Key Considerations When Building Pipelines for Behavioral Data

Most companies recognize that they have troves of customer data (both online and offline) and that leveraging this will help them better understand their customers. Research cited by McKinsey also confirms that organizations that leverage behavioral data to generate behavioral insights outperform peers by 85% in sales growth and more than 25% in gross margin.

If you're considering (or already trying), to build pipelines to deliver behavioral data to your data lake, you know about the challenges associated with this kind of endeavor. Synthesizing external behavioral data from multiple sources into a uniform and useful format is an arduous process. That's why we've created this guide for you. 

In this guide, you'll find:

  • Initial steps to consolidating your customer data, including behavioral data
  • Ways in which large organizations have built pipelines for behavioral data 
  • How to overcome your pipeline challenges and increase speed to market so that you can finally get your AI projects off the ground 
Key Considerations When Building Pipelines for Behavioral Data: A Guide for Data Engineers (Syntasa Synthesizer)

Get the guide here

As the leader in AI Assisted Customer Analytics, Syntasa is helping enterprises generate real-time, actionable customer insight to enhance the customer experience and drive conversions. Syntasa's software can be installed on-premise, allowing the enterprise to integrate individual-level clickstream data with sensitive enterprise data. By leveraging open-source technologies, Syntasa seamlessly fits in the new enterprise big data analytics ecosystem and provides easy-to-use behavioral analytics applications which significantly reduce time-to-value. The company is headquartered in the Washington, DC metropolitan area with an office in London.