The intelligent customer service knowledge base is the whole process from data cleaning to dynamic optimization

In the tide of digital services, intelligent customer service is no longer the icing on the cake, but a cornerstone service that supports business operation and protects user experience. I have seen too many users who have lost due to the “intellectual disability” of intelligent customer service, and I also know how important a competent “intelligent assistant” is to the growth and reputation of the platform. The knowledge base is the core engine of this “intelligent brain”. Its build quality directly determines the direct effect of intelligent customer service.

However, building a user-friendly, accurate, and self-evolving knowledge base is no easy task. From the initial messy data cleaning to the final dynamic optimization, there are countless problems in the entire process. Today, I will combine my experience over the years to talk to you about the problems and corresponding solutions in the whole process of building a knowledge base.

1. Clarify the destructive power of the knowledge base

I still remember that one year when there was a big e-commerce promotion, our intelligent customer service suddenly had a problem: repeatedly pushing the wrong preferential rules to users, and the number of follow-up customer complaints soared by 200% in place! Afterwards, it was found that there were three historical versions of preferential documents in the knowledge base at the same time, and the system “randomly” selected the oldest version! Such negative cases are not isolated cases, and the hidden costs and destructive power of knowledge bases often lurk quietly in four key links:

1. Data Layer:Source data is like ingredients, if it is sent rotten vegetable leaves (redundant, chaotic format, full of errors), no matter how powerful the chef (knowledge base system) is, he will not be able to cook a good dish. The marketing department, the product department, and the customer service department each give a set of statements? Excel, Word, PDF, screenshots? If you don’t clean the source data, warehousing is the beginning of disaster.
2. Entry layer:Business experts may be confused by the terms that are “obvious”; The “human words” answer written by customer service may not be parsed by the machine at all. Throughout the input and translation process, serious errors (accuracy and context) can occur if you are not careful.
3. Search layer:The user asked, “How can I return the clothes when I buy too much?” In this case, the user did not mention the word “process”, and the knowledge base could not find matchable keywords at all! Rigid matching, in the face of the ever-changing natural language of users, allows intelligent customer service to answer questions.
4. Iteration layer:Product functions have been updated, event rules have been adjusted, and policies and regulations have changed…… Under business changes, if the knowledge base is still stuck in the “previous version”, then the answer it gives is outdated information, which may detonate user dissatisfaction at any time.

If these problems are not solved, the knowledge base is not an assistant, but a tool to create user dissatisfaction.

2. Data cleaning

The “foundation” of the knowledge base is data. But this foundation often has to be dug out of a piece of “garbage data”. Believe me, if you don’t lay a good foundation, you will pay back with double overtime later.

1. Data redundancy

Imagine that when the marketing department throws over a gorgeous product manual (200 pages of PDF), the technical department provides detailed API documentation (scattered in Confluence), the customer service team contributes three years of “historical Q&A essence” (a huge Excel), and the operation side has a bunch of scattered “618/Double 11 Event FAQs” (WeChat group chat history + email)……

To achieve these three challenges, product managers will only continue to appreciate
Good product managers are very scarce, and product managers who understand users, business, and data are still in demand when they go out of the Internet. On the contrary, if you only do simple communication, inefficient execution, and shallow thinking, I am afraid that you will not be able to go through the torrent of the next 3-5 years.

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When all these things are stuffed into the knowledge base? Congratulations, you will gain a bloated and slow “knowledge fat man”! This massive amount of duplicate data is not just a waste of expensive storage space. What’s even more frightening is that it can exponentially reduce the efficiency of retrieval! We once served a vertical e-commerce company, and we did not do strict deduplication in the early stage, but the “basic parameters” description of the same product was repeatedly uploaded by different departments more than 20 times in the library! Imagine that the user just wants to check a simple screen size, but the background engine has to go through more than 20 almost identical records, and the response time is directly dragged from the ideal 1 second to more than 3-5 seconds. The user’s side? The waiting progress bar turns and turns, the experience collapses in minutes, patience is exhausted, and bad reviews are just around the corner.

