It’s projected that 75% of the data from surveys, market sentiments, customer satisfaction, and other critical sources is never analyzed by marketing analytics leaders.
“Our vision is to ensure data platforms aren’t impediment but the driver to success”
– Rawcubes, Co-Founder, Deepak Sondhi
Diverse and masked buying signals from thousands of sources
Separating noise and bias from the overwhelming data manually becomes a hassle
Growing amount of Qualitative & Quantitative data
Data silos across the organziation creates impedimaents for collaboration
Collection of data at scale and ingesting it into an intelligent customer-centric data platform
Building a modernized platform that can utilize services across all multiple cloud providers is expensive
DataBlaze uses advanced data strategies to intelligently harmonize any source of information related to your customer and delivery actionable insights.
Whether you are analyzing social media, CRM, website, survey, Google, advertising, external sources, etc. Not only that, you can integrate other sources like critical business applications and ERP. All this provides context which can be tied back to business.
Think of Knowledge Explorer like a google search wizard that knows everything and anything aboutyour customer, the market, and competitors.
- Find me evidence on which segment of the customer had the maximum impact on the net promoter score?
- What are all the negative scenarios today that impacted the buyer behavior for product X?
- Score the highest segment that was targeted in campaigning that ha negative brand sentiment?
Customer analytics with intention. The difference between basic insights and extreme customer insights is having access to data from every source, all at once.
Rawcubes’ unique Knowledge Graph-backed analytics helps marketing teams stay ahead of customer intent by identifying critical patterns and relationships from any source of information.
Do you rely on pre-built reports? With Knowledge Explorer and dynamic analytics (linear regression, factor analysis, cluster analysis, T-testing, etc.), teams future extend their capabilities to analyze important metrics all in real-time. Types of analytics that is supported by DataBlaze.
- ALGORITHMIC ATTRIBUTION FOR CHANNEL CONTRIBUTION
- RECOMMENDATION MODEL
- RFM MODEL FOR CUSTOMER RECENCY
- PROBABILISTIC CLUSTERING FOR CUSTOMER SEGMENTATION
- Survey analytics
Real time Audience view
ML Models – churn, Up and Cross – sell