For one global manufacturer, the time it takes to provide a sales quote means everything given the highly competitive nature of its industry. The company stocks over 100,000 skus and receives a request for quote every 4.5 minutes. If the company is not able to respond in a timely manner, it risks losing incoming sales opportunities to competitors. Given the vast number of product skus, the rapid pace at which quote requests come in and the fact that the manufacturer has over 600 people on its sales team, the company was curious if technology could improve the process for and speed at which it is able to provide sales quotes, providing the company with a competitive advantage.
Concurrency leveraged its artificial intelligence and text analytics expertise to create a Natural Language Processing (NLP) model to process incoming emails in real-time to support the manufacturers efforts to respond to request for quotes as quickly as possible.
To understand the impact of this solution, it is helpful to have a deeper understanding of NLP. NLP is a field of artificial intelligence that deconstructs vast amounts of text data and codes the semantic relationships between words. The goal is to create a system that can not only understand and quantify the intent hidden within human language, but also generate sensible text responses that mimics a human. NLP algorithms can power innovation with ease while staying hidden behind everyday interactions in the digital world. Word suggestions that show up when you type into Google or your phone chat window and language translation apps are just some of the examples for NLP in use.
For purposes of this project, Concurrency created an NLP model that has a Microsoft Outlook integration to provide the manufacturer’s sales team with product SKU links to existing ERP quoting tools. The NLP model leverages Azure infrastructure including Azure Machine Learning, DevOps, Databricks, Spark NLP, Functions, Logic Apps, Service Bus, SQL and Kubernetes Services to support live, real-time parsing of incoming quote emails.
The manufacturer was able to leverage the resulting NLP model to increase the speed at which it is able to provide a sales quote for an incoming request for quote inquiry. Doing so has positioned its 600+ person sales team to provide more timely, accurate customer responses which has increased sales conversion rates, revenue and profits.
Interested in learning more about how AI technologies such as Natural Language Processing can improve the speed at which you respond to customer inquiries, make operations more efficient and drive business value at your organization? Contact us today.