Our Filling Strategy:

1) Algorithm first: Don’t be naïve and expect artificial visual screening! In the face of massive data, it is hopelessly inefficient. We’ve introduced the tool of text similarity calculation:

• Text hash: Quickly calculate the “fingerprint” of the text and identify highly similar or even identical blocks of text. To deal with simple copy and paste, grab one grasp and one accurate.
• NLP similarity model: Calculate semantic similarity for expressions that “change the soup but not the medicine” (such as “mobile phone” and “mobile phone”, “unable to log in” and “login failed”). It’s like doing an efficient “DNA paternity test” for the data, no matter what vest you change, the core duplication can be found out. The tool can be selected with Python’s difflib, gensim, or directly use ES’s more_like_this query to get started.

2) Source governance: Relying solely on the later stage to address the symptoms rather than the root cause, it is necessary to establish a unified data collection template and force all departments to provide information in a fixed format.

• Product Information:What core fields (model, parameters, applicable scenarios, links to FAQs) must be included, and the format is uniform (JSON/YAML).
• FAQ：Strictly follow the structure of “standard questions + concise answers + relevant links/how-to”.
• Operation Documentation:Clear steps and screenshots/video links must be provided.

This is equivalent to installing a “standardized funnel” at the source of data, which greatly reduces the duplication and confusion caused by “silosing” from the beginning. There was a lot of resistance in the early stages of implementation, but with a few cases of accidents caused by data confusion, everyone understands.

3) Entry gatekeeping: In the core entry link of the knowledge base management background, we have added an intelligent plagiarism check reminder function. When the entry staff has worked hard to edit a new piece of knowledge and clicked “Save”:

• The system background starts the comparison engine in real time to scan the existing data in the database.
• Once the semantic similarity exceeds the preset threshold (e.g., 75%), a prominent warning box will pop up immediately: “Attention! Knowledge inventory in highly similar entries [link], do you want to add it repeatedly? Or merge updates? and give the corresponding operation button.

This trick may seem simple and crude, but the effect is outstanding! It directly intercepts a large number of meaningless repetitive labor in the entry process, and also reminds the entry staff to read the existing content first to avoid information fragmentation.

2. Complex data formats

Data sources are varied and formatted: product descriptions in Word documents, operation steps in Excel sheets, activity rules on HTML web pages, even contract clauses in PDFs… Forcibly stuffing these contents of different formats into a knowledge base, the result is that the intelligent customer service identifies errors and cannot be understood! A company that makes SaaS software has suffered this loss, and its knowledge base is mixed with documents in various formats. When a user asks “how to export a report”, the customer service engine is stunned by the long speech in Word and the screenshot of the steps in Excel, and the answer given is either incomplete or completely off.

Our way to break the game:

1) Establish an ETL “translation center”: In the face of format chaos, we must use the classic weapon in the field of data engineering – ETL (extract-transform-load).

Extract: Use tools (such as Apache Nifi, Talend, or Python’s pandas+ various Parser libraries) to extract raw data from different sources (databases, APIs, file systems, web pages).

Transform: This step is the core link, the conversion of the extracted raw data, which can be understood as “translating” all the fished out “raw materials” into “standard Mandarin” that can be understood by the knowledge base. This includes:

• Structured: Convert unstructured/semi-structured text (e.g., PDF paragraphs, Word sections) into structured data (usually JSON or XML). For example, extract the “description of characteristics” in the product manual into one field and the “technical specifications” into a list of other fields.
• The format is unified: The date is unified into ISO format, the number is removed from the deciliterate, and the unit is standardized (e.g., “GB” and “G” are unified into “GB”).
• Key information extraction: Identify and extract the core entities (product name, operation steps, parameter values) in the document.

Load: Load cleaned, structured, and standardized data into knowledge database storage (database, search engine, vector database, etc.) in categories.

