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1 //! Regional Price Optimization
2 //!
3 //! Geographic pricing optimization based on local market conditions
4
5 use serde::{Deserialize, Serialize};
6 use std::collections::HashMap;
7 use tokio::time::{Duration, Instant};
8
9 #[derive(Debug, Clone, Serialize, Deserialize)]
10 pub struct RegionalMarket {
11 pub region_id: String,
12 pub region_name: String,
13 pub market_conditions: MarketConditions,
14 pub price_adjustments: PriceAdjustment,
15 pub economic_factors: EconomicFactors,
16 pub infrastructure_costs: InfrastructureCosts,
17 pub competitive_landscape: CompetitiveLandscape,
18 pub demand_patterns: DemandPatterns,
19 pub supply_characteristics: SupplyCharacteristics,
20 pub regulatory_environment: RegulatoryEnvironment,
21 }
22
23 #[derive(Debug, Clone, Serialize, Deserialize)]
24 pub struct MarketConditions {
25 pub market_maturity: MarketMaturity,
26 pub competition_level: CompetitionLevel,
27 pub customer_segments: Vec<CustomerSegment>,
28 pub growth_rate: f64, // Annual growth rate
29 pub market_volatility: f64, // 0.0 = stable, 1.0 = highly volatile
30 pub seasonal_patterns: SeasonalityData,
31 pub economic_stability: f64, // 0.0 = unstable, 1.0 = very stable
32 }
33
34 #[derive(Debug, Clone, Serialize, Deserialize)]
35 pub enum MarketMaturity {
36 Emerging, // New market, high growth potential
37 Developing, // Growing market, increasing adoption
38 Mature, // Established market, stable demand
39 Saturated, // Highly competitive, price-sensitive
40 }
41
42 #[derive(Debug, Clone, Serialize, Deserialize)]
43 pub enum CompetitionLevel {
44 Monopolistic, // Dominant position, premium pricing
45 Oligopolistic, // Few competitors, coordinated pricing
46 Competitive, // Many competitors, market-driven pricing
47 PerfectCompetition, // Commodity pricing, minimal margins
48 }
49
50 #[derive(Debug, Clone, Serialize, Deserialize)]
51 pub struct CustomerSegment {
52 pub segment_id: String,
53 pub segment_name: String,
54 pub price_sensitivity: f64, // 0.0 = price insensitive, 1.0 = highly sensitive
55 pub quality_preference: f64, // 0.0 = cost-focused, 1.0 = quality-focused
56 pub adoption_rate: f64,
57 pub average_spend: f64,
58 pub growth_potential: f64,
59 }
60
61 #[derive(Debug, Clone, Serialize, Deserialize)]
62 pub struct PriceAdjustment {
63 pub base_multiplier: f64,
64 pub demand_adjustment: f64,
65 pub competition_adjustment: f64,
66 pub cost_adjustment: f64,
67 pub regulatory_adjustment: f64,
68 pub currency_adjustment: f64,
69 pub final_multiplier: f64,
70 pub confidence_score: f64,
71 pub last_updated: Instant,
72 }
73
74 #[derive(Debug, Clone, Serialize, Deserialize)]
75 pub struct EconomicFactors {
76 pub gdp_per_capita: f64,
77 pub purchasing_power_parity: f64,
78 pub inflation_rate: f64,
79 pub currency_stability: f64,
80 pub internet_penetration: f64,
81 pub digital_adoption_index: f64,
82 pub business_environment_rank: u16,
83 pub technology_readiness: f64,
84 }
85
86 #[derive(Debug, Clone, Serialize, Deserialize)]
87 pub struct InfrastructureCosts {
88 pub datacenter_costs: DatacenterCosts,
89 pub network_costs: NetworkCosts,
90 pub energy_costs: EnergyCosts,
91 pub labor_costs: LaborCosts,
92 pub regulatory_costs: RegulatoryCosts,
93 pub total_cost_index: f64, // Relative to global average (1.0)
94 }
95
96 #[derive(Debug, Clone, Serialize, Deserialize)]
97 pub struct DatacenterCosts {
98 pub real_estate_cost_per_sqm: f64,
99 pub construction_cost_multiplier: f64,
100 pub equipment_import_duties: f64,
101 pub maintenance_cost_multiplier: f64,
102 }
103
104 #[derive(Debug, Clone, Serialize, Deserialize)]
105 pub struct NetworkCosts {
106 pub fiber_deployment_cost: f64,
107 pub international_bandwidth_cost: f64,
108 pub local_peering_costs: f64,
