Get Price Suggestions

Retrieve AI-powered price suggestions and effectiveness ratings from Google Merchant Center

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.

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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

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Inputs

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

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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

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Credit Cost

• Cost per run: 1 credit

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Common Workflows

Price Optimization Campaign:

[Select Accounts] → [Get Price Suggestions] → [Filter High Effectiveness] → [Implement Changes] → [Monitor Results]

A/B Price Testing:

[Get Price Suggestions] → [Select Test Products] → [Split Test Prices] → [Measure Performance] → [Scale Winners]

Competitive Response:

[Get Price Benchmarks] → [Get Price Suggestions] → [Compare Strategies] → [Execute Response] → [Track Impact]

Automated Optimization:

[Get Price Suggestions] → [Filter by Effectiveness] → [Auto-implement High Confidence] → [Report Changes]

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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

Category Performance Optimization

Optimize by product category:

Fields: Category, Effectiveness, Predicted Changes
Group By: Category
Analysis: Category-level optimization opportunities
Strategy: Category-specific pricing rules

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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

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Use Cases

Performance Optimization

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

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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

Performance Tracking:

• 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

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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