TL;DR:
- FedEx collaborates with Microsoft Azure to leverage machine learning tools for improved package visibility and customer experience.
- Machine learning enables FedEx to predict and address delivery issues, leading to continuous improvement and enhanced operations.
- Washington Federal Bank utilizes natural language processing (NLP) tools to streamline customer interactions and reduce friction within its call center.
- NLP tools such as Amazon Lex and Amazon Polly provide seamless voice recognition and transcription capabilities for enhanced customer engagement.
- Archive360 leverages machine learning to improve archival data search, allowing customers to easily extract relevant information from legal files, videos, and more.
- Azure Video Indexer and Azure Cognitive Search, combined with open-source AI marketplaces, enable accurate and efficient search capabilities.
- The integration of machine learning and AI technologies enhances customer experiences, drives operational efficiencies, and provides valuable insights for businesses.
Main AI News:
In today’s business landscape, machine learning is revolutionizing the way companies gain valuable insights. FedEx, in collaboration with Microsoft Azure, has harnessed the power of machine learning to enhance its operations, digital solutions, and customer experience. This strategic approach has led to improved package visibility and the availability of better information for both customers and customer service representatives. The result? A continuous cycle of improvement that significantly impacts tens of thousands of FedEx customers.
Neil Gibson, the senior vice president of global customer experience for FedEx, emphasizes the significance of even the slightest data point enhancement. Leveraging cloud-based machine learning tools, FedEx, as well as smaller enterprises and those catering to B2B customers, can unlock exceptional improvements in the customer experience. According to Christina McAllister, a senior analyst with Forrester, the adoption of advanced AI applications is on the rise, thanks to low-code tooling and more intuitive user experience designs that empower average business users to engage with AI solutions without requiring a data science degree.
So how exactly does machine learning alert FedEx to potential issues? By leveraging advanced machine learning techniques across its business operations, FedEx has deployed tools from Microsoft Azure, including Azure Databricks, Azure Machine Learning, and Azure Data Factory, along with complementary external solutions that seamlessly integrate with Azure, such as GitHub. Anthony Norris, the senior vice president of IT for global platforms and customer solutions at FedEx Services, explains that machine learning has elevated their data intelligence capabilities, acting as a metaphorical canary in the coal mine that warns leaders about systemic challenges or individual delivery problems. This early warning system enables FedEx to intervene promptly and deliver exceptional value to its customers.
Moreover, FedEx’s machine learning systems enable a deeper understanding of broader challenges. For instance, during the 2022 holiday period, FedEx’s call center experienced a significant volume of calls related to deliveries requiring customer signatures, accounting for 25 percent of all calls. However, only 2 percent of deliveries actually necessitated signatures. This discrepancy raised concerns about the customer experience, prompting FedEx to form dedicated teams to address specific issues based on insights gleaned from machine learning. By leveraging AI, FedEx has successfully tackled challenges associated with time-sensitive pharmaceutical deliveries, developing a packaged fingerprint that enables monitoring and intervention for high-priority packages.
Before the era of machine learning, FedEx relied on rules-driven approaches to gather data intelligence. However, given the complexity of their network, it was difficult to devise rules to address every possible scenario. Machine learning models, on the other hand, adapt to dynamic situations in real time and leverage existing knowledge to provide accurate and timely insights. As a result, FedEx can proactively communicate with customers and significantly improve accuracy. With enhanced reliability and visibility, the customer experience is elevated to new heights.
In the realm of customer engagement, natural language processing (NLP) plays a pivotal role. Washington Federal Bank (WaFd), a leading Seattle-based institution, has embraced AI and machine learning to transform customer interactions, particularly within their call center. By leveraging tools like Amazon Lex and Amazon Polly, which are built on the Amazon Web Services cloud, WaFd has created a seamless experience for customers. These NLP tools transcribe spoken language and provide callers with natural-sounding automated phone voices, reducing friction and enabling quick access to the right personnel.
Machine learning also facilitates the analysis of customer calls at WaFd. By providing real-time insights into the purpose of a call and the caller’s sentiment, such as annoyance or frustration, agents are equipped with valuable information that enhances their interactions with customers. Additionally, WaFd has introduced a voice authentication service that allows customers to sign up for voiceprint recognition instead of answering security questions over the phone. This highly reliable technology distinguishes genuine, live voices from fraudulent voice recordings, resulting in faster transaction completion times and reduced call volumes for the call center.
For Archive360, a New York-based company specializing in archival data search, machine learning has been instrumental in improving search capabilities and efficiency. Over the past decade, Archive360 has harnessed machine learning to extract meaning and content from archival materials, ranging from character recognition to generating text from speech. The transition to the Azure cloud has further expanded possibilities and cost savings for customers, enabling them to search diverse content, including legal files and videos, in multiple languages. Tools like Azure Video Indexer and Azure Cognitive Search seamlessly integrate with open-source AI marketplaces, while continuous feedback from customers helps refine and enhance search results.
Azure Video Indexer, in particular, provides accurate transcripts of video content, supplemented by a confidence score that allows human users to rate the relevance of the results. This iterative process of machine learning and human feedback fosters improved accuracy over time, empowering businesses to unlock valuable insights from their archival data.
Conclusion:
The implementation of machine learning and AI technologies in various industries, including logistics, banking, and data management, has transformative effects on customer experiences and operational efficiencies. Companies like FedEx, Washington Federal Bank, and Archive360 have leveraged these technologies to enhance package visibility, streamline customer interactions, and unlock valuable insights from data. As the market continues to embrace machine learning and AI, businesses will have the opportunity to improve their operations, elevate customer experiences, and gain a competitive edge.