109 pub routing_equipment_costs: f64,
110 }
111
112 #[derive(Debug, Clone, Serialize, Deserialize)]
113 pub struct EnergyCosts {
114 pub electricity_cost_per_kwh: f64,
115 pub renewable_energy_availability: f64,
116 pub grid_stability_score: f64,
117 pub carbon_tax_rate: f64,
118 }
119
120 #[derive(Debug, Clone, Serialize, Deserialize)]
121 pub struct LaborCosts {
122 pub average_tech_salary: f64,
123 pub benefits_multiplier: f64,
124 pub training_costs: f64,
125 pub turnover_rate: f64,
126 }
127
128 #[derive(Debug, Clone, Serialize, Deserialize)]
129 pub struct RegulatoryCosts {
130 pub compliance_costs: f64,
131 pub licensing_fees: f64,
132 pub audit_costs: f64,
133 pub data_protection_costs: f64,
134 }
135
136 #[derive(Debug, Clone, Serialize, Deserialize)]
137 pub struct CompetitiveLandscape {
138 pub major_competitors: Vec<CompetitorAnalysis>,
139 pub market_share_distribution: HashMap<String, f64>,
140 pub pricing_strategies: HashMap<String, PricingStrategy>,
141 pub competitive_advantages: Vec<CompetitiveAdvantage>,
142 pub market_differentiation: MarketDifferentiation,
143 }
144
145 #[derive(Debug, Clone, Serialize, Deserialize)]
146 pub struct CompetitorAnalysis {
147 pub company_name: String,
148 pub market_share: f64,
149 pub pricing_model: PricingModel,
150 pub service_quality: f64,
151 pub strengths: Vec<String>,
152 pub weaknesses: Vec<String>,
153 pub pricing_aggressiveness: f64, // 0.0 = conservative, 1.0 = aggressive
154 }
155
156 #[derive(Debug, Clone, Serialize, Deserialize)]
157 pub enum PricingModel {
158 PremiumPricing, // High price, high quality
159 ValuePricing, // Balanced price/quality
160 EconomyPricing, // Low price, basic features
161 DynamicPricing, // Variable pricing based on demand
162 FreeBasicPaid, // Freemium model
163 }
164
165 #[derive(Debug, Clone, Serialize, Deserialize)]
166 pub enum PricingStrategy {
167 PenetrationPricing, // Low prices to gain market share
168 SkimmingPricing, // High initial prices, lower over time
169 CompetitivePricing, // Match competitor prices
170 ValueBasedPricing, // Price based on perceived value
171 CostPlusPricing, // Cost plus margin
172 }
173
174 #[derive(Debug, Clone, Serialize, Deserialize)]
175 pub struct CompetitiveAdvantage {
176 pub advantage_type: AdvantageType,
177 pub strength_score: f64, // 0.0 = weak, 1.0 = strong
178 pub sustainability: f64, // How long advantage can be maintained
179 pub market_impact: f64, // Impact on customer decision-making
180 }
181
182 #[derive(Debug, Clone, Serialize, Deserialize)]
183 pub enum AdvantageType {
184 TechnologySuperiority,
185 CostLeadership,
186 NetworkEffects,
187 BrandRecognition,
188 CustomerService,
189 GlobalPresence,
190 SecurityCertifications,
191 PerformanceAdvantage,
192 EcosystemIntegration,
193 }
194
195 #[derive(Debug, Clone, Serialize, Deserialize)]
196 pub struct MarketDifferentiation {
197 pub unique_value_propositions: Vec<String>,
198 pub target_customer_segments: Vec<String>,
199 pub positioning_strategy: PositioningStrategy,
200 pub brand_perception: BrandPerception,
201 }
202
203 #[derive(Debug, Clone, Serialize, Deserialize)]
204 pub enum PositioningStrategy {
205 PremiumProvider, // High-end, premium features
206 ValueLeader, // Best value for money
207 InnovationLeader, // Cutting-edge technology
208 ServiceExcellence, // Superior customer service
209 CostLeader, // Lowest cost provider
210 NicheSpecialist, // Focused on specific segments
211 }
212
213 #[derive(Debug, Clone, Serialize, Deserialize)]
214 pub struct BrandPerception {
215 pub reliability_score: f64,
216 pub innovation_score: f64,
217 pub customer_satisfaction: f64,
218 pub market_reputation: f64,
219 pub trust_index: f64,
220 }
221
222 #[derive(Debug, Clone, Serialize, Deserialize)]
223 pub struct DemandPatterns {
