The Ultimate Guide to Social Media Data Collection for Influencer Marketing

Influencer marketing is a powerful strategy for brands to tap into the engaged audiences of trusted creators. But with so many influencers to choose from, how can you find the right partners to drive your campaign goals? The answer lies in data.

By leveraging social media data, brands and influencer platforms can identify the ideal influencers based on metrics like audience demographics, brand affinity, engagement rate, and more. Rather than making decisions based on gut feel alone, data enables a targeted, scalable approach to influencer discovery and management.

In this ultimate guide, I‘ll share my expertise on how to effectively collect and operationalize social media data to power your influencer marketing efforts. We‘ll cover:

• Why social data is critical for influencer marketing success
• Key influencer data points and metrics to focus on
• How to collect influencer data efficiently and at scale
• Analyzing influencer data for actionable insights
• Operationalizing data-driven influencer marketing
• The future of AI-powered influencer data

Whether you‘re a brand marketer or work for an influencer platform, this guide will equip you with the strategies and tools you need to thrive in the data-driven future of influencer marketing. Let‘s dive in.

The Power of Data for Influencer Marketing

Influencer marketing is rapidly becoming one of the most effective and popular ways for brands to reach new audiences. According to Influencer Marketing Hub, the influencer marketing industry is set to grow to $16.4 billion in 2022.

But to succeed in this increasingly competitive space, brands need to be strategic about the influencers they partner with. Partnering with an influencer who isn‘t relevant to your brand or audience is at best a waste of budget, and at worst could harm your reputation.

That‘s where data comes in. By collecting and analyzing social media data, brands can take the guesswork out of influencer identification and ensure they partner with influencers who are the ideal fit.

Think of influencer data as the key that unlocks effective influencer discovery at scale. Some of the benefits of data-driven influencer marketing include:

• Audience alignment: Analyze an influencer‘s audience demographics to ensure they match your target persona
• Brand fit: Assess an influencer‘s past content, brand mentions and aesthetic to see if they‘re a strong fit
• Engagement: Evaluate an influencer‘s engagement rate and benchmark it against their peers to select partners with an active, loyal community
• Cost efficiency: Model influencer reach vs. cost-per-engagement to build cost-effective campaigns
• Optimization: A/B test influencer content and adjust your roster based on performance data

The proof is in the results. 89% of marketers say influencer marketing ROI is as good or better than other channels. By using data as the foundation of your strategy, you can be among the brands driving strong returns from influencer partnerships.

Key Influencer Data Points & Metrics

So what influencer data should you focus on collecting and analyzing? While the specific metrics may vary depending on your goals, here are some of the most important data points to look at:

Audience Demographics

Understanding the makeup of an influencer‘s audience is critical for determining brand alignment. Key demographic data to collect includes:

• Age
• Gender
• Location
• Language
• Interests/affinities
• Income level

By analyzing this data, you can get a clear picture of who an influencer reaches and see how well it matches your target customer profile. Some tools will even allow you to input your ideal customer persona and surface influencers with matching audience demographics.

Engagement Metrics

Engagement is a key measure of how actively an influencer‘s community interacts with and consumes their content. Likes, comments, and shares demonstrate that an influencer‘s audience is tuned in and interested.

Core engagement metrics to track include:

• Engagement rate: Average engagements per post / follower count
• Engagement rate by reach (ERR): Average engagements per post / reach per post
• Cost-per-engagement (CPE): Total fees paid to the influencer / engagements on sponsored posts

Here‘s an example of how you might compare influencers‘ engagement rates:

In this scenario, Influencer B has the highest engagement rate at 4.4%, so may be a more effective partner even though they have a smaller following than Influencer C.

Content Performance

In addition to understanding who an influencer reaches, it‘s also important to assess how their content performs. This will give you an idea of the type and quality of content they produce and how well it resonates with their audience.

Some content metrics to analyze include:

• Reach per post
• Impressions per post
• Video views & completion rate
• Content themes and pillars
• Posting frequency and consistency

Look for influencers whose content style and cadence aligns with your brand‘s, and who receive strong reach and completion rates on their posts. This signals their content is hitting the mark with their audience.

Brand Affinity

Brand affinity refers to how well an influencer‘s content and values align with your brand. Partnering with influencers who are an authentic fit leads to better results, as their endorsements come across as truly genuine.

To assess brand affinity, analyze data points like:

• Brand mentions: Has the influencer organically mentioned your brand or competitors in the past?
• Past sponsorships: What other brands has the influencer worked with and in what capacity?
• Audience insights: What other brands and interests do the influencer‘s audience engage with?
• Sentiment: When the influencer mentions relevant topics or products, what is the sentiment of their messages and the audience‘s response?

You may also consider having your team manually review the influencer‘s feed to get a qualitative sense of their persona and style. Tools can surface promising influencers based on data, but it still pays to have a human eye confirm brand alignment.

How to Collect Influencer Data at Scale

Now that you know what data to look for, let‘s discuss how to actually collect it efficiently and at scale. With millions of social media accounts out there, manually gathering this data isn‘t feasible. You need tools to automate the process.

Here are some of the key ways to collect influencer data:

APIs

Many social platforms offer APIs that allow developers to access and pull public data from their site. This can include follower counts, engagement metrics, audience demographics, and more.

However, APIs often have limitations on the amount and type of data you can pull. You may need to combine multiple APIs (e.g. Instagram Graph API + Facebook Marketing API) to get a full picture of an influencer‘s reach and performance.

Web Scraping

Web scraping involves using bots to automatically scan websites and social networks and extract publicly available data. This is helpful for gathering information not available through APIs, such as an influencer‘s past posts, sponsorships, contact info, and more.

