连接与治理
接通你的系统、政策与知识，让 AI 在你的规则之内工作，而不是绕过它们。
/ Customer & Revenue /
The market moves every day. Your prices should too.
AI that monitors market signals, competitor pricing and demand patterns to recommend pricing decisions that protect margin without losing volume.
The problem
Pricing decisions made on data that is already out of date
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Pricing decisions made on stale data
Price lists are updated quarterly. Markets move daily. The gap between your prices and the right prices costs margin constantly.
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No visibility on competitor pricing at scale
Manual competitor price tracking covers a fraction of the catalogue. Most pricing blind spots are never discovered.
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Promotions that erode margin without clarity
Discounts are applied broadly without understanding elasticity. The result is margin erosion with uncertain volume gains.
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Elasticity nobody models
Price changes go live without scenario testing. Margin and volume surprises follow because impact is guessed, not simulated.
How it works
从信号到成果——治理内置
自动化与辅助
让工作流上线：分流、起草、解决或升级，全程带完整上下文与审计追溯。
度量与改进
跟踪运营 KPI、质量与风险——再和你的团队一起调优剧本。
流程序列会适配你的工具、渠道与风险态势。
The market moves every day. Your prices should too.
What's included
What you get when you run this with Thinkia
A governed layer across data, workflows, and handoffs—so teams ship safely and scale with metrics.
Competitor price monitoring
Tracks competitor pricing across products, channels and markets in real time.
Demand elasticity modelling
Estimates how price changes affect volume for each product and customer segment.
Dynamic pricing recommendations
Suggests optimal prices based on demand signals, inventory levels and competitive position.
Promotion impact analysis
Models the margin and volume impact of promotional scenarios before execution.
Market pricing alerts
Notifies when competitor prices cross defined thresholds requiring a response.
Pricing decision audit trail
Logs every price change, its trigger and its outcome for performance review.
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Results
What changes when this runs in production
Results vary by catalogue size, market competitiveness and data availability.
+3–5%
Typical uplift from data-driven pricing vs. periodic manual updates
Orientative — confirmed in discovery; depends on the starting point.
10×
More SKUs monitored vs. manual tracking
Orientative — confirmed in discovery; depends on the starting point.
+20%
Improvement in margin return on promotional spend with elasticity modelling
Orientative — confirmed in discovery; depends on the starting point.
How we work
From spreadsheet wars to governed recommendations sales can explain
Economics map
Economics map
Week 1–2
Segments, elasticity hypotheses, and guardrails (floor, parity, regulation) are documented.
Data & features
Data & features
Week 3–5
Comp sets, win/loss, and cost inputs are cleaned; simulation scenarios are agreed with finance.
Deal desk pilot
Deal desk pilot
Week 6–9
Rep guidance and approvals run in parallel; margin and win-rate are tracked vs control.
Enterprise pricing
Enterprise pricing
Week 10+
CPQ or ERP hooks, rebate logic, and audit trails roll out by region or product line.
Channel conflict and deal complexity drive rules; waves follow product families with clean data.
Go deeper
Pricing, value, and GenAI economics
Ideas, trends, and tools to stay ahead
Get started
Ready to scope this for your context?
We start with a focused session—no commitment—to map constraints and a sensible path.