224 pub historical_demand: Vec<DemandDataPoint>,
225 pub seasonal_factors: SeasonalityData,
226 pub growth_trends: GrowthTrends,
227 pub demand_elasticity: DemandElasticity,
228 pub customer_behavior: CustomerBehavior,
229 }
230
231 #[derive(Debug, Clone, Serialize, Deserialize)]
232 pub struct DemandDataPoint {
233 pub timestamp: Instant,
234 pub demand_volume: f64,
235 pub average_price: f64,
236 pub customer_count: u32,
237 pub market_events: Vec<String>,
238 }
239
240 #[derive(Debug, Clone, Serialize, Deserialize)]
241 pub struct SeasonalityData {
242 pub monthly_factors: [f64; 12], // Multipliers for each month
243 pub weekly_factors: [f64; 7], // Multipliers for each day of week
244 pub holiday_factors: HashMap<String, f64>, // Holiday impact
245 pub business_cycle_impact: f64,
246 }
247
248 #[derive(Debug, Clone, Serialize, Deserialize)]
249 pub struct GrowthTrends {
250 pub short_term_growth: f64, // Next 3 months
251 pub medium_term_growth: f64, // Next 12 months
252 pub long_term_growth: f64, // Next 5 years
253 pub growth_drivers: Vec<String>,
254 pub growth_constraints: Vec<String>,
255 }
256
257 #[derive(Debug, Clone, Serialize, Deserialize)]
258 pub struct DemandElasticity {
259 pub price_elasticity: f64, // % demand change / % price change
260 pub income_elasticity: f64, // Response to economic changes
261 pub substitution_elasticity: f64, // Response to competitor changes
262 pub quality_elasticity: f64, // Response to service quality changes
263 }
264
265 #[derive(Debug, Clone, Serialize, Deserialize)]
266 pub struct CustomerBehavior {
267 pub switching_costs: f64, // Cost for customers to switch providers
268 pub loyalty_index: f64, // Customer retention likelihood
269 pub word_of_mouth_factor: f64, // Referral impact
270 pub decision_factors: Vec<DecisionFactor>, // What drives purchase decisions
271 }
272
273 #[derive(Debug, Clone, Serialize, Deserialize)]
274 pub struct DecisionFactor {
275 pub factor_name: String,
276 pub importance_weight: f64, // 0.0 = not important, 1.0 = very important
277 pub satisfaction_score: f64, // How well we satisfy this factor
278 }
279
280 #[derive(Debug, Clone, Serialize, Deserialize)]
281 pub struct SupplyCharacteristics {
282 pub node_density: f64, // Nodes per capita
283 pub infrastructure_quality: f64, // Quality of local infrastructure
284 pub node_reliability: f64, // Average node uptime
285 pub capacity_utilization: f64, // How much capacity is being used
286 pub expansion_potential: f64, // Potential for network growth
287 pub technical_expertise: f64, // Local technical skill availability
288 }
289
290 #[derive(Debug, Clone, Serialize, Deserialize)]
291 pub struct RegulatoryEnvironment {
292 pub data_sovereignty_requirements: Vec<String>,
293 pub privacy_regulations: Vec<String>,
294 pub content_restrictions: Vec<String>,
295 pub tax_implications: TaxStructure,
296 pub compliance_complexity: f64, // 0.0 = simple, 1.0 = very complex
297 pub regulatory_risk: f64, // Risk of regulatory changes
298 }
299
300 #[derive(Debug, Clone, Serialize, Deserialize)]
301 pub struct TaxStructure {
302 pub corporate_tax_rate: f64,
303 pub digital_services_tax: f64,
304 pub vat_gst_rate: f64,
305 pub withholding_tax_rate: f64,
306 pub tax_incentives: Vec<String>,
307 }
308
309 pub struct RegionalPriceOptimizer {
310 regional_markets: HashMap<String, RegionalMarket>,
311 global_baseline: GlobalBaseline,
312 optimization_algorithms: OptimizationAlgorithms,
313 price_history: HashMap<String, Vec<PriceUpdate>>,
314 market_intelligence: MarketIntelligence,
315 }
316
317 #[derive(Debug, Clone)]
318 struct GlobalBaseline {
319 base_storage_price: f64,
320 base_bandwidth_price: f64,
321 base_compute_price: f64,
322 global_average_costs: f64,
323 reference_currency: String,
324 }
325
326 struct OptimizationAlgorithms {