This process can sort out the messy information flow into a clear, unified, and machine-friendly information flow. The choice of tools depends on the team’s technology stack, whether it is an open source solution (Airflow + self-developed scripts) or a commercial ETL tool (such as Informatica, Fivetran).

2) Text preprocessingAfter ETL conversion, the text content to be used for Q&A (FAQ answers, product description text) must also go through a strict text preprocessing pipeline before storage:

• Clean up dirt: Kill garbled characters, special symbols (Martian text, emoji, etc. useless symbol data?) depending on the situation), HTML tags.
• Case is the same: uniformly converted to lowercase (avoid “APP” and “app” being treated as two words), unless proper nouns can be treated as special treatment.
• Tokenization: Use reliable word segmentation tools (Jieba, HanLP, LTP) to cut sentences into meaningful tokens. For example, “Apple mobile phone” cannot be cut into “apple” + “fruit” + “mobile phone”.
• Stop Words Removal: Kill those high-frequency but low-informative words (“of”, “done”, “is”, “in”, “how”). But be careful! In Q&A, words like “how” and “why” may imply the type of question, sometimes reserved or treated specially.
• Stemming/Lemmatization: English processing tool. Restore “running”, “ran”, and “runs” to the root word “run”. Improve the recall rate of subsequent matches.

This step is the basic guarantee for the subsequent semantic understanding engine (NLP model) to be “readable and distinguished”. Imagine that a bunch of raw materials with different shapes are cleaned, cut, and standardized and polished into “parts” with unified specifications, and the subsequent “assembly” (retrieval, matching) can be efficient and accurate.

3. Knowledge entry and management

The data is washed, and the next step is to load “knowledge” into it. In this step, precision and organization are the core lifeblood.

1. Answer accuracy

The greatest value of a knowledge base is providing accurate answers. A wrong piece of information can cause users to work in vain, and at worst, it can cause complaints and even legal risks, which is a fatal blow to the reputation of the enterprise.

For example, in the knowledge base of a financial institution, the expected rate of return information of a wealth management product was not updated in a timely manner. Users make investment decisions based on this “outdated” information with anticipation, and the actual returns are far lower than expected, and angry complaints follow, so the trust that has been built will collapse in an instant. The reason for this is either that the entry personnel do not understand the complex business thoroughly, or the information update mechanism is paralyzed.

Build a line of defense for accuracy:

1) Two-person review: business expert + customer service perspective: We have enforced the “two-person review system”. Any new knowledge or important update must be reviewed by at least two pairs of eyes:

• Business expert: Responsible for ensuring that the business content of knowledge points is absolutely accurate and complies with the latest product rules, policies and regulations. This is a professional gatekeeping.
• Senior customer service: From the perspective of user understanding and experience, is the audit answer clear, unambiguous, and easy to understand? Is the wording too professional and obscure? Is the process description logically smooth? This is the gatekeeper of availability.

2) Regular sampling inspection: The knowledge base is by no means a “hammer deal”, and it is only a random inspection on a regular basis to ensure that the knowledge will not become obsolete.

• rules: Randomly select no less than 10% of the knowledge items every month (or according to the frequency of business changes) for manual review. The proportion of sampling in high-risk areas (such as prices, policies, regulations, and key operations) can be higher.
• execute: Performed by a QA or knowledge base operations specialist independent of the entry/review team.

3) Identify problems: Fix it now! But more importantly, it is to trace back to the source: is it a mistake when entering? Negligence during audit? Or is it lagging behind when information is transmitted from the business department? Or is there a loophole in the process itself?

4) Continuous improvement: After finding the root cause, targeted improvement: strengthen training? Optimize the synchronization process? Upgrade the audit tool? This is equivalent to giving the knowledge base regular physical examinations to ensure its continued health.

