Robust data models are essential for marketing optimization, but their implementation requires a reliable system for setting data standards comprised of naming conventions and tracking taxonomies. ​This is a great challenge for insurance companies that face data complexity due to their network of products and customers. Targeting at the right time is critical due to high CAC and high CLV, and campaign activation is led across multiple brands and agency partners. Robust data models are essential for marketing optimization, but their implementation requires a reliable system for setting data standards comprised of naming conventions and tracking taxonomies. ​This is a great challenge for insurance companies that face data complexity due to their network of products and customers. Targeting at the right time is critical due to high CAC and high CLV, and campaign activation is led across multiple brands and agency partners. 

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The Fortune 100 insurer struggled with siloed marketing data across regions and platforms. Inconsistent naming conventions and a lack of centralized tracking made campaign analysis and budget allocation difficult.  This limited visibility into the customer journey and hindered marketing ROI. Partnering with Accutics, they embarked on a data standardization pilot in North America, paving the way for a global solution.

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‍Solution:

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1. Data standardization: A scalable onboarding process established a robust marketing data foundation and consistent campaign tracking standardars across all regions.  This included user training and a the rollout of a centralized platform for managing the data taxonomy.
2. Improved campaign performance visibility: Accutics reduced "unspecified" traffic in Adobe Analytics by 85%, allowing for clearer attribution and more informed decision-making. This resulted in an 85% improvement in campaign performance in the US and a 75% improvement in key areas for Japan.
3. Seamless data flow to Adobe Analytics: Automated data collection and routing eliminated manual processes and ensured consistent marketing data. This freed up valuable time for analytics teams and improved data accuracy.

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Outcome:

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• Reduced costs: Unspecified web traffic reduction in Adobe Analytics led to a substantial campaign cost saving of $65,000 per month.
• Increased efficiency: Automated data workflows freed up analytics resources for strategic initiatives.
• Improved marketing ROI: Clearer campaign performance visibility empowered informed budget allocation, leading to a 100% increase in campaign spend in key South American markets