327 demand_based_optimizer: DemandOptimizer,
328 competition_based_optimizer: CompetitionOptimizer,
329 cost_based_optimizer: CostOptimizer,
330 value_based_optimizer: ValueOptimizer,
331 }
332
333 struct DemandOptimizer {
334 elasticity_models: HashMap<String, ElasticityModel>,
335 demand_forecasts: HashMap<String, DemandForecast>,
336 }
337
338 struct CompetitionOptimizer {
339 competitor_monitoring: CompetitorMonitoring,
340 pricing_game_models: HashMap<String, GameTheoryModel>,
341 }
342
343 struct CostOptimizer {
344 cost_models: HashMap<String, CostModel>,
345 efficiency_targets: HashMap<String, f64>,
346 }
347
348 struct ValueOptimizer {
349 value_perception_models: HashMap<String, ValueModel>,
350 willingness_to_pay_curves: HashMap<String, WillingnessToPayCurve>,
351 }
352
353 #[derive(Debug, Clone)]
354 struct PriceUpdate {
355 timestamp: Instant,
356 old_price: f64,
357 new_price: f64,
358 reason: String,
359 impact_assessment: PriceImpactAssessment,
360 }
361
362 #[derive(Debug, Clone)]
363 struct PriceImpactAssessment {
364 expected_demand_change: f64,
365 expected_revenue_change: f64,
366 competitor_response_likelihood: f64,
367 customer_satisfaction_impact: f64,
368 }
369
370 struct MarketIntelligence {
371 data_sources: Vec<DataSource>,
372 intelligence_reports: HashMap<String, IntelligenceReport>,
373 trend_analysis: TrendAnalysisEngine,
374 }
375
376 #[derive(Debug, Clone)]
377 struct DataSource {
378 source_id: String,
379 source_type: DataSourceType,
380 reliability_score: f64,
381 update_frequency: Duration,
382 }
383
384 #[derive(Debug, Clone)]
385 enum DataSourceType {
386 CompetitorPricing,
387 EconomicIndicators,
388 CustomerSurveys,
389 UsageAnalytics,
390 MarketResearch,
391 RegulatoryUpdates,
392 }
393
394 #[derive(Debug, Clone)]
395 struct IntelligenceReport {
396 report_id: String,
397 region: String,
398 key_insights: Vec<String>,
399 recommendations: Vec<String>,
400 confidence_level: f64,
401 valid_until: Instant,
402 }
403
404 struct TrendAnalysisEngine {
405 trend_models: HashMap<String, TrendModel>,
406 prediction_accuracy: HashMap<String, f64>,
407 }
408
409 // Placeholder structures for complex models
410 #[derive(Debug, Clone)]
411 struct ElasticityModel { coefficients: Vec<f64> }
412
413 #[derive(Debug, Clone)]
414 struct DemandForecast {
415 predictions: Vec<f64>,
416 confidence_intervals: Vec<(f64, f64)>,
417 }
418
419 #[derive(Debug, Clone)]
420 struct CompetitorMonitoring {
421 tracked_competitors: Vec<String>,
422 price_alerts: Vec<PriceAlert>,
423 }
424
425 #[derive(Debug, Clone)]
426 struct PriceAlert {
427 competitor: String,
428 price_change: f64,
429 timestamp: Instant,
430 }
431
432 #[derive(Debug, Clone)]
433 struct GameTheoryModel { payoff_matrix: Vec<Vec<f64>> }
434
435 #[derive(Debug, Clone)]
436 struct CostModel {
437 fixed_costs: f64,
438 variable_costs: f64,
439 economies_of_scale: f64,
440 }
441
442 #[derive(Debug, Clone)]
443 struct ValueModel {
444 value_attributes: HashMap<String, f64>,
445 attribute_weights: HashMap<String, f64>,
446 }
447
448 #[derive(Debug, Clone)]
449 struct WillingnessToPayCurve {
450 price_points: Vec<f64>,
451 demand_probabilities: Vec<f64>,
452 }
453
454 #[derive(Debug, Clone)]
455 struct TrendModel {
456 trend_type: TrendType,
457 parameters: Vec<f64>,
458 accuracy_score: f64,
459 }
460
461 #[derive(Debug, Clone)]
462 enum TrendType {
463 Linear,
464 Exponential,
465 Seasonal,
466 Cyclical,
467 MachineLearning,
468 }
469
470 impl RegionalPriceOptimizer {
471 pub fn new() -> Self {
472 Self {
473 regional_markets: Self::initialize_regional_markets(),
474 global_baseline: GlobalBaseline::default(),
475 optimization_algorithms: OptimizationAlgorithms::new(),
476 price_history: HashMap::new(),
477 market_intelligence: MarketIntelligence::new(),