2. The knowledge system is chaotic

As the business grows, knowledge entries explode. Without scientific management, the knowledge base becomes a giant warehouse full of clutter. The user wanted to find “XX model mobile phone after-sales repair point query”, but the intelligent customer service returned a bunch of “mobile phone new product launch news”, “old mobile phone promotion policy”, “mobile phone charger purchase link”… Users are instantly confused and can only reluctantly turn to manual customer service or give up directly. In this case, users are looking for an accurate answer like looking for a needle in a haystack, which is extremely inefficient.

Build a clear “knowledge map”:

1) Dendritic classification:The core of solving the confusion is to establish a clear and logical knowledge classification system. Adopting a tree-like structure (taxonomy) is the best option:

• First-level category: Divided by large business areas. For example, e-commerce platforms: product information, orders and payments, logistics and distribution, after-sales service, accounts and security.
• Second-level category: Breakdown by product line/issue type under level one. For example, under product information: household appliances, digital 3C, beauty and personal care, fresh food; Under after-sales service: return and exchange, maintenance, complaint suggestions.

More granular: If necessary, it can continue to be subdivided (level 3 and level 4). For example, under digital 3C: mobile phones, laptops, smart wearables; There can even be a breakdown by brand under the phone.

Key principles: Clear hierarchy (generally no more than 4 levels), logical self-consistency, consistent naming, and avoidance of overlap. This structure requires a joint effort between business experts, customer service representatives, and product managers, and is regularly reviewed and adjusted as the business evolves.

2) Labeling system: Tree classification alone is not flexible enough, and it is also necessary to tag each piece of knowledge with rich tags. These tags are multidimensional and can be understood as “quick search buttons”:

• Product dimension: Product model, SKU, version number.
• Problem dimension: Core keywords (e.g., “return”, “password reset”, “installation failure”), problem scenarios (e.g., “new user”, “post-payment”).
• Strategy/policy dimensions: Policy type (“7 days without reason”, “30 days price guarantee”), applicable area (“Chinese mainland”, “Hong Kong, Macao and Taiwan”, “overseas”), urgency (“high”, “medium”, “low”).

Content type: Is it “How-to”, “Policy Terms”, “Trouble Codes” or “Video Tutorials”?

Case: A piece of knowledge about “iPhone 15 Pro Screen Warranty Policy (Chinese mainland only)”, it may be labeled as: iPhone, iPhone15Pro, screen, warranty policy, after-sales service, Apple, Chinese mainland, policy terms.

Even if the user’s question is wild and does not follow your preset classification path (such as directly asking “Is the iPhone screen broken under warranty?”). A powerful labeling system can also be like a sensitive radar, quickly capturing relevant dimensions and accurately associating this knowledge. The labeling system is like installing countless flexible “quick search buttons” for each knowledge item, which greatly improves recall and flexibility. Note that managing tags needs to be standardized (avoid the proliferation of synonyms such as “mobile phone”/”mobile phone”), and you can use tag cloud tools to assist in management.

4. Knowledge base search and matching

With the knowledge organized, how can users find it quickly and accurately when asking questions? This tests the ability to retrieve and match.

1. Upgrade keyword search

Many knowledge bases rely on simple keyword matching in the early stages. The user asked, “How can I return the clothes when I buy too much?” There is only a document titled “Product Return Process” in the knowledge base. The user didn’t mention the word “process”? Sorry, I can’t find it! This mechanical matching method seems inadequate in the face of the user’s natural and changeable expression, and it is also the main source of most users’ complaints about “answering questions”. To this end, it must be solved in combination with the power of semantic understanding:

NLP vs Vectorization:To bridge the keyword divide and understand “intent” rather than “keyword”, it is necessary to introduce semantic retrieval techniques, with natural language processing (NLP) at its core. Its essence lies in:

