WibiData Launches WibiRetail Rooted in Sophisticated Machine Learning and Predictive Analytics

wibidata_logoWibiData, a leading big data personalization provider, ha announced the launch of WibiRetail, a new software platform designed for retailers to rapidly deploy algorithmically-driven personalized shopping experiences.  Ideal for marketing, merchandising and data science teams, WibiRetail empowers retailers to deliver experiences that amaze and delight their customers.

WibiRetail has been designed from the ground up to address the gap between the consumer experiences that modern machine-learning and predictive analytics technologies are capable of delivering and what currently exists today.  Most large retailers, whether department stores, specialty stores or mass merchandisers, are supporting their digitally-connected customer touch points with segmentation, manual analysis and “one size fits all” product recommendation solutions.  The result is a universe of shopping experiences that are largely conservative, unimaginative and undifferentiated.

Consumers have begun to expect digital and even offline experiences and interactions that are on par with those being delivered by large innovators like Amazon and Google and smaller forward-thinking retail entrants.  WibiRetail helps retailers capitalize on these changing expectations and improve engagement and conversion with intuitive, natural  interactions like:

  • intent-aware commerce applications that use in-the-moment data to determine whether a consumer is shopping for herself, a family member or a partner

  • discovery and browsing experiences that engage consumers with media and editorial content that appeals to their unique tastes and preferences

  • email and mobile marketing offers that reflect consumers’ current behavior and context

Retailers are no longer competing against just themselves, but also against the experiences that consumers have grown to love from Google, Facebook, and Twitter. In order to compete with these experiences, companies must make a shift to big data and analytics techniques pioneered by these technology giants.” said Garrett Wu, founder and CTO, WibiData.  “We built WibiRetail to solve this problem, empowering retailers to move beyond stale batch analysis and segmentation and into the era of experimentation, big data and real-time machine learning for true one-to-one experiences with their customers.”

WibiRetail is an end-to-end platform that gives retailers the ability to personalize every interaction with their customers in real-time. WibiRetail keeps data in-house, and arms retailers with state-of-the-art data infrastructure and simple interfaces for next-level personalization, without needing to build these tools in-house. This allows merchandising and data science to seamlessly explore data, develop and train models, and deploy the best models to production instantly where they are scored on the fly, delivering real-time, individualized and contextually relevant experiences across all touch points.

WibiRetail features include:

  • Customer-centric data store – Organize all information about customers into a cohesive customer-centric schema that uses the newest big data storage technologies and is optimized for real-time personalization.

  • Bulk Import – import customers, products, order history, web analytics, social and other data from existing data sources

  • Interaction Tracking – a lightweight application endpoint that can easily track all interactions from various front end applications in real time (website, tablet, mobile app, in-store kiosk, call center, etc)

  • Predictive Models – models optimized for personalizing product lists at massive scale in retail (customers also viewed, customers also purchased, what’s hot, best sellers, recently in stock, about to go out of stock, recently on sale, etc.).

  • Model Deployment – framework for data science to influence and change the behavior of models, create their own, and deploy on the fly with no re-engineering needed

  • Marketing and Data Science Consoles – monitor model performance, simulate customer experience and track merchandising and marketing performance

 

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