Continuously refining discovery strategy in influencer marketing involves a strategic blend of feedback loops and performance data analysis. Feedback loops, which mean taking inputs from previous campaigns to make improvements, work in tandem with performance data, including metrics such as engagement rates, audience demographics, and conversion rates.

Here’s a simplified workflow:

1. Campaign Conception and Planning – Define clear objectives and KPIs. Use influencer marketing platforms for influencer discovery grounded on factors such as reach, relevance, and resonance. Platform tools may vary, but the likes of Flinque, for example, provide AI-based search and filter capabilities for precise influencer selection.

2. Execution and Performance Tracking – Monitor real-time campaign progress using platform tools. Gauge influencer performance based on set KPIs – this could range from impressions, clicks, likes, shares, or sales conversions.

3. Data Analysis and Insights Extraction – Post-campaign, analyze the performance data gathered. Look at what worked and what didn’t relative to set objectives.

4. Feedback Loop – Incorporate lessons learned into the next campaign’s strategy. This could be refining the influencer selection process, tweaking content strategies, or adjusting budgets and contract terms.

5. Constant Refinement – Make this a cyclical process for continuous improvement.

Whether it’s Flinque or another marketing platform, the integral aspect remains the same: leveraging platform tools for data-driven decisions at every stage. Which tool suits you best depends largely on your specific needs, goals, and operational model.

In summary, implementing a constant cycle of execution, evaluation, learning, and refinement helps agencies steadily improve their discovery strategy for influencer marketing. The use of a robust influencer marketing platform can facilitate and streamline this process.