Think Behavioral Data Pipelines Are Too Complex? They’re Not.
The sheer amount of behavioral data that consumers generate online each day is exactly what makes it so useful to companies. It’s also, of course, what makes this data so difficult to process and consolidate. Syntasa’s platform is designed to make it simple for enterprises to build the behavioral data pipelines which provide better analytics, better decisions, and better actions.
Should Marketers Wait for IT?
The customer data which has traditionally resided in the realm of IT (and taken months or years to implement), is quickly becoming more accessible to marketers. In many cases, there’s no need to wait on IT for quick projects. Current marketing technologies put marketers in charge of their own fate; empowering them to track the visitor’s journey, build scores, and enact data-driven actions based on digital activity.
Tools to Help You Collaborate with Your Data Science Team
Putting AI/ML models into production is a team effort. Multiple individuals are involved and it takes a great deal of planning and teamwork…
Test Drive Syntasa’s Customer Intelligence Platform Today
Syntasa is now offering a free trial of its platform to show firsthand how it can help your team unlock more value from your clickstream data.
Sizing Lookback Windows with Cohort Analysis
How to choose the appropriate window size for your dataset, and what tradeoffs you need to consider when making that choice.
The Death of Retail and How Big Data Can Help Revive Them
As digital experiences take over and consumer behavior changes, survival for traditional retail stores hinges on finding a way to compete with…
Syntasa at the 2019 Adobe EMEA Summit
Syntasa was at Adobe Summit and here’s what we discussed with attendees…
Customer Engagement Strategies from the Trenches of Social Gaming
Now that brands have defined and built digital customer experiences, it’s time to think about customer engagement strategies which will…
Towards a Next-Gen Data Lake Architecture
In order to generate ROI from our data lake investments, we need to successfully productionise multiple AI-powered programs within our data lake. That is, programs which produce a continuous stream of actionable insights based on the digital, enterprise, and third-party data being stored inside data lakes.
A CDO’s Strategy for Successful AI Implementation
Although there is no single standard when it comes to implementation of AI, we see the successful ones going through these three phases.