478 }
479 }
480
481 pub async fn optimize_regional_pricing(&mut self) -> Result<HashMap<String, PriceAdjustment>, Box<dyn std::error::Error>> {
482 let mut optimized_prices = HashMap::new();
483
484 for (region_id, market) in &mut self.regional_markets {
485 let price_adjustment = self.calculate_optimal_pricing(region_id, market).await?;
486
487 // Apply the price adjustment
488 market.price_adjustments = price_adjustment.clone();
489
490 // Record the price update
491 self.record_price_update(region_id, &price_adjustment).await;
492
493 optimized_prices.insert(region_id.clone(), price_adjustment);
494 }
495
496 Ok(optimized_prices)
497 }
498
499 pub fn get_regional_price(&self, region_id: &str, base_price: f64) -> Option<f64> {
500 self.regional_markets.get(region_id)
501 .map(|market| base_price * market.price_adjustments.final_multiplier)
502 }
503
504 pub async fn analyze_price_sensitivity(&self, region_id: &str) -> Option<PriceSensitivityAnalysis> {
505 let market = self.regional_markets.get(region_id)?;
506
507 let customer_price_sensitivity = market.market_conditions.customer_segments.iter()
508 .map(|segment| segment.price_sensitivity * segment.average_spend)
509 .sum::<f64>() / market.market_conditions.customer_segments.len() as f64;
510
511 let competitive_pressure = match market.market_conditions.competition_level {
512 CompetitionLevel::Monopolistic => 0.1,
513 CompetitionLevel::Oligopolistic => 0.4,
514 CompetitionLevel::Competitive => 0.7,
515 CompetitionLevel::PerfectCompetition => 1.0,
516 };
517
518 let elasticity = market.demand_patterns.demand_elasticity.price_elasticity;
519
520 Some(PriceSensitivityAnalysis {
521 customer_sensitivity: customer_price_sensitivity,
522 competitive_pressure,
523 price_elasticity: elasticity,
524 optimal_price_range: self.calculate_optimal_price_range(customer_price_sensitivity, competitive_pressure),
525 recommendation: self.generate_pricing_recommendation(customer_price_sensitivity, competitive_pressure, elasticity),
526 })
527 }
528
529 pub async fn forecast_demand(&self, region_id: &str, price_change: f64) -> Option<DemandForecast> {
530 let market = self.regional_markets.get(region_id)?;
531 let elasticity = market.demand_patterns.demand_elasticity.price_elasticity;
532
533 // Simple elasticity-based demand forecasting
534 let demand_change = elasticity * price_change;
535 let current_demand = self.estimate_current_demand(region_id);
536
537 let predictions = vec![
538 current_demand * (1.0 + demand_change),
539 current_demand * (1.0 + demand_change * 0.8), // Dampened long-term effect
540 current_demand * (1.0 + demand_change * 0.6),
541 ];
542
543 let confidence_intervals = predictions.iter()
544 .map(|&pred| (pred * 0.9, pred * 1.1))
545 .collect();
546
547 Some(DemandForecast {
548 predictions,
549 confidence_intervals,
550 })
551 }
552
553 pub async fn benchmark_against_competitors(&self, region_id: &str) -> Option<CompetitiveBenchmark> {
554 let market = self.regional_markets.get(region_id)?;
555 let our_price = self.global_baseline.base_storage_price * market.price_adjustments.final_multiplier;
556
557 let competitor_prices: Vec<f64> = market.competitive_landscape.major_competitors.iter()
558 .map(|comp| self.estimate_competitor_price(&comp.company_name))
559 .collect();
560
561 if competitor_prices.is_empty() {
562 return None;
563 }
564
565 let avg_competitor_price = competitor_prices.iter().sum::<f64>() / competitor_prices.len() as f64;
566 let min_competitor_price = competitor_prices.iter().fold(f64::INFINITY, |a, &b| a.min(b));
567 let max_competitor_price = competitor_prices.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b));
568
569 let position = if our_price < min_competitor_price {
570 CompetitivePosition::PriceLeader
571 } else if our_price > max_competitor_price {
