15% QoQ (quarter over quarter) and brand awareness by 5%.
In this case, your objectives could be generating X impressions, website traffic, or video views. Your KPIs would be focused on influencer engagement rate (likes, comments, shares) from the content posted on social, traffic to the website (time spent on site, product page views), and conversion rate (leads, sales).
Your KPIs will be different for each program but should always be measurable and actionable. You can use a few different platforms to measure your influencer programs. Here’s a quick write-up of varying influencer marketing software on the market today.
To make this happen, you’ll have to ensure that all the tracking is set up correctly. This includes using campaign-specific landing pages and UTM codes so you can track the user journey of influencer-generated web traffic. When negotiating influencer contracts, you may add incentives for hitting specific sales targets. This is known as performance-based influencer marketing, which consumer brands use more.
Tracking the basics, like influencer engagement rate, is a given when identifying influencers and monitoring the performance of influencers you are already working with.
How to calculate influencer engagement rate
Influencer engagement rate indicates how an influencer’s content is performing and whether or not their audience is interested in the products or services you offer.
There are a few different ways to calculate influencer engagement rate:
- Total number of likes + comments + shares divided by the total number of their followers, multiplied by 100 (to get a percentage).
- The Number of likes on each post is divided by the influencer’s average number of followers.
Below are examples of influencer engagement rate benchmarks provided by the 2021 State of Influencer Marketing Benchmark report specific to Instagram, YouTube, and TikTok. The influencer data shows that TikTok delivers the most robust engagement rate.
For consumer e-commerce brands, you must apply influencer analytics when tracking performance and identifying influencers.
However, when it comes to analyzing influencer data, there are a few things you should keep in mind:
- Volume vs. quality: It’s not all about the numbers. A micro-influencer with an engaged following can generate just as much (if not more) value as an influencer with millions of followers.
- Frequency: How often are they posting? You want to work with influencers who are active and posting content regularly.
- Local influencers: If you’re a retailer, you’ll want to work with local influencers and drive in-store traffic and sales.
- Relevancy: Are they a good fit for your brand? Make sure their content is relevant to your industry and target audience.
- Type of content: What type of content are they posting? If you’re looking to drive website traffic and increase your rankings in Google, you’ll want to work with influencers who have authority in writing long-form content like blogs and articles.
- Channel influencers: Many marketers like to focus on channel-based influencers. However, it’s best to partner with influencers with authority across more than one social media channel.
- Brand safety: Are they posting offensive or inappropriate content? You’ll want to avoid influencers with a history of posting offensive or insensitive content.
Now that you know the basics of influencer analytics, let’s look at a different way to analyze influencers, specifically for B2B influencer marketing.
Influencer marketing data that uncovers topical authority & relevance
I’ve been using influencer marketing analytics since 2010. When clients ask, “Can you help me identify the top 30 influencers in cloud computing?” It wasn’t always “cloud” specifically; it was any relevant topic.
My team would spend three to four weeks scraping influencer data, applying algorithms, validating data, and writing influencer insights. The outcome was a 30-40 page slide deck with a list of stack-ranked influencers according to pre-defined criteria. We analyzed the conversations of the entire influencer community and the topical relevance of each.
If the client’s request were to identify the top 30 most relevant cloud influencers, we would analyze the aggregate conversation of the 30 influencers and then contextualize the results using cluster analysis. This would help us and the client understand the “what” about the cloud. At the time, and still today, everyone in the technology community talked about the cloud. Still, influencers could be talking specifically about cloud migration, hybrid or public, security, data, scale, or innovation.
We also looked at each specific influencer and applied the same analysis. We analyzed their published content on social media, blogs, and contributed articles. We call this topical relevance. It helped us dive deep into what was relevant for each specific influencer.
The difference between an influencer marketing analysis and what was already described is that this approach is strictly for research purposes.
What is an influencer analysis?
Influencer analysis measures the reach, engagement, performance, conversation, and business impact of one influencer or influencer group. It includes influencer data analytics in search, traditional media, and a deep dive into their social media channels, not just one.
It involves more than just tracking engagement rate, frequency, volume, quality, etc.
When analyzing influencers, B2B and technology companies approach this differently than consumer brands. For consumer brands and retailers like Adidas, Sephora, Airbnb, Spotify, and Home Depot, the primary marketing goals focus more on sales, subscribers, or foot traffic in local stores. It’s all transactional. There will also be secondary and adjacent goals, but consumer brands hire influencers to drive sales.
B2B and technology companies are looking at influencer analytics a little differently. While they are paying and collaborating with influencers to activate programs and campaigns, the smart brands invest time and budgets looking into influencer marketing analytics and research before a program ever starts. Influencer research is usually a deep dive into influencer affinities, conversational trends and patterns, and how their content influences various audiences.
How to use influencer analytics to understand their audiences
Rather than explaining an approach for analyzing influencer audience data, I thought it would be better to walk through a real example.
But first, let’s level set on a few things.
