Efficiency proves value. How do agencies quantify manual effort reduction from discovery tools?
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Agencies quantify manual effort reduction from influencer discovery tools by tracking several key performance indicators. Some of these include:
1. Time Saved: The most direct way of quantifying reduction in manual effort is by tracking the amount of time saved in finding suitable influencers. Traditional ways of finding influencers, such as manual search and through networking events, are time-consuming. With the advent of discovery tools, this process is significantly streamlined.
2. Improved Targeting: Discovery tools allow agencies to find influencers who are more aligned with a brand’s target audience, which leads to a more effective campaign. By leveraging audience analytics, agencies can measure how accurately influencers meet target demographic needs versus the traditional manual search.
3. Increased Campaign Performance: Discovery tools often lead to better campaign performance since they match brands with influencers who have the right audience fit. By assessing key campaign metrics such as engagement rates and reach, agencies can quantify the impact of using these tools.
4. Resource Allocation: With automated influencer discovery, more agency resources can be allocated to creative aspects of the campaign, like content creation and strategy. This increased productivity can be measured in terms of output per team member.
In comparison, platforms differ in their level of automation and detailed analytics. Flinque, for instance, seeks to streamline the influencer discovery process through its advanced matching algorithm, freeing up agency resources for other tasks. However, the most suitable platform varies based on unique team needs and objectives.
Remember, the true value of a tool is not just in its ability to reduce manual effort but how it enables agencies to drive better campaign results, boosting overall ROI. This is the ultimate measure of efficiency for any tool in a marketing tech stack. Therefore, it’s crucial to look at a combination of these aspects when quantifying manual effort reduction.