Data Cleaning
Hexastruct normalizes names, emails, phone numbers, product categories, countries, duplicate records, RFQ language, urgency signals, and missing fields before scoring and routing.
Cleaning engine
The same lead should look the same no matter whether it came from a website form, email, TikTok, LinkedIn, Xiaohongshu, supplier sheet, or manual note.
Automation literacy
The page explains public data monitoring, data cleaning, scoring, routing, and human review in buyer language before it shows the technical workflow.
A phone number, email, TikTok account, RFQ note, or comment is only useful when it becomes a structured record. Data cleaning makes buyer information comparable, searchable, and safe for follow-up.
Hexastruct treats crawler work as public-signal monitoring with source logs, platform rules, rate limits, error recovery, and human review. The goal is fewer but better opportunities.
Gemini-style extraction or other AI prompts can convert unstructured page text, RFQ emails, comments, and files into product category, buyer role, pain point, region, urgency, and next action.
A B2B team cannot chase every signal. Rule-based and Bayesian-style scoring prioritize records by category fit, channel quality, recency, buyer language, product match, and negative signals.
For hardware, quotation, outreach, and supplier promises carry risk. Hexastruct automates collection, cleaning, summarizing, and routing, while keeping final judgment, quote language, and relationship building under human control.
Crawler operating rules
Hexastruct treats crawler automation as public-signal monitoring, not raw scraping volume. Every source, field, filter, and human checkpoint is designed before collection starts.
Clean company names, country names, emails, phone formats, platform handles, product tags, lead status, and duplicate records before scoring.
Without cleaning, sales teams waste time on repeated or misleading records.Rules and Bayesian-style scoring can rank fit, urgency, buyer language, source quality, negative signals, and confidence.
Only qualified signals should create alerts.A high-fit record becomes a concise alert with source link, summary, score, missing fields, owner, suggested follow-up, and next reminder.
The workflow becomes action, not just a report.Repeated buyer questions, product names, objections, and platform phrases become FAQ pages, answer-hub entries, case pages, and sales scripts.
Automation improves the website, not only the sales inbox.Data cleaning logic
A lead record is useful only when the same fields, naming rules, missing-data checks, and scoring logic are applied every time.
One phone field, normalized country code, region tag, WhatsApp availability, and missing-field flag
Sales can contact faster and segment by market.One merged lead record with source history, first-seen date, last action, and owner
The team stops repeating follow-up and sees the full relationship trail.Controlled product taxonomy with category, subcategory, application, and supply-chain route
RFQ, SEO, supplier matching, and quote preparation become consistent.Structured fields for quantity, target price, certification, material, deadline, files, and open questions
Engineers and sales can review the same brief instead of re-reading the whole inbox.Pain-point clusters, usage scenarios, negative objections, product feature clues, and topic frequency
Product planning and content pages use real customer language.Comparable supplier table with normalized specs, risk notes, capability tags, and update dates
Buyers get a stronger shortlist and faster sample-stage decisions.Versioned watch record with changed fields, screenshot or source note, and alert reason
The team notices competitor, price, feature, and market shifts earlier.Negative-signal tags, blocked patterns, low-score queue, and human override path
Alerts stay clean and the team trusts the workflow.Buyer education
Stored with source URL, timestamp, channel, extracted fields, and confidence score.
Built with tools such as Python, Playwright, Scrapling, Yingdao RPA, browser queues, and source logs.
Includes normalization, deduplication, validation, missing-field flags, taxonomy mapping, and quality scoring.
Useful for RFQs, product pages, comments, supplier notes, emails, and research documents.
Combines category fit, target market, buyer role, urgency, source reliability, negative signals, and confidence.
Keeps automation useful without letting weak or risky output reach buyers.
Can include source, summary, score, missing fields, recommended next action, and owner.
The same cleaned questions feed FAQ schema, answer hub pages, llms.txt, service pages, and sales scripts.
Create controlled fields for country, industry, buyer type, product category, subcategory, target price, quantity, certification, files, and deadline.
Merge repeated contacts across email, forms, social platforms, sheets, and manual notes while preserving source history.
Extract requirement fields from RFQ email, attachment text, product references, and buyer notes.
Feishu, Telegram, or CRM alerts should include enough context for action: source, score, summary, missing fields, and next step.
AI Automation Case Library
These workflows show how inquiries, RFQs, public signals, social comments, and supplier tables can become consistent B2B sales records.
A public-signal monitoring workflow that scans selected keywords, product categories, and buyer language before the sales team starts manual outreach.
A lead routing concept that reads public role, company, category, and inquiry wording, then sends higher-fit prospects into a sales review queue.
A TikTok and short-video monitoring playbook that turns visible product interest into a buyer-facing design and sourcing brief.
A practical inquiry workflow: capture form data, create a JS lead file, score urgency, send email attachment when SMTP is configured, and push a readable alert to Feishu.
A monitoring workflow that helps exporters understand price bands, MOQ pressure, feature claims, and positioning gaps before sending a quote.
A remote dispatch model where routine checks, lead reminders, prompt-based replies, and human review checkpoints are connected into one sales operating queue.
A Yingdao RPA workflow inspired by the app named '关键词表-热卖商品' that turns hot-product terms, platform phrases, and buyer language into a structured product-opportunity table.
A structured browser workflow based on the screenshot entries 'tiktok--比特浏览器' and '爬虫DC' for collecting public TikTok category signals and routing qualified leads to review.
A workflow based on the visible '小红书抓取 平滑滚动--评论最多 一周内' apps, designed to turn recent high-comment posts into product pain points and buyer language.
A workflow inspired by the screenshot entry 'tiktok-edge完美 2026 抓取与 触达', built to separate public-signal discovery from careful human-reviewed outreach.
A controlled workflow inspired by 'TikTok 微软版 在线私信mac' for preparing message queues, status notes, and review checkpoints across browser-based TikTok operations.
A workflow based on screenshot entries '提取GEMINI_云迁', '提取GEMINI', and '新建应用_云迁', turning messy data into structured sales and product intelligence.
A scoring playbook inspired by the screenshot entry '贝叶斯工具源码', designed to rank B2B leads by product fit, urgency, market, and evidence quality.
A category workflow based on the screenshot app '充电桩', built for EV charging hardware teams that need installer, distributor, project, and policy-related buyer signals.
A controlled crawler automation workflow that monitors public category pages, product launches, supplier listings, and buyer clues, then turns them into structured B2B lead records.
A data cleaning pipeline that turns messy inquiry text, phone numbers, emails, country names, product categories, and duplicate entries into consistent records for scoring and follow-up.
A workflow that reads long RFQ emails and attachments, extracts structured requirement fields, creates a JS lead file, and alerts the team with a reviewable summary.
A public-signal workflow that watches crowdfunding hardware categories, extracts creator needs, product promise risks, and supply-chain opportunities for China-side support.
A monitoring workflow that tracks public supplier catalog changes, normalizes specs, and alerts the team when price, MOQ, material, feature, or capability language changes.
A workflow that turns website forms, crawler signals, RFQ emails, and manual notes into qualified Feishu alerts with owner, score, missing fields, and follow-up action.
Google-ready FAQ
AI scoring is unreliable when fields are inconsistent. Clean country, category, contact, source, quantity, urgency, and duplicate status first, then score the record.
Yes. Website and email inquiries can become structured JS lead records with source page, contact fields, category, summary, score, missing fields, and recommended next action.
CRM 只负责存储和管理，如果进入 CRM 的数据本身混乱，销售还是会浪费时间。数据清洗是在进入 CRM 或飞书提醒之前，把字段、重复项、评分和下一步动作整理清楚。