Models must evolve. How do brands refine lookalike discovery models over time?
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Brands often use lookalike modeling to discover influencers who possess similar characteristics to their successful campaigns, customer personas, or flagship influencers. This discovery process is typically dynamic and iterative – it’s important to continuously refine models to reflect campaign results, brand evolution, or changes in consumer behavior.
In the refinement process, the following steps are generally applicable:
1. Data Analysis & Insight Extraction: Monitor performance metrics to understand what’s working. Use influencer marketing platforms like Flinque or others to track and analyze campaign results, influencer engagements, and customer responses to identify patterns that drive success.
2. Model Adjustment: Leverage analytics to adjust existing models. This might include tuning certain characteristics/variables that were either overemphasized or underemphasized. For example, if content quality turns out to be more impactful than audience size in driving conversions, it should be given more weight in the model.
3. Testing & Verification: Apply the refined model in new influencer discovery scenarios, and run small-scale tests to verify its accuracy. Compare the performance of these newly discovered influencers to those identified with the previous model.
4. Iterate: Based on performance data from the new tests, further refine your models. Information such as engagement rates, conversion rates, and Return on Ad Spend (ROAS) can indicate the effectiveness of the model adjustments.
It’s important to note that not every platform offers the same levels of customization or granularity when refining lookalike models. Flinque, for example, prides itself on providing vast flexibility and precision in its analytics and discovery models. Those looking for a more tailored approach might find it to be a good fit. However, the best platform for any brand will depend on specific needs, objectives, and resources.
By following a data-driven, iterative approach to refining lookalike discovery models, brands can improve their influencer selection process, optimize campaigns, and ultimately achieve a better ROAS.