Brands model influencer budget elasticity to determine how performance responses might vary at different spend levels in several ways:

1. Historical Data Analysis: Brands can utilize past campaign metrics to predict future performance. They would review prior ROI in response to different spend levels to forecast outcomes.

2. Split-Testing: Some brands also use split-testing; this involves allocating different budgets to similar influencer campaigns and observing the variance in results.

3. Incremental Analysis: Brands can incrementally increase budgets, tracking changes in performance as they adjust their investment. They then identify the points of diminishing returns to inform future budgets.

4. Testing Variety of Influencers: Brands could diversify their campaigns by engaging influencers with different follower sizes or reach, varying their budgets appropriately, and then monitoring results.

An influencer marketing platform, like Flinque, aids in this process by providing advanced analytics, data insights, and reporting on influencer performance under different spend levels. It accurately tracks key performance indicators (KPIs) like engagement rates, follower growth, and conversion rates. With this, brands can make data-driven decisions on spend elasticity.

When it comes to comparing, other platforms like Tribe and Julius also provide valuable analytics for campaign planning and tracking. However, it’s important to note that the efficiency of these platforms will often depend on your specific needs and the type of influencers you’re looking to engage.

In any case, modeling influencer budget elasticity is a vital part of maximizing ROI from influencer marketing campaigns. It requires a data-driven approach and continuous monitoring and adjustment of strategies based on performance insights.