To scrape data effectively, you‘ll need reliable proxy servers which allow the scraper to send requests from different IP addresses. This avoids getting blocked by the social sites‘ anti-bot measures. Residential proxy networks like Bright Data offer ethically-sourced, rotating IPs that enable compliant large-scale data collection.

Influencer Marketing Platforms

Many brands and agencies choose to partner with an influencer marketing platform to access their database of vetted influencers and audience insights. These platforms have done the hard work of aggregating influencer data which you can then filter and analyze based on your needs.

Some leading influencer discovery platforms include:

• Upfluence
• AspireIQ
• Traackr
• Klear
• Heepsy

The right mix of data collection strategies for you will depend on your resources, budget and goals. Whichever route you choose, having a solid data foundation is essential for effective influencer discovery.

Analyzing Influencer Data for Actionable Insights

Collecting the data is just the first step – next you need to analyze it to surface actionable insights for your campaigns. Here are some key strategies for influencer data analysis:

Define Your Ideal Partner Profile

Before diving into the data, clearly define the attributes of your ideal influencer partner. What audience demographics are you looking for? What content style and aesthetic? What values and interest areas should they embody?

Documenting this ideal partner profile upfront will help you focus your analysis and avoid getting overwhelmed by the data. You can score influencers based on how well they match your target criteria.

Compare Relative Performance

To identify top performers, analyze influencers‘ metrics relative to each other, not just in isolation. For example, an influencer who averages a 4% engagement rate may seem strong, but if most of their peers are achieving 6-8%, they‘re actually lagging.

Benchmark influencers‘ performance against others in their vertical and follower range. This will highlight the standouts who are driving results.

Visualize the Data

Large datasets can be difficult to parse, especially if you‘re dealing with spreadsheets full of numbers. Visualizing the data through charts, graphs and dashboards makes it easier to spot insights and trends at a glance.

For example, you could create a scatter plot comparing influencers‘ engagement rates vs. follower counts to identify those with an outsized impact. Or show a breakdown of each influencer‘s audience by age and gender using a stacked bar chart.

Many influencer platforms offer built-in data visualization and reporting tools. If you‘re collecting data in-house, you can connect your data sources to a BI tool like Tableau or Google Data Studio to bring it to life.

Segment & Model Scenarios

Don‘t just analyze your influencer data as one big lump. Segment the data based on different attributes to uncover deeper insights. For instance, you could:

• Compare performance of micro vs. macro influencers
• Analyze which content pillars drive the highest engagement rate
• Model cost vs. reach for different tiers of influencers

Play around with building different segments and models. The more ways you slice the data, the more insights you‘ll find to inform your influencer strategy.

Operationalizing Data-Driven Influencer Marketing

Moving to a data-driven influencer approach isn‘t just a one-time project. To drive continued results, you need to operationalize the strategy and infuse data into your ongoing influencer workflow. Some tips:

• Set data-driven goals & KPIs: Use your past campaign data to inform your partnership goals and success metrics. With clear KPIs, you can objectively assess performance.

• Build data into your vetting process: Incorporate key data points like audience demographics and engagement rate into your influencer vetting scorecard. Every potential partner should be assessed using the same data-driven criteria.

• Test & learn: Lean on data to guide your influencer creative strategy. A/B test different content pillars, formats and partnerships to see what moves the needle. Let the results shape your ongoing gameplan.

• Monitor real-time insights: Don‘t wait until the campaign wrap-up to check the data. Implement real-time social listening and reporting dashboards to stay on top of results and optimize quickly. The more responsive you can be, the better.

• Showcase results: Regularly report on data-driven results to demonstrate the impact of your influencer strategy. Share learnings broadly so your full marketing team is aligned on the data-driven approach.

The key is weaving data into the entire lifecycle of your influencer program. From discovery through to measurement and optimization, data should guide your day-to-day decision making.

The AI-Powered Future of Influencer Marketing

Looking ahead, AI and machine learning will unlock even more powerful applications of influencer data. We‘re not far off from a future where AI can predict an influencer‘s content performance before they‘ve even made a post.

Some exciting AI use cases on the horizon:

• Predictive analytics: Analyzing an influencer‘s past performance to forecast their potential results for future campaigns. Brands could model out different influencer rosters to predict the best mix.

• Computer vision: Using image recognition to detect all the brands, products and themes an influencer has posted about. This technology could instantly surface relevant brand partnerships based on visual data.

• Lookalike modeling: Taking a brand‘s top-performing influencer partners and finding other creators who match their audience and content profiles. This allows you to easily scale your program with vetted, hand-picked partners.

• Sentiment analysis: Parsing the language of an influencer‘s captions and comments to assess audience sentiment and brand safety. AI could flag potentially controversial statements before they turn into a PR crisis.

While some of these applications may seem far off, leading influencer platforms are already experimenting with AI behind the scenes. Those who can harness the power of machine learning to make smarter decisions from influencer data will have a huge competitive advantage.

Putting Your Influencer Data to Work

Whatever your influencer marketing goals, data is your key to achieving them effectively and efficiently. With the wealth of social media data out there, brands can take a scientific approach to identifying high-impact creators who authentically resonate with their target audience.

Start by assessing your current influencer data capabilities. Do you have a strong system in place for collecting and analyzing the metrics that matter? If not, look for tools and partners to help fill in the gaps.

The influencer landscape is becoming more data-driven by the day. Brands who embrace this shift and build data into their core strategy will be well-equipped to drive maximum ROI from their creator partnerships now and into the future.

To learn more about how leading brands are leveraging influencer data to power their programs, check out these additional resources:

Case Study: How Bonafide Leveraged Data-Driven Influencer Marketing to Drive Conversions

The State of Influencer Marketing 2021: Benchmark Report

The Data-Driven Future of Influencer Marketing (blog post)

Happy data collecting!