Many e-commerce companies wonder how they can compete with the recommender systems made famous by industry giants like Amazon. The new WibiEnterprise 3.0 platform from big data application provider Wibidata provides for building real-time apps that includes accessible machine learning to power a website with advanced analytics that fine-tunes itself, providing better recommendations over time, including more relevant search results and personalized content. Wibidata counts Google’s Eric Schmidt as one of its investors.
The platform is designed for companies just beginning to use data science, said Omer Trajman, vice president of field operations at Wibidata. “They are not classically trained but they have an analytics background. They have been doing marketing analytics. The mechanics are similar, what has changed is the availability of data.”
Built on an open source framework called Kiji, WibiEnterprise 3.0 provides a common platform for building applications that leverage large data sets, but at the same time considers the need to quickly engage the customer. The platform was built around the understanding that companies have an opportunity to learn about their customers by analyzing their digital interactions and doing so means building a storage system that provides a 360-degree view of the customer.
In a manner similar to Amazon and Google, Wibidata’s Kijii framework uses a central storage system that allows a company to collect customer interactions across all of its applications, searches, sales transactions, Likes, clicks and requests for product information. It pools all the data so a company with sophisticated apps and services can do real-time queries and act on a customer’s recent information to deliver content personalization, relevant search results and recommendations.
Unlike traditional data warehouse based approaches that can get expensive and are centered around the transaction instead of the user that is generating those transactions, WibiEnterprise manages data in a much different way. In the context of e-commerce, these legacy systems store transactional information such as likely purchases, or shopping cart manipulations in a central fact table. For a retail bank, this data might include credits and deductions from accounts. SKU information or geographic location data are stored in dimension tables to provide a detailed view of the transaction.
A number of companies already depend on WibiEnterprise 3.0:
- A top 10 retailer which has integrated it with its website to create relevant, contextual shopping recommendations during the online sales process.
- An international retail bank is also using WibiEnterprise 3.0 technology to combine multiple customer data sources and apply in-house debt models to better detect fraud and credit risks.
- Virginia-based Opower uses WibiEnterprise 3.0 to deliver personalized reports to utility provider customers explaining how to reduce energy usage and save money.
- One of the largest SaaS providers uses WibiEnterprise 3.0 to help their customers identify prospective customers.
With big data solutions like WibiEnterprise 3.0, there is no doubt that it is getting easier to have the same capabilities as a company like Amazon.