Healthcare Company Sees 85% CTR and 2.6x Traffic Growth with AI

A global healthcare leader faced a challenge: predicting customer needs for cold and flu treatments. They implemented a machine learning forecasting model, boosting click-through rates by 85% and website traffic by 2.6x. This AI solution allowed for targeted marketing and optimized ad spending, leading to a successful data-driven approach.

Client:

Bayer is a worldwide company that specializes in healthcare and agriculture. Its impact extends across borders. Their dedication to healthcare and agriculture translates into effective treatments for pain, allergies, and cardiovascular conditions, alongside seed and pesticide technology advancements that benefit farmers worldwide.

Problem Statement:

● Reactive vs. Proactive Marketing: Traditionally, marketing relied on analyzing past data to react to trends. Bayer wanted to be proactive by predicting future customer needs.

● Improved Targeting: Their existing strategy used generic keywords for cold and flu products. The AI model offered a more granular view, allowing them to target specific regions and emerging search trends.

● Data-Driven Optimization: The model used various data sets (search trends, weather, public health reports) to predict cold and flu seasonality. This data helped them optimize marketing efforts throughout the season.

● Personalization and Efficiency: The AI model allowed for automatic keyword adjustments and ad copy optimization. This ensured targeted messaging for the right audience at the right time.

Results:

☑️ 85% increase in click-through rates (CTR): Targeted advertising based on accurate predictions led to a substantial increase in CTR, indicating improved ad relevance and engagement.

☑️ 33% reduction in cost per click (CPC): Efficient ad spend optimization resulted in a significant reduction in CPC, demonstrating improved marketing ROI.

☑️ 2.6x increase in website traffic: The combination of timely and relevant marketing campaigns led to a substantial increase in website traffic, showcasing the effectiveness of the AI-powered approach.

☑️ Beyond Marketing: Accurate flu predictions enabled better adjustment of production schedules to meet demand during potential early-onset flu seasons.

AI Solution:

Bayer developed a machine learning forecasting model using Google Cloud technology to address these challenges. The model combined internal search data from Google Trends with external weather and climate conditions data to predict cold and flu search trends across various regions.

How Bayer Applied the AI Solution

The implementation of the machine learning forecasting model transformed Bayer’s marketing approach, enabling them to:

● Plan marketing campaigns in advance: With accurate predictions of cold and flu seasons, Bayer could develop and activate marketing campaigns well beforehand, ensuring they reached customers at the peak of their needs.

● Target campaigns based on regional trends: The model provided granular insights into search trends, allowing Bayer to tailor campaigns to specific regions and emerging cold and flu-related searches.

● Optimize ad spend dynamically: Bayer used the model to automate keyword adjustments and ad copy personalization, ensuring that the most relevant and engaging ads were delivered to the right audience.

References:

1. How Bayer Uses ML to Predict Cold and Flu Trends

Industry: Healthcare, Pharmaceuticals
Vendor: Google Cloud
Client: Bayer