Influencer marketing spend was expected to reach as much as $15 billion by 2022. But, as budgets are drastically reduced due to the global pandemic, marketers are under intense pressure to accurately optimize spend, and properly measure return on their investments.
Even prior to COVID-19, many organizations suffered from inefficient influencer marketing resource allocation due to a lack of systematic standardization, data management, and access to insights.
At the same time, the crisis has heightened the importance of digital marketing channels and; specifically, influencer marketing is proving to be an effective strategy for reaching consumers and a cost-effective means for branded content production.
This paper articulates how data-driven influencer marketing software enables cost savings, performance, and budget optimizations at scale by empowering marketers to:
If you would like to download a PDF of this white paper, click here (direct download).
Without the proper technology, your influencer marketing program is operating in the dark. To illustrate the scale of the problem and the risk to your organization, we analyzed sponsored content from lifestyle influencers on Instagram and YouTube.
On Instagram, we looked at the paid content from 1,000 influencers from January 1, 2019 to April 15, 2020. The influencers ranged from nano influencers with 5,000 followers to megastars with 237 million followers. The median audience size was 130,419. Collectively, they posted 85,473 paid mentions.
Based on a traditional CPM model (the amount a brand would pay to reach 1,000 people), the average post cost was $2,158. However, if the brands had been using a CPME model (the amount a brand would pay to generate 1,000 engagements), paying based on content performance, the average cost per post (CPP) should have been $643. On average, brands paying based on CPM versus CPME are overpaying by 262%.
The CPME model is preferable because it rewards results and helps tackle influencer fraud, one of our industry’s most challenging problems, by disincentivizing vanity metrics like reach.
Of the 1,000 Instagram influencers studied, only 122 would have been underpaid based on their content performance and 569 influencers would have been overpaid by at least 100%.
If we look at YouTube influencers, the situation is even more striking. Among the most active sponsored influencers, the average CPM per sponsored video was $9,735, while the CPE model would have resulted in an average cost per video of $4,417. This indicates that on average, brands paying based on CPM versus CPME are overpaying by 572%.
Of the 1,000 YouTube influencers, only 136 would have been underpaid based on their content performance. A staggering 662 influencers were overpaid by at least 100%.
Certainly, influencer compensation and campaign budgeting is complicated. The problem is systemic in the industry as many marketers find it difficult to know which influencers to work with and how much to invest in them. At scale, these issues become overwhelming financial challenges.
This is where the right influencer marketing technology becomes essential for running an optimized program and avoiding the most costly influencer investment mistakes.
While every brand will have different requirements for its influencer selection process, there are three common traps where the lack of data-driven influencer marketing software is costing your brand precious dollars.
The most costly mistake by far is investing in the wrong influencer partnerships. With millions of potential creators available, it’s essential to screen out the individuals who are least likely to create impact for your brand.
Common examples of misalignment include mismatched audience demographics, poor audience quality, and below average engagement—especially when posting content about your brand or category. The right technology platform will enable you to
filter out the wrong people and quickly assess potential impact of influencers.
Here is a nano influencer with 4,750 followers and a 12.6% engagement rate when posting about beauty products. On the surface, a great candidate. However, this individual’s audience quality score indicates that they likely purchased their following or engage in inauthentic practices to generate engagement.
You can also screen out people whose audience doesn’t align with your buyer. Here is an influencer with great engagement on paid sponsorships for makeup and fashion brands, however, 70% of her audience is male and not likely in the market for such products.
A data-driven influencer marketing platform will also ensure your teams screen out influencers whose values do not align with your brand or pose a risk to your reputation. By analyzing influencer content for red flags (i.e. derogatory terms), you can avoid inadvertently partnering with people who could tarnish your brand and create consumer backlash.
It is important to note that at the heart of your data-driven process is your influencer vetting. More information on the critical step of creating your own influencer selection framework can be found here.
Once your team has created the essential foundation of establishing a criteria for choosing the right influencers for your brand to work with, the next step is negotiating a partnership. There are two common mistakes you could make with influencer compensation if you don’t have the right technology to support your program. In one case, you’ll be throwing money out the window; and the other, you’ll be leaving dollars on the table.
You may be working with the right influencer, meaning they reach the right audiences and produce great content, but basing compensation off an inflated rate card.
This can happen when only taking overall audience size into consideration, without looking at the impact the influencer drives for your brand, product, or category.
For example, Vanessa Siriwalothakul is a mid-tier influencer with great audience quality and 154,000 followers. Her collaborations with clothing brand Revolve have generated top engagement (11.2%) while content for ScentBird have generated less impact (1.32%).
A data-driven influencer marketing platform will enable you to identify the actual impact an influencer can generate for you, recommending an appropriate fee based on the projected performance. At scale, over investing in the wrong partnerships can cost brands significantly.
On the flip side, you could be under investing in influencers who generate disproportionate results for your brand. Without clear insights into influencer performance, compared to industry benchmarks, you can easily miss opportunities to expand partnerships that over perform and thereby maximize your cost- per-performance metric.
This can often happen when managing large micro influencers, or ambassador programs where you have collaborators with smaller audiences, driving more impact than your bigger investments in macro partners. In these cases, you could be saving by reducing your spend with the average, or under-peforming partners and increasing your collaborations with the overperformers.
Sometimes, you can actually miss out on effective partnerships by focusing on influencers with a certain persona or content aesthetic. This is why it is important to leverage multiple criteria for influencer selection and measure content performance closely during campaigns and across campaigns so you don’t miss out on top performers.
To make influencer compensation results-oriented and transparent, Traackr developed an influencer budget calculator that provides you with suggested fees for influencers' sponsored posts based on their past performance.
The influencer budget calculator uses a suggested CPME based on industry averages by social platform, but can be customized based on your brand’s experience.
It helps marketers move away from paying influencers purely on audience size, to a model that rewards influencers’ impact for your brand.
The result: you will get more value out of your investment.
The calculator empowers influencer marketing teams with different filters by which you can assess an influencer’s historical content performance: sponsored branded content, organic branded content, sponsored content, and organic content.
The first filter, sponsored branded content, will be the most representative, as this looks at the performance of that influencer’s previous sponsored content for your brand.
If the person has not created sponsored content for you, the calculator defaults to the next filter (organic brand mentions).
If the influencer is completely new to you, the calculator will base suggested fees on their engagement rate across all organic mentions.
Once you have identified the right investments to make and have properly aligned compensation to performance, you can turn to analyzing efficiency across all campaigns, markets, and brands.
Today, even some of the most advanced organizations don’t know how much they’re spending across all influencer campaigns or which strategies are delivering the greatest returns.
Influencer marketing budget optimization tools will enable you to compare the performance of your investments with cost- based efficiency metrics such as Cost per Post (CPP), Cost per Engagement (CPE) and Cost per View (CPV) so you can see how each campaign, strategy, and influencer performs.
One of the key values of a data-driven influencer marketing software is that it provides a single source of truth for influencer marketing spend and ROI. Such platforms go beyond basic campaign management tools, which might help you scale your activities, but not in a cost-effective or performance-driven way.
A data-driven influencer marketing platform will enable you to take control of your influencer marketing spend, choose the right partners, understand the performance of your investments, and optimize your ROI by investing in the right strategies.
To learn more about Traackr’s influencer marketing budget optimization capabilities, visit traackr.com.
If you would like to download this white paper click here.