• Embedding: Using powerful pre-trained models (such as BERT, SBERT, RoBERTa, etc., based on Transformer architecture), every piece of text (title, body, label) in the user’s natural language question AND knowledge base is transformed into a numerical vector (Vector Embedding) in a high-dimensional space. This vector magically contains the deep semantic information of the text. Sentences with similar meanings will have very close vectors in high-dimensional space.
• Similarity Search: When a user asks a question, the system converts their question into a vector, and then calculates the similarity of this vector to all knowledge text vectors in the knowledge base (Cosine Similarity). Find the Top N answers that are semantically closest (vectors closest).

effect: Even if the user asks “Can I return the clothes when they are big?” “What should I do if the size of the clothes I buy is not suitable?” The model can also understand that its core intent is highly related to the “product return process”, so as to accurately recall the most relevant answer documents. It jumps out of the literal shackles and grabs the “soul” of the problem.

Technical selection: There are many mature solutions on the market:

• Search engine enhancements: Elasticsearch+ NLP plugins (such as ELK’s Elastic Learned Sparse Encoder).
• Dedicated vector databases: Milvus, Pinecone, Weaviate, Qdrant+ pre-trained embedding models (OpenAI text-embedding, hugging face sentence transformers).
• Cloud services: Major cloud platforms (AWS Kendra, Azure Cognitive Search, GCP Vertex AI Matching Engine) also provide managed solutions.

Which path you choose depends on your team’s technical strength, data size, budget, and latency requirements. This step of upgrade is a key leap in the evolution of intelligent customer service from a “literacy machine” to an “understanding assistant”.

2. Filter and sort the search results

Finally, a batch of relevant answers were recalled with semantic retrieval, and if the ranking was messed up, users would have to “pan for gold” in a bunch of results, and the experience would still be bad. Common pain points:

• An old question answer that is popular but may be outdated always dominates the list.
• The answers to new, more urgent questions are drowned out after pages.
• The user was obviously asking about product A, but the popular answer of product B was ranked first because of the high number of historical clicks.
• A long and obscure official document is ranked in front of a concise and clear solution idea.

To solve the above pain points, you can choose to create an intelligent sorting model:

Multi-factor fusion sorting:Solving the sorting problem requires building a comprehensive sorting model that considers multiple factors, not just similarity:

1) Semantic similarity (core weight): This is the basis to ensure that the content of the recall is truly relevant. The weight is usually the highest.

2) Answer authority/credibility: Sources matter! Answers reviewed by domain experts, officially published, or from authoritative knowledge sources should have higher weight. The weight of answers entered by ordinary customer service or user contributions (to be marked) is secondary. You can set a credibility level for different sources.

3) Timeliness: For strong time-sensitive knowledge such as policies, prices, event rules, software release notes, etc., newly created or updated answers should receive significant points. Never let outdated information mislead users! You can set a time decay function.

4) User behavior data: The value of user behavior “voting” data is huge!

• Click-through rate (CTR): Which answer are users more likely to click? Explain that the title and summary are engaging and relevant.
• Resolution rate/satisfaction feedback: When the user clicks, is it marked as “resolved”? Or take the initiative to give positive feedback? This directly illustrates the validity of the answer.
• Length of stay: Do users spend significantly longer reading an answer than others? It may indicate that the content is detailed or valuable (but it may also be incomprehensible…) other signals are required).

Answers that are clicked by users and have a lot of satisfied feedback after solving indicate their effectiveness and popularity, and should rank high.

5) Answer quality: Text length (too short may not have enough information, too long may be redundant), readability score (Flesch-Kincaid, etc.), whether it contains structured information (steps, tables), whether there are attachments (figures, videos), etc. can also be used as factors.

6) Contextual Information (Advanced): If system capabilities allow, it can be combined:

• User identity (new/veteran/VIP): New users may need more basic onboarding.
• Current Session Context: What was asked before? Is the current question a continuation of it?
• Geolocation: Provide information that aligns with local policies or services.
• Device type: Mobile may require a more concise answer.

