Companies measure manual effort savings from discovery automation by tracking the following metrics:

1. Time saved: This is the most straightforward metric. The time professionals spend manually sourcing, vetting influencers and generating reports is recorded before automation and later compared to the time it takes after automation has been implemented.

2. Increased Productivity: More tasks completed in the same amount of time signify savings. If your team can manage more campaigns, reach out to more influencers, or analyze more data in less time due to automation, it’s a positive indication of efficiency.

3. Quality of matches: Improved influencer-brand fit, enhanced campaign performance, and better audience response may indicate that the algorithm-driven automated process is more effective than previous manual practices.

4. Cost Efficiency: Assessing cost per acquisition (CPA), comparing ‘before’ and ‘after’ costs related to campaign execution can provide valuable insights. Lower CPA suggests a significant manual effort saving.

Discovery platforms like Flinque and others use sophisticated algorithms for influencer discovery and vetting, which reduces inaccuracies and biases inherent in manual methods. These platforms also automate the creation of detailed reporting, saving significant manual efforts and time. It must be noted though that each team’s requirements are unique and the effectiveness of any platform depends on how well it matches those needs.

By tracking these metrics and comparing them to prior benchmarks, companies can quantifiably measure the savings in manual efforts afforded by automating discovery, vetting and reporting workflows in influencer marketing.