We recently built an analytics platform for a startup company whose mobile game acts as a digital advertising platform while providing rewards and instant winning experiences to its users.
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In a previous blogpost we explored how retail companies are utilizing emerging augmented reality (AR) technologies to close the “imagination gap” between the showroom and digital experience to improve their online sales.
The Solr suggester component allows you to vastly improve your search capabilities and experience. It provides users with automatic suggestions for query terms and can be used to implement useful auto-suggest features in your search application.
Augmented Reality (AR) is a type of computer vision technology that allows real-time views to be overlaid with virtual, computer generated objects. Powerful modern computer processing has also opened the doors of the mobile world to this technology.
Delivering actionable insights in real time by moving from batch to stream processing: a digital media case study
In early 2016 one of our online media customers came to us with a problem. Our customer, a media giant, hosts articles from its newspapers and magazines on its websites. Each of the articles’ web pages has three ad blocks, and the customer buys paid redirects to the article pages.
The retail industry is embracing big data, analytics, and machine learning (ML) to improve customer engagement, optimize operations, and drive sales. Increasingly, we’re seeing business intelligence systems that were once based on
Recently, we started working on a mini chat application for a client support team. Though we expected a quick and easy implementation, we hit on some unexpected issues. We built the UI for our real-time application, wrote unit tests, and prepared some routes and controllers on the back end.
A responsive UI is becoming a staple of the digital experience. Learn 18 of the best practices in planning, designing and implementing these projects correctly.
In this blog post we share our experience delivering deduplicated data during In-Stream Processing for a large-scale RTB (real time bidding) platform. A common problem in such systems is the existence of duplicate data records that can cause false results when processed by the analytic queries.
Unit tests are the de facto standard for establishing a consistent product and an efficient continuous delivery process. They force you to write well-structured code that is split into modules with clear interfaces between them. Unit testing is necessary to safely refactor your code.
Dear Oracle ATG users, It is time to treat your ATG stack as a legacy system and move forward to the cloud, open source, and microservices.
If you are an omni-channel retailer with a significant and growing online presence, it is likely that Oracle ATG has been your platform of choice for many years. You have probably invested thousands of hours and tens of millions of dollars in the tuning of your Oracle ATG implementations.
In our previous post, we showed you how to use image recognition to solve the issue of misattribution in e-commerce catalogs. Once you start to trust your models and have trained them to detect a valuable amount of attributes, it is easy to expand from attribute verification to auto tagging.
Open source full-text search engines provide rich functionality out of the box. However, there are some use cases when naive implementation may lead to terrible customer experience. We faced one of such use cases when we worked with a patent management company.
Detecting and correcting e-commerce catalog misattribution with image and text classification using Google TensorFlow
In our previous post, we discussed the impact of product misattribution in e-commerce and how image recognition with Machine Learning can be an important tool to resolve this issue.
Developments in Image Recognition Machine Learning can improve E-commerce catalog quality, search-ability and tailor user experience to individual customers.
Increasingly, regional and national retail chains are facing anemic sales figures in their “brick and mortar” stores.
A customer may need to search for an item by size or price, not product name. This means we must map our e-commerce catalog hierarchy as an inverted index.
In the previous blog post we went through the details of how to set up the Docker infrastructure with Mesos and Marathon, and how to bootstrap the environment to get it ready to host application services. Now it is time to deploy some applications.
Companies like Google, Amazon, Apple, IBM, and Microsoft, along with an ever-increasing array of AI startups, have been gradually perfecting Conversational User Interfaces (CUIs) that allow people to interact with computers in natural language.
In this post we’ll focus on more details of Mesos and Marathon, and provide you with scripts to provision the complete computational environment on any cloud of your choice.
Post 5: DevOps stack for In-Stream Processing Service using AWS, Docker, Mesos, Marathon, Ansible and Tonomi
This post is about the approach to the “DevOps” part of our In-Stream Processing blueprint — namely, deploying the platform on a dynamic cloud infrastructure.
In online commerce, catalog navigation functionality is one of the key aspects of the overall user experience. Removing frustration from the product discovery and selection experience is a big part of this mission.
In the previous post we discussed which models we tried for sentiment classification and which one has demonstrated the best performance. In this post we’ll show you how to visualize our under-the-hood findings so that others can see the results of our analysis.