Combining the above factors, more refined personalized sorting can be achieved:

• Rule weighting: Relatively simple, set a fixed weight for each factor and calculate the composite score. For example: Total score = 0.6 * similarity + 0.2 * authority + 0.15 * freshness + 0.05 * click-through rate. Manual reference adjustment is required.
• Machine Learning Ordering (LTR): Better solution. Collect a large amount of data <query, document, relevance label>, and use algorithms such as LambdaMART to train the model and automatically learn the optimal combination of weights for each feature. It works better but requires data accumulation and ML engineering capabilities.

By carefully designing the fusion of these factors, the model can prioritize the most relevant, authoritative, fresh, and likely to be recognized by users to the user’s eyes.

5. Dynamically optimize the knowledge base

Knowledge bases are by no means a one-hammer deal. The market is changing, products are iterating, and user needs are evolving, and the knowledge base must continue to evolve to maintain its vitality.

1. Fix update lag

Information obsolescence is an old problem of knowledge bases, and the lag in knowledge base updates often stems from the broken information synchronization chain or lack of automation means.

Establish an agile update response network:

1) Open up information connections: The knowledge base team must establish strong connections with business units such as product, operations, and marketing. The business party is required to synchronize the update information to the knowledge base management team as soon as there is a change in rules, policies, and product functions (preferably before the change goes live). Dedicated communication groups can be established, collaboration tools can be used, and even integrated into product launch processes.

2) Automated monitoring and updates: For information with strong external dependence (such as industry policies, regulations, and competing product dynamics), deploy automated monitoring tools:

• Write a crawler program and regularly scan relevant government websites, official websites of industry associations, and important news sources.
• Set keywords (e.g., key regulatory names related to your business, industry terms).
• Once the target information update is detected, an alarm is automatically triggered to notify the person in charge of the knowledge base, and even the draft of key change points can be automatically extracted, greatly reducing the response time. Keep your knowledge base on a keen sense of change.

2. Integrate user feedback

Without user feedback, optimization is behind closed doors. When users encounter intelligent customer service that cannot answer or cannot answer well, if they can only leave silently or turn to labor, the enterprise will miss valuable opportunities for improvement, and the shortcomings of the knowledge base can never be made up, and a smooth feedback closed-loop channel must be built.

1) Lower the feedback threshold: Set a prominent “Feedback” button in a prominent position in the Intelligent Contact Conversation interface, usually below each answer or before the end of the session. The copy should be friendly and direct, such as “Does this answer solve your problem?” Provides simple options (e.g., resolved/unresolved) and an optional detailed comment box. The key to its design is to make feedback feel less cumbersome and more valuable.

2) Feedback content is structured: Provide optional references to guide users to provide valuable feedback:

• What are the specific problems encountered? (Unclear?) Wrong answer? Lack? ）
• Where are you not satisfied with the answer given by intelligent customer service?
• What is the answer to what users expect?

3) Feedback analysis drives optimization: Establish a feedback data analysis process:

Real-time/periodic aggregate analysis: Identify high-frequency feedback points and common pain points (which questions are always answered incorrectly?) What questions can’t be answered? Which answers are unclear? ）。

Triggers optimization actions：

• For incorrect/outdated answers: Fix updates immediately and retroactively review process vulnerabilities.
• For missing answers: Evaluate whether it is a high-frequency question, and if so, organize business experts to supplement knowledge.
• For unclear expressions: Optimized the intelligibility of answers by senior customer service.
• For retrieval failures: Check whether keywords, tags, and semantic models need to be adjusted.

Incentivize user engagement: Give small rewards (points, coupons, lucky draw opportunities) to users who provide effective feedback, forming a positive cycle of feedback behavior.

