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To best serve customers, Dell Services leverages in-house built, AI and Generative AI tools such as Next Best Action (NBA), which is part of a larger ecosystem of support optimization AI tools known as Contact Connect AI. The customer support journey typically begins when a customer encounters a problem with their purchased hardware, leading them to seek self-support solutions. Once these self-support options are exhausted, they contact official customer support through various communication channels. Tools like NBA have been trained with historical data to predict the best resolution based on symptom and telemetry data. The PSQN Retrieval solution supplements NBA capabilities and aids agents in troubleshooting specific hardware issues faster and more efficiently by surfacing and ranking the most relevant KB articles according to their relevance, while filtering out irrelevant ones.
Historically, this solution employed a primarily lexical-based, direct database implementation for IR, which proved to be suboptimal. It retrieved the top ten PSQN articles based solely on keyword similarity matches against a manually curated keyword set for each specific article. These articles were then shown to the support agent through the NBA user-interface. However, since many of these articles were not always relevant to the given symptom, support agents had to sift through each of the 10 articles to find the most applicable article for resolving customer issues. This suboptimal retrieval process, coupled with the need to manually search through multiple articles, introduced significant inefficiencies. DTS’s enhanced article retrieval strategy, based on semantic embedding, addresses these inefficiencies by ensuring that only the most relevant articles are surfaced.
In addition to improving the retrieval strategy, substantial optimizations were made to the PSQN article data preparation and organization workflow, eliminating error-prone and tedious human interventions in manual keyword generation and article tagging for applicable hardware configurations by leveraging Generative AI technologies. Before DTS’s new solution, experts analyzed specific articles, manually identified keywords, and tagged articles with specific hardware configuration identifiers using a manually curated product taxonomy. This solution fully automates both the article tagging process and the article content preparation for database indexing, yielding substantial accuracy gains and process improvements. Now, only the most relevant articles—up to a maximum of 10 (or none if no articles are relevant), are shown reducing the time agents need to sift through the filtered top known articles.
DTS’s improved implementation enables faster diagnosis and troubleshooting of customer issues, leading to higher customer satisfaction.