Each year, customer service and the customer experience (CX) are reported to be more important than ever. According to Forrester, 72% of businesses say that improving the customer experience is a top priority. A study from New Voice Media indicates that companies lose more than $62 billion due to poor customer service.
Leading software organizations are increasingly aware of the need to differentiate themselves by offering excellent technical support and customer service. Yet as products become more complex and the high-tech market becomes more competitive, many companies are concerned that support costs will consume already thinning profit margins.
One major software company identified technical support as a key competitive differentiator but struggled to close support calls quickly because staff had trouble finding the information they needed. As a result, incidents that should have been closed with a single call were extended.
Support staff had an informal time threshold of four to nine minutes to find the information they needed to resolve a customers' issue. When the time limit was reached, they requested additional information, such as a log file from the customer and the ticket remained open. The result - additional calls to the customer and an increase in the number of level 2 support incidents.
"The combination of ontology management and auto-classification is extremely powerful. “Individually, each component provides value, but the value created by combining them is exponential. We were able not only to provide more reliable search results, but also to provide users with better and more intuitive ways to explore the content. Semaphore was the only product we evaluated that brought all of these components together."
The company applied semantics to their store of technical support documentation, including manuals, knowledgebase entries, support histories and outcomes, etc. The documentation existed throughout the enterprise, in disparate systems and formats.
Using Semaphore Knowledge Model Management (KMM), the company developed a model that included the concepts, topics and subjects of interest to technical support staff. The model included products, known issues, bugs and related fixes as well as the relationships between concepts.
The model was published and used by Semaphore Classification & Language Services to analyze each document and apply precise, complete, and consistent metadata based on the rules derived from the model.
The metadata created by Semaphore is used to enrich the existing metadata in a content management system such as, SharePoint, OpenText and Documentum to improve search and retrieval results.
The company integrated Semaphore metadata with their existing Google Search Appliance to leverage the knowledge created by the model enriched tagging, to return highly relevant results.
In the final step, they implemented Semaphore Semantic Integration Services (SIS), to further enriched the user experience by allowing real-time queries against the model and presenting the results as topic maps, topic pages and related content links.
SIS lets users perform faceted search and browse content using filters to narrow the result set.
The company leveraged Semaphore’s Semantic AI platform to build a customer support portal that delivers highly relevant results to user queries regarding support issues for a fraction of the cost in an efficient manner.
The quality of search results improved dramatically, as did the confidence of technical support personnel in those results. The number of incidents closed in the first call increased, while average call time remained steady. Customer and employee satisfaction increased, and overall support costs were reduced.