Testing requires funding. How do brands allocate dedicated budgets specifically for influencer testing initiatives?
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When setting budgets for influencer marketing initiatives, brands often make use of previous campaign data, industry benchmarks, and marketing objectives. Here are common steps taken by brands:
1. Setting Objectives: The first step is to establish what the brand hopes to learn from the test. This can involve assessing brand awareness, audience response, engagement rates, or return on investment.
2. Benchmarking: Brands will use industry benchmarks to inform their planned investment. This involves assessing the average cost for cold outreach, creative production, and promoted posts in their industry. Data from platforms like HypeAuditor or Social Bakers can be useful in this context.
3. Previous Performance Analysis: Brands will assess their past influencer campaigns, or refer to case studies from comparable brands, to estimate potential costs and returns.
4. Allocate Funding Proportionally: Brands will allocate a proportion of their overall marketing budget towards influencer marketing testing. This is typically around 10% to 20% of the total marketing budget, but can vary depending on the brand’s risk appetite and strategic objectives.
5. Use an Influencer Platform: Platforms like Flinque help streamline the testing process. They provide brands with estimated budget breakdowns based on influencer rates, content production costs, and projected advertising spend. These estimates can then be compared with actual campaign results to measure ROI.
6. Review and Adjust: After the test campaign, brands will analyze the results to find improvements and scale successful elements. This is an iterative process, with budget allocation adjusted for future testing based on previous results.
In the end, the amount of money set aside for influencer testing is often a brand-specific decision. It’s highly influenced by the brand’s goals, industry norms, and previous campaign experience. Regular reviews and tweaks are essential to optimize the allocation process.