BlueConic leverages your first-party customer data to deliver personalized recommendations at scale. By tailoring content and product suggestions based on individual behavior and preferences, you can enhance engagement, drive conversions, and build customer loyalty.
Implement personalized recommendations
BlueConic enables personalized content and product recommendations through three key phases:
1. Exploration Phase
To optimize recommendations, begin by testing multiple variants, each employing a distinct algorithm like "Breaking news" or "Viral news." This initial phase identifies effective algorithms and ensures diverse data collection. Subsequently, once sufficient click data is accumulated, BlueConic analyzes it to determine the optimal algorithm combination. Finally, validate the identified optimal combination by testing it against the current best-performing variant to confirm its real-world effectiveness.
2. Exploitation Phase
During the exploitation phase, use what you learned during the exploration phase to get the maximum amount of value out of the BlueConic recommendations engine. This means turning off all variants except the best-performing one.
3. Continuous Improvement Phase
Our most successful BlueConic users are continuously improving the quality of their recommendations. Once you've figured out the best algorithm combination across all visitors, it's time to start thinking about specific audience segments that would benefit from recommendations specifically tailored to them.
Next steps
Familiarize yourself with the product and content recommendation algorithms.
Set up a Product or Content Collector that will collect, organize, and store product or content data in the platform.