Marketing Analytics: From Traditional BI to Dynamic AI

Gaining insights from customer data has never been more important. There has never been a time when gaining insights from customer data has been more important. Traditional analytics and business intelligence software was built to address structured data, providing business insights with reporting and visualization capabilities from aggregated and summarised information that could take weeks to process.

Predictive Churn AcceleratorThe modern marketer needs quicker, even real-time insights for always-on campaigns across digital channels. Marketers have been working around the limitations of traditional analytics for some time. Many software solutions have been implemented to address the gap created by customer data silos and data integration challenges.

Unfortunately, it’s impossible to procure a software platform or tool that can address the unique needs of large organizations to bring together customer data from disparate tools and sources.  If the goal is to really transform customer engagement and execute high-performance multi-channel marketing across digital platforms, a new approach is needed.

Check out our recent handbook on AI Enabled Marketing Analytics

Supercharging Marketing Analytics using Custom Data Models

Marketing teams are looking for a scalable solution to easily bring customer data from various channels and sources together to fundamentally change how companies engage customers across channels by offering truly personalized offers and campaigns while achieving phenomenal ROI on marketing spend. Furthermore, traditional analytics approaches have struggled to provide predictive insights from all these customer data hidden in disparate tools and data silos across the organization.

AI-enabled data models can analyze customer behavior and segments in real and near real-time and even automatically tweak campaigns. AI supercharged marketing analytics also provides predictive insights so that marketers can personalize customer experience. With traditional analytics approaches and tools, this would be difficult if not impossible to execute. In additional AI approaches to marketing analytics are much faster to implement than traditional analytics. Most projects take just a few weeks to execute but are smaller in scope and use case-based. For example, these use cases can take advantage of AI and be deployed in just a few weeks:

  • Real-time customer segmentation models
  • Customer lifetime value analysis (LTV)
  • Conversion/Purchase Predictions

Most enterprises are migrating to cloud-native big data architectures and are in the process of establishing the back-end infrastructure needed to leverage AI data models. Large scale data warehouse and cloud migrations projects are needed to make all these customer data accessible to the latest new AI algorithms that can make sense of all the disparate data sources and formats.

In order to go on this journey, large organizations need a partner, a geek squad of data scientists as it were. Your very own nerd brigade to go on this journey with you with creating the data models that sit on a cloud-native big data infrastructure custom-built for the unique needs of the organization. Successful marketing analytics relies on a unique combination of customer data and AI models and is in fact a gold mine of potential competitive advantage if mined effectively.

If you are growing your business and want to stay competitive in this rapidly changing marketplace with the latest in analytics, Machine Learning, and AI, contact us at info@SpringML.com or tweet us @springmlinc.