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The Get Price Suggestions node retrieves intelligent pricing recommendations from Google Merchant Center, including suggested prices, effectiveness ratings, and predicted performance changes. Perfect for data-driven pricing optimization. This is an AI-powered node that can understand natural language instructions.
When to Use It
- Get AI-powered pricing recommendations
- Optimize product pricing for better performance
- Understand predicted impact of price changes
- Validate pricing decisions with Google’s data
- Improve click-through rates and conversions
- Balance competitiveness with profitability
- Automate pricing optimization workflows
| Field | Type | Required | Description |
|---|
| Account | Dropdown | Yes | Google Merchant Center account to query |
| Fields | Multi-select | Yes | Suggestion fields to retrieve |
| Filters | Filter Builder | No | Conditions to filter products |
| Limit | Number | No | Maximum number of products to return |
Available Suggestion Fields
| Field | Description | Use Case |
|---|
| Product ID | Unique product identifier | Product identification |
| Title | Product title | Product recognition |
| Current Price | Your current product price | Baseline comparison |
| Suggested Price | AI-recommended price | Optimization target |
| Effectiveness | Suggestion effectiveness rating | Impact assessment |
| Predicted Change | Expected performance impact | ROI prediction |
| Price Type | Type of price suggestion | Strategy understanding |
| Currency | Price currency | International optimization |
| Country | Target market | Geographic optimization |
| Last Updated | When suggestion was generated | Data freshness |
Effectiveness Ratings
| Rating | Description | Recommendation |
|---|
| High | Strong positive impact expected | Implement suggestion |
| Medium | Moderate positive impact | Consider implementing |
| Low | Small positive impact | Evaluate carefully |
| Neutral | Minimal impact expected | Optional implementation |
Price Suggestion Types
| Type | Description | Use Case |
|---|
| Competitive | Match market competition | Maintain market position |
| Performance | Optimize for conversions | Improve sales performance |
| Margin | Balance profit and volume | Profit optimization |
| Promotional | Short-term price reduction | Sales boost campaigns |
Output
Returns AI-powered pricing suggestions:
{
"price_suggestions": [
{
"product_id": "online:en:US:12345",
"title": "Apple iPhone 15 Pro 128GB - Natural Titanium",
"current_price": {
"value": "999.00",
"currency": "USD"
},
"suggested_price": {
"value": "949.00",
"currency": "USD"
},
"price_change": {
"value": "-50.00",
"percentage": -5.01
},
"effectiveness": "high",
"price_type": "performance",
"predicted_changes": {
"clicks_change": "+25%",
"impressions_change": "+15%",
"conversion_rate_change": "+8%",
"revenue_impact": "+18%"
},
"confidence_score": 0.87,
"country": "US",
"last_updated": "2024-03-15T10:30:00Z"
},
{
"product_id": "online:en:US:67890",
"title": "Samsung Galaxy S24 256GB - Phantom Black",
"current_price": {
"value": "899.00",
"currency": "USD"
},
"suggested_price": {
"value": "829.00",
"currency": "USD"
},
"price_change": {
"value": "-70.00",
"percentage": -7.79
},
"effectiveness": "medium",
"price_type": "competitive",
"predicted_changes": {
"clicks_change": "+35%",
"impressions_change": "+20%",
"conversion_rate_change": "+12%",
"revenue_impact": "+15%"
},
"confidence_score": 0.72,
"country": "US",
"last_updated": "2024-03-15T09:45:00Z"
}
],
"summary": {
"total_suggestions": 2,
"high_effectiveness": 1,
"medium_effectiveness": 1,
"low_effectiveness": 0,
"average_price_reduction": -6.40,
"average_predicted_revenue_impact": "+16.5%"
},
"account_info": {
"account_id": "123456789",
"account_name": "Main Store GMC"
}
}
Suggestion Fields:
| Field | Description |
|---|
| suggested_price | AI-recommended optimal price |
| price_change | Difference from current price |
| effectiveness | Expected impact rating |
| price_type | Strategy behind suggestion |
| predicted_changes | Expected performance improvements |
| confidence_score | AI confidence in suggestion (0-1) |
Predicted Impact Metrics:
| Metric | Description |
|---|
| clicks_change | Expected change in product clicks |
| impressions_change | Expected change in visibility |
| conversion_rate_change | Expected change in conversion rate |
| revenue_impact | Expected overall revenue impact |
Credit Cost
Common Workflows
Price Optimization Campaign:
[Select Accounts] → [Get Price Suggestions] → [Filter High Effectiveness] → [Implement Changes] → [Monitor Results]
