What Semantic Search Means for Knowledge and Support Teams
For many helpdesks, call centers, and other support teams, their biggest challenge today may simply be finding the internal knowledge they need.
Business organizations today are generating data, knowledge, and content faster than ever before. Their internal knowledge bases frequently grow beyond easy management, leading to difficult searches and contradictory results. This is a significant issue when someone calling into a helpdesk wants their problem resolved quickly and accurately on the first call.
Semantic search systems, powered by next-generation AI natural language search processes, present the best solution yet: Smart understanding of the meaning of search queries, without relying on ‘dumb’ keyword matching.
Integrating a contextual semantic search into your knowledge management system can immediately improve CS and helpdesk response times, efficiency, and can even cut costs.
Read on to learn how it works.
How Does Semantic Search Interpret Intent, Context, and Similarity?
The key distinguishing feature of semantic search technology is that it leverages AI to discern the actual meaning of words, rather than simply looking at the words themselves.
For example, say someone searches “what are the best hiking shoes for national parks?” In an old keyword-based system, the search engine would simply pluck out (best hiking shoes) and (national parks) then look for webpages that include those phrases. Then the user is left to tediously sort through the results, page by page.
A semantic search system, on the other hand, would fully parse the search and understand the user is interested in the outdoors, that “national parks” could potentially relate to any kind of hiking, and look for products/reviews that match. It could also pull in the user’s geographic data for extra context. If they’re currently near Yellowstone, the search would modify itself to look for shoes fit for Yellowstone’s terrain. It could potentially recommend exact shoes for the user’s needs at that moment.
The same holds true for your own helpdesk and other contact centers. Agents don’t have to rely on keyword searches and manually paging through pages and pages of KB articles. They merely ask their AI agent a question, and it processes their query much like a human would before answering based on existing internal knowledge.
The AI can also look at customer records so, for example, it would not suggest a tech support step that had already been performed on a prior call. This contextual awareness adds to its value.
The result is simply better searching. Your human agents find answers more quickly and more accurately, allowing them to assist the caller more quickly as well. This cuts CS costs, while improving customer satisfaction.
How Does Semantic Search Bring Faster, More Accurate Answers?
Within the software, it’s a four-step process.
1 – Query Analysis
After the user makes a query, the AI breaks it down and analyzes the words and meaning, seeking a full understanding of the query and the intent behind it.
2 – Knowledge Graph Comparisons
Modern semantic search systems rely on knowledge graphs, which are effectively maps that describe the connections between pieces of information. This is at the heart of its contextual understanding, and also allows it to make reasonable inferences.
For example, if the AI knows that John Smith is the lead hardware designer at Phone Company, and it knows that Phone Company released Smartphone Alpha, it could then connect the dots and understand that John Smith designed Smartphone Alpha without being told so specifically.
This deductive process makes semantic search especially powerful.
3 – Content Analysis
These same basic processes are used when the AI begins crawling through the knowledge base looking for answers to the query. It reads the KB entries with semantic contextual awareness, looking for the information which most closely matches its understanding of the question.
4 – Results
Having found the best-fit answers, the query is answered in natural language, typically alongside suggestions for next steps or other searches to conduct.
What Are the Benefits of Semantic Search in Enterprise Knowledge?
Semantic search brings numerous benefits to virtually any organization with a knowledge base too large for workers hold in their own heads.
You can see:
- Greatly improved call center metrics like AHT and FCR
- Internal helpdesks getting employees back to work faster
- Fewer errors or unhelpful tech support suggestions
- Customer-facing self-help portals powered by the AI search, deflecting tickets while offering even faster service.
- Improved customer satisfaction
- Higher employee morale
- Lowered CS costs
Beyond the initial startup and deployment costs, semantic search systems will quickly provide strong ROI while continuing to become smarter and more efficient over time, as the AI keeps learning.
Frequently Asked Questions
Semantic search relies on complicated multi-dimensional mathematics to map the connections between pieces of information, and improve the accuracy of search results.
“Embeddings” refers to extra meta-data within the text, containing mathematical representations of the content. Similarity scoring is based on the search engine’s own calculated estimations of the similarity between potential results. Combined, these mathematical elements greatly boost the AI’s ability to offer accurate, customized search results.
For best results, knowledge content should be prepared for semantic search systems and their methods of processing information. Some common mistakes include:
- Chunking longer articles into smaller, seemingly disconnected articles. Keep relevant information together so the AI more easily understands that it’s all connected.
- Missing metadata. Metadata is vital for providing extra context to the AI.
- Generic or repetitive content tends to confuse AIs. Your KB articles should be concise, to the point, and directly relevant in-context.
- Inconsistent terminology, especially where Proper Nouns are concerned. Be sure uncommon words are always used consistently to aid AI understanding.
Generally, your existing metrics should show the value of contextual semantic search systems. Average Handle Times will go down; First Call Resolutions will go up. You can also measure how long agents spend searching the KB while on calls; that number should go down drastically after semantic search is implemented.
Additionally, pay attention to which articles are most/least accessed. The most popular articles should receive attention to ensure they remain relevant; the least popular should be revised to be more relevant.