“Cami” Boosts Customer Engagement at Dixons Carphone
Dixons Carphone is a major electronics retailer that is based in the UK but employs many people over 11 different countries. Dixons Carphone provides consumers with products and services that help them lead seamlessly connected lives at home, in the office, and on the move. Similar to most retailers, Dixons Carphone has had to adapt to modern consumer buying patterns by incorporating a larger amount of online product research and shopping. In fact, 90% of their customers start their shopping in some way or form online, and an astounding 65% use their phones to assist them while shopping in-store.
Dixons Carphone partnered with Microsoft with aims to find better ways to increase customer engagement as well as ways to better optimize employee time spent with customers, they determined that AI was the answer. Specifically, Dixons Carphone investigated the capabilities of the Microsoft Bot Framework and Microsoft Cognitive Services in the context of customer interactions. The Bot Framework helps companies build, test and deploy intelligent bots capable of interacting with customers in a conversational way, working in tandem with Cognitive Services, a collection of intelligent APIs hosted on Azure that provide the underlying language and image recognition capabilities that power the bots.
After contemplating and brainstorming a personality and persona for their bot, Dixons Carphone decided on “Cami” with a mildly geeky and confident personality. Cami, for the time being, accepts questions (as text-based input) as well as pictures of products’ in-store shelf labels to check stock status, using the Cognitive Services Language Understanding Intelligent Service (LUIS) for conversational abilities and the Computer Vision API to process images.
Dixons Carphone will also be putting Cami to use in order to help employees in their day-to-day responsibilities, for example, in doing stock checks. In addition, the research done in conjunction with Microsoft showed that when shopping in store, customers who researched a product online (as far as things like stock level) are frustrated by the fact that when they get into the store they must start from scratch through store employees. Cami helps bridge that gap through a “Wishlist” feature. As customers add items to their Wishlist, Cami saves the search criteria they used and store colleagues can pull up that information in-store to see what the customer was looking for, leading to a much more efficient shopping process.
When Dixons Carphone goes live with the use of Cami, they will use the Cognitive Services Text Analytics API, Azure Application Insights, and Power BI dashboard to review which products customers are looking at, the sentiment of their interactions, and the questions they are asking. Understanding the questions that customers are asking and analyzing their interactions with the bot will help the company improve their communications and messaging as well.
Arvato Bertelsmann Protects Online Merchants from E-Commerce Fraud
An estimated 70 percent of online sellers in Germany have suffered fraud attempts, but only 14 percent of them use any safeguards today. Even though merchants are aware of the dangers of e-commerce fraud and the solutions available to protect themselves, they lack the resources to be able to manage the risk efficiently. On top of this, hackers quickly adapt their fraudulent ways and as a result, whatever solutions are put in place must adapt as well.
Arvato Financial Solutions, an integrated financial services provider, offers vital services around e-commerce safety for some 2,000 odd customers. One of eight divisions of Bertelsmann – the German media, services, and education giant – Arvato has recently partnered with Microsoft, inovex GmbH (a cloud and big data specialist), and a few of Arvato’s e-commerce customers with aims to create a fraud detection solution using Microsoft’s big data and machine learning offerings.
Through the combination of Azure services with the open-source Storm and Hadoop frameworks, Arvato built an integrated cloud-based solution that uses a modern lambda architecture to process massive data quantities using both batch and stream processing. The batch path transforms existing data using Hadoop, then, by applying machine learning algorithms, the solution develops self-learning analytical models from past fraud cases, for early recognition of any new fraudulent approaches. The stream-processing path captures incoming real-time transaction data via Azure Event Hubs. It then analyzes the data with the assistance of Storm and Azure Machine Learning to uncover fraudulent activities as they happen.
An important goal of the project was to visualize and monitor the models, and Power BI serves this function by displaying data sets drawn directly from cloud sources, Azure HDInsight and SQL Database, on several large screens in Arvato’s monitoring center.
Avato’s investment in good cloud design is paying for itself, helping the company reliably fulfill SLAs using cloud services. Their flexible architecture enables rapid deployment, which is key for fraud recognition in an international e-commerce setting. Using Microsoft machine learning on big data, Avato has created an innovative e-commerce fraud recognition solution and built the basis for innovative financial BPO services based on Microsoft Azure.