[Get Price Suggestions] → [Select Test Products] → [Split Test Prices] → [Measure Performance] → [Scale Winners]
[Get Price Benchmarks] → [Get Price Suggestions] → [Compare Strategies] → [Execute Response] → [Track Impact]
[Get Price Suggestions] → [Filter by Effectiveness] → [Auto-implement High Confidence] → [Report Changes]
Suggestion Analysis
High Effectiveness Suggestions
Priority implementation candidates:
Fields: Product ID, Suggested Price, Predicted Changes
Filter: Effectiveness = "high"
Sort: Confidence Score (descending)
Action: Implement immediately
Revenue Impact Analysis
Focus on revenue-positive suggestions:
Fields: Current Price, Suggested Price, Revenue Impact
Filter: Revenue Impact > 0
Sort: Revenue Impact (descending)
Analysis: Prioritize by revenue potential
Margin Impact Assessment
Balance optimization with profitability:
Fields: Price Change, Predicted Changes, Product Margin
Analysis: Calculate net profit impact
Decision: Implement if net profit increases
Fields: Category, Effectiveness, Predicted Changes
Group By: Category
Analysis: Category-level optimization opportunities
Strategy: Category-specific pricing rules
Implementation Strategies
Conservative Approach
Minimize risk while optimizing:
- Implement only high effectiveness suggestions
- Start with small price changes
- Test on low-risk products first
- Monitor closely and adjust quickly
Aggressive Optimization
Maximize performance improvements:
- Implement high and medium effectiveness suggestions
- Accept larger price changes for better results
- Focus on revenue impact over margin protection
- Scale successful changes quickly
Balanced Strategy
Optimize performance while managing risk:
- Prioritize high effectiveness suggestions
- Consider medium effectiveness for key products
- Balance revenue impact with margin requirements
- Implement in phases with monitoring
Data-Driven Testing
Use systematic testing approach:
- A/B test suggestions vs current prices
- Measure actual vs predicted performance
- Build confidence in suggestion accuracy
- Scale based on proven results
Use Cases
Improve product performance metrics:
- Focus on suggestions with high click/conversion predictions
- Implement price reductions for better competitiveness
- Monitor CTR and conversion rate improvements
- Scale successful optimizations
Revenue Maximization
Optimize for total revenue growth:
- Prioritize suggestions with positive revenue impact
- Balance price reductions with volume increases
- Test revenue predictions with actual results
- Adjust strategy based on performance
Competitive Positioning
Maintain competitive market position:
- Implement competitive-type suggestions
- Match or beat competitor pricing
- Monitor market position changes
- Respond to competitive moves
Margin Optimization
Balance profitability with performance:
- Calculate net profit impact of suggestions
- Implement suggestions that improve total profit
- Consider long-term customer value
- Monitor margin impact closely
Best Practices
Implementation Guidelines:
- Start with high confidence suggestions (confidence > 0.8)
- Test on representative products before broad implementation
- Monitor results closely for the first 2-4 weeks
- Be prepared to revert if results don’t match predictions
Risk Management:
- Avoid large price increases unless strongly justified
- Consider seasonal factors in pricing decisions
- Monitor competitor reactions to price changes
- Maintain minimum margin requirements
- Compare actual vs predicted results to validate suggestions
- Track key metrics (clicks, conversions, revenue) closely
- Document successful strategies for future use
- Adjust implementation based on learnings
Tips
Suggestion Evaluation:
- Prioritize high effectiveness suggestions for implementation
- Consider confidence scores when making decisions
- Evaluate predicted revenue impact against margin requirements
- Test suggestions systematically rather than implementing all at once
Implementation Strategy:
- Start with low-risk products to test suggestion accuracy
- Implement gradually to monitor impact and adjust
- Consider market conditions when timing price changes
- Document results to improve future decision-making
Performance Monitoring:
- Track actual vs predicted performance to validate suggestions
- Monitor competitive responses to your price changes
- Adjust quickly if results don’t meet expectations
- Scale successful strategies to similar products
Integration Approach:
- Combine with price benchmarks for comprehensive pricing strategy
- Use with performance reports to validate optimization impact
- Connect to pricing systems for efficient implementation
- Feed results back to improve future suggestions
