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BIG DATA ANALYTICS

BUSINESS INTELLIGENCE

– Quantivo Behavior Analytics Review

Product Snapshot

Technology

Quantivo’s product line focuses on remotely hosted SaaS solutions using patented technology not based off Hadoop architecture.

Customer Focus

Quantivo focuses on mid and large-sized companies who conduct their business primarily through online sales, retail, wholesale or warehouse distribution in a variety of industries.

Select Customers

ASOS, RAPP, Kohler, Party City, Orange, Simplicity Creative, Manheim Auctions

About Quantivo Behavior Analytics

- Quantivo Behavior AnalyticsQuantivo Behavior Analytics is a solution aimed at the analysis of customer behavior. Using information from multiple data sources, the solution is able to not only spot trends in customer behavior, but identify correlations between different trends.

Customers can be segmented into specific demographics based on their behavior patterns, allowing a company to better study the effectiveness of its targeted marketing and promotional campaigns. Additionally, “purchase analytics” are provided, which take into account granular variables like geography, store layout and regional demographics.

About Quantivo
Quantivo’s roots as a company date back to BigFix, a Big Data company purchased by IBM in 2010. With Quantivo CEO Dave Robbins offered cloud-based business intelligence solutions. After beginning with a focus on data analysis for retail vendors, the company expanded its service to also target prospective customers in the marketing, insurance and online sales industries.

Quantivo offers solutions in customer behavior analysis, marketing optimization and web data analytics. Central to Quantivo’s approach is speed. The company’s solutions boast the ability to deliver analysis of large data sets “within hours, not weeks.” This quick performance is combined with a simple drag-and-drop, web-based user interface which virtually anyone in a company’s command structure can use.

Quantivo Behavior Analytics Key Features

  • Customer segmentation derived from multiple data sources
  • Loyalty analytics of customer segments
  • CRM analytics
  • Customer purchase analytics
  • Instant data export
  • Context-specific targeting and filtering of different customer segments

 

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