There are several ways to do an influencer analysis. The first way is to start the research by identifying the influencers with authority about a specific topic. The second way is to do an industry analysis, blend multiple data sources and find influencers within that specific industry. The last way is to do an audience analysis and then identify who influences that particular audience. This is easier with B2B and technology audiences because they use descriptive terms and language in their social media bios.
The below influencer analysis is a subset of digital transformation topics and research studies we did a couple of years ago. We wanted to uncover which media outlets were driving impactful coverage of digital transformation, how different audiences were talking about it contextually, and which influencers had topical authority on this topic.
If you have worked in B2B or the technology sector, you will be familiar with this term. It’s been around for a long time, and every SaaS company wants to be associated with it. Some tech brands align their entire messaging and brand storytelling framework to digital transformation.
Below is an influencer profile for Kirk Borne. He’s one of the most influential data scientists globally and a nice guy too. His profile was one of the many that came up when researching the digital transformation industry.
Let’s explore Kirk’s influencer analytics in detail, from the top down and right to the left.
At the very top is his name and audience size. His audience size is the aggregate sum of his followers across all his social media channels.
Under his name and bio, you’ll notice “total mentions” and “share of mentions.” These influencer data points refer to what I call “reference.” When measuring influence, reference quantifies whether other influencers are mentioning the influencer. In this specific influencer analysis, “other influencers” are an audience of ITDMs (IT decision-makers). Over this period, Kirk was mentioned 176K thousand times by the IT audience, either by a mention, reply, or sharing an article where he is mentioned.
The 15% share of audience mentions is a data point that compares mentions of Kirk against the mentions of all technical topics like AI, data science, and machine learning.
Here’s what was done in more detail.
We built an audience panel of about 8K self-identified ITDMs and included job titles like Head of IT, VP of IT, VO of Infrastructure, etc. Then, we ran a search filter in that audience panel to see if Kirk was mentioned.
The “media mentions” are manual searches of Kirk’s full name to quantify if he is being mentioned in traditional media outlets. While this is a manual process, verifying if the influencers are being mentioned in the media, either by name or by research they commissioned, is good. This is an excellent indicator when mentioned in top-tier media publications like ZDNet, Fortune, and Forbes.
You can see who influences Kirk in the “Influenced By” section. Everyone who uses the internet leaves digital breadcrumbs by following others, liking, sharing content, or linking from one blog to another. These breadcrumbs are documented forever and are usually found through an influencer mapping exercise.
This section of Kirk’s influencer profile is vital because if Kirk isn’t calling you back or not engaging with you on social media, you can engage with other influencers like Mike Quindazzi, Dion Hinchcliffe, or Mark Lynd to reach Kirk.
The last section is what I call topical relevance. It’s a cluster analysis of all the keywords and phrases that Kirk has used and published on social media. Again, it’s clustered based on the volume of mentions. Over this period, Kirk’s conversations can be categorized into four sections–Data Science, Artificial Intelligence, 5G, and Quantum Computing.
Naturally, data science is the most crucial topic of conversation, given that he is a data scientist. The data also shows Kirk’s subtopics related to data science, like data mining, analytics, Python, machine learning, regression, etc. These subtopics are also sized based on volume, which is essential because it shows topical relevance to data science.
Sometimes, you may want to add influencer audience data to these influencer profile pages. For example, it can show the influencer’s audience’s basic demographics, interest areas, job titles, and industries.
Quantifying aggregate influencer analytics
While looking at influencers individually is essential, you should also analyze them as a group. Given the data size and sample, this will give you more trending analysis.
So, by analyzing the ten influencers in the digital transformation space, including Kirk, you can uncover the important topics to them as a group. You’ll notice that these topics are a little different than the topics when looking at just Kirk’s topical relevance.
You’ll see some overlaps and consistencies. Still, topics like digital transformation and enterprise security are more applicable to the more extensive set of influencers than Kirk.
How to make influencer analytics actionable
Some marketers think the job is over once they’ve identified a set of influencers. They have yet to realize the power of social influencer analytics and, more importantly, how actionable the data can be to all marketing and communications programs.
This influencer data tells us what’s essential to these influencers. These are the issues that keep them up at night. These are the conversations they have with other influencers, executives, the media, and B2B buyers.
They are developing thought leadership, discussing technical challenges, and engaging with these buyers publicly about these exact issues. They create a sense of urgency in their communities where buyers are researching information, using Google to find white papers, industry analyst reports, blog posts, and forum conversations. Finally, they ask peers, colleagues, and other industry experts for their points of view.
The influencer group’s language, vernacular, and topical relevance can inform several different marketing tactics, but I’ll talk about one for now–content.
Content is the lifeline to the internet. B2B buyers, reporters, and other influencers use it to learn and get more thoughtful about specific topics. Smart and innovative brands are getting ahead by researching influencers and using influencer marketing analytics to inform everything they publish online. This includes blogs, headlines, press releases, white papers, eBooks, sponsored content, etc. When creating paid ads, they also use the same language in social media content, including hashtags and keyword targeting.
The result is that their content becomes more visible in the search engines, driving more relevance and authority within online communities for business.
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