A benchmark is defined as a way to “Evaluate or check (something) by comparison with a standard.” Ah, but what is the standard? In the influencer marketing industry, one that changes and evolves so rapidly, a standard is more difficult to pinpoint. “Standard” does not and should not simply refer to industry medians.
At Fohr, we believe there are four ways to determine a standard when measuring and evaluating your campaign performance. The standards you go by should be customized for your goals and needs.
How fast can you run a mile?
In our latest webinar, ‘How to Measure Influencer Performance,’ we used an analogy of running a mile to help break down the different ways to benchmark campaign performance. Now let's break down the four ways to benchmark campaign performance.
CATEGORY: Benchmarking against industry or category averages or medians.
Good for: Gathering tactics and learnings from your competition or other players within your vertical. In some cases, this strategy is best if you’re starting out in the influencer space or launching a new product that you don’t have historical data for.
Not good for: Comparing against competitive brands that are technically within the same category but have a vastly different brand identity or influencer archetype (e.g., MAC vs. Bobbi Brown).
Example: A luxury DTC haircare brand is emerging into the space and has predominantly focused on influencer activations and word of mouth for brand awareness. They are adding a new dry shampoo to their suite of products and don’t have any prior data to compare the campaign's performance. Three of their competitors launched dry shampoos in the last two years, so they pulled the results from those programs to measure their success in the haircare and, more specifically, the dry shampoo category.
- The constant: The hair care category + dry shampoo comparison
- The variable: Their product vs. competitor products
Analogy framing: How fast are my competitors running?
COMPANY: Benchmarking against prior campaign performance for your brand.
Good for: Focusing on ongoing improvement, sustainable evolution, and consistent, compounding learnings. You should always keep your brand’s past performance in mind when setting any benchmarks. A great use case would be to run a campaign with the same goal as the control and use creative direction, influencer type, timing, etc., as the variables to optimize program performance.
Not good for: Becoming too insular. Start here, but don’t assume there’s nothing to learn from elsewhere. You should not use the ‘company’ benchmarking if you are launching a campaign with different goals from the previous one or a campaign with multiple goals.
Analogy framing: How fast have you run a mile in the past?
CREATOR: Benchmarking against the average performance of your selected influencer partners.
Good for: Comparing an influencer’s performance on a campaign against their own personal performance to understand whether or not the message is resonating with their audience. This works well for long-term ambassadorships. If you are expecting someone who has a personal average of 1% to perform at 2% engagement, you'll likely be disappointed. An influencer who has 1% engagement can still be the right person for your brand because remember: one like for the right reason is better than 5 likes for the wrong one.
Not good for: Comparing the performance of influencers on the same campaign against one another who have different aesthetics within their feed and content. This benchmarking type is not ideal if you aren’t sure whether your selected brand partners are the right fit for meeting your goals.
Analogy framing: How fast can I expect this partner to run?
CHAPTER: Benchmarking performance by each phase of a long-term campaign.
Good for: This is for our advanced class. If you are running long-term programming, it's beneficial to investigate performance trends from phase to phase. This will allow you to start to understand patterns and optimize future campaign strategies and messaging. For example, a call to action that provides an offer or audience incentive typically drives more traffic than generic ‘learn more’ messaging. The best use cases for this benchmark type are mid-program optimization and A/B testing.
Not good for: Programs where the goals shift from phase to phase, or quick turnaround campaigns within limited windows to analyze performance.
Example: We partner with 10 creators to talk about a tech rewards program that highlights the benefits of being a member of the program. In the first phase, influencers were asked to create talking head style content or vlog day in the lifestyle content for their TikTok videos. After reviewing the results of Phase 1, it was apparent that the talking head style content was performing better. So, for Phase 2, all creators were asked to speak to the camera and highlight product benefits in their TikTok videos. The results of the second Phase outperformed the first phase due to the content type resonating more with the target audience for that program.
- The constant: Influencer group
- The variable: Creative direction and messaging
Analogy framing: Which mile did I run the fastest, and why?