6. Case: Intelligent customer service optimization of online education platform

I was deeply involved in the intelligent customer service optimization project of a large online education platform. They have a huge number of courses (programming, languages, vocational skills, etc.) and millions of users. The initial knowledge base construction faces severe challenges:

1. Knowledge management is chaotic: Course documents, FAQs, and policy statements are mixed, and there is a lack of effective classification and labeling.
2. The search accuracy is extremely low: A user asked, “Is there a discount for an introductory Python course?” “, returns a bunch of advanced Java course materials or expired event announcements.
3. Updates lag badly: After the new course is launched and the old course is upgraded, the content of the knowledge base cannot keep up.
4. outcome: A large number of simple questions flock to manual customer service, and the customer service team is miserable.

The solutions to the above problems are as follows:

1) Build a refined labeling system

We worked closely with the curriculum operation and teaching and research teams to jointly design a multi-dimensional labeling system:

• Curriculum dimensions: Course Type (Programming/Language/Design…), Course Name (Python Basics/IELTS Sprint…), Course Level (Beginner/Intermediate/Advanced).
• Problem dimension: Question type (course content/registration payment/learning tools/certificate query), core keywords (discount/refund/installation/exam).
• Operational dimensions: Activity type (limited time discount/join/scholarship).

achievement: Organize manpower to carry out a thorough labeling transformation of historical knowledge documents. For example, the knowledge of “Python introductory course December registration is 80% off” is labeled: programming, Python, introduction, registration process, promotion, December and other tags. Knowledge instantly becomes “located”.

2) Semantic retrieval engine upgrade

• Abandon the old keyword matching and deploy a semantic retrieval model based on the Transformer architecture (such as Sentence-BERT).
• Convert both user questions and labeled knowledge text into vectors to calculate semantic similarity.
• Combined with user profile optimization: The system will recognize the user’s learning preferences (such as the user’s history of consulting Python questions), and when the user asks the question again, the model will prioritize the answer ranking of Python-related courses, even if the question is vague.

3) User feedback closed-loop

• After each round of answers in the customer service dialog window, clearly place the “Is it helpful?” “Feedback button.
• After clicking “No”, users can further select the reason (wrong/irrelevant/incomprehensible/missing) and fill in specific comments.

Establish a real-time monitoring dashboard: The operation team can see high-frequency feedback points in real time. For example, the system automatically warns of the negative feedback of consultations related to “Python 3.11 New Features Explanation”, which is found to be that the knowledge has not been updated after the course has been upgraded. Then the process will be automatically triggered: notify the person in charge of Python course teaching and research to update the knowledge content → submit it for review → go online quickly. At the same time, small points coins will be given to users who give positive feedback.

A year after the implementation of this combo, the results are exciting:

1. The transfer rate of manual customer service dropped by 30%: Frees up a lot of manpower, allowing customer service teams to focus more on complex, high-value issues.
2. User satisfaction jumped from 60% to 80%: User experience has been significantly improved, and platform reputation and user stickiness have been improved simultaneously.

This case vividly proves that a reasonable labeling system, strong semantic retrieval, and effective user feedback can truly give the knowledge base vitality and evolutionary ability.

7. The evolution direction of the knowledge base project

1. Multimodal knowledge integrationFor example, product demonstration videos, maintenance site diagrams, operation GIFs, sales explanation audio and video, etc. are included in the knowledge system; The CLIP model can be used to realize cross-modal retrieval of images and texts.
2. Active knowledge push: Based on the user’s behavior trajectory to predict the demand, push the “Tax Refund Material List You May Need” before consultation to prevent problems before they occur.
3. Trusted knowledge verification: Introduce blockchain certificate storage technology to trace the source of knowledge in high-risk fields such as finance and medical care to improve the credibility of answers.

8. Conclusion: Building an intelligent customer service knowledge base is by no means a one-day task

An excellent intelligent customer service knowledge base is essentially a digital mirror of business logic. Building and maintaining an excellent intelligent customer service knowledge base is not a one-day task, but requires continuous injection of three living waters: keen perception of business changes, humble listening to user feedback, and rational use of technical tools. When the knowledge base has the ability to evolve itself, intelligent customer service can truly cross the gap from “customer service” to “intelligence”.