572 CompetitivePosition::Premium
573 } else if our_price < avg_competitor_price {
574 CompetitivePosition::BelowAverage
575 } else {
576 CompetitivePosition::AboveAverage
577 };
578
579 Some(CompetitiveBenchmark {
580 our_price,
581 average_competitor_price: avg_competitor_price,
582 price_range: (min_competitor_price, max_competitor_price),
583 market_position: position,
584 price_gap: our_price - avg_competitor_price,
585 recommendations: self.generate_competitive_recommendations(our_price, avg_competitor_price, &position),
586 })
587 }
588
589 async fn calculate_optimal_pricing(&mut self, region_id: &str, market: &RegionalMarket) -> Result<PriceAdjustment, Box<dyn std::error::Error>> {
590 // Demand-based adjustment
591 let demand_multiplier = self.calculate_demand_adjustment(market);
592
593 // Competition-based adjustment
594 let competition_multiplier = self.calculate_competition_adjustment(market);
595
596 // Cost-based adjustment
597 let cost_multiplier = self.calculate_cost_adjustment(market);
598
599 // Regulatory adjustment
600 let regulatory_multiplier = self.calculate_regulatory_adjustment(market);
601
602 // Currency adjustment
603 let currency_multiplier = self.calculate_currency_adjustment(market);
604
605 // Combine all adjustments
606 let final_multiplier = demand_multiplier * competition_multiplier *
607 cost_multiplier * regulatory_multiplier * currency_multiplier;
608
609 // Calculate confidence score
610 let confidence_score = self.calculate_pricing_confidence(market, final_multiplier);
611
612 Ok(PriceAdjustment {
613 base_multiplier: 1.0,
614 demand_adjustment: demand_multiplier,
615 competition_adjustment: competition_multiplier,
616 cost_adjustment: cost_multiplier,
617 regulatory_adjustment: regulatory_multiplier,
618 currency_adjustment: currency_multiplier,
619 final_multiplier,
620 confidence_score,
621 last_updated: Instant::now(),
622 })
623 }
624
625 fn calculate_demand_adjustment(&self, market: &RegionalMarket) -> f64 {
626 let growth_factor = 1.0 + (market.market_conditions.growth_rate * 0.1);
627 let maturity_factor = match market.market_conditions.market_maturity {
628 MarketMaturity::Emerging => 1.2, // Higher prices in emerging markets
629 MarketMaturity::Developing => 1.1,
630 MarketMaturity::Mature => 1.0,
631 MarketMaturity::Saturated => 0.9, // Lower prices in saturated markets
632 };
633
634 growth_factor * maturity_factor
635 }
636
637 fn calculate_competition_adjustment(&self, market: &RegionalMarket) -> f64 {
638 match market.market_conditions.competition_level {
639 CompetitionLevel::Monopolistic => 1.3, // Can charge premium
640 CompetitionLevel::Oligopolistic => 1.1, // Moderate premium
641 CompetitionLevel::Competitive => 1.0, // Market pricing
642 CompetitionLevel::PerfectCompetition => 0.9, // Discount pricing
643 }
644 }
645
646 fn calculate_cost_adjustment(&self, market: &RegionalMarket) -> f64 {
647 market.infrastructure_costs.total_cost_index
648 }
649
650 fn calculate_regulatory_adjustment(&self, market: &RegionalMarket) -> f64 {
651 let complexity_penalty = 1.0 + (market.regulatory_environment.compliance_complexity * 0.1);
652 let tax_adjustment = 1.0 + (market.regulatory_environment.tax_implications.corporate_tax_rate * 0.5);
653
654 complexity_penalty * tax_adjustment
655 }
656
657 fn calculate_currency_adjustment(&self, market: &RegionalMarket) -> f64 {
658 // Adjust for currency stability and purchasing power
659 let stability_factor = market.economic_factors.currency_stability;
660 let ppp_adjustment = market.economic_factors.purchasing_power_parity;
661
662 (stability_factor + ppp_adjustment) / 2.0
663 }
664
665 fn calculate_pricing_confidence(&self, market: &RegionalMarket, multiplier: f64) -> f64 {
666 let data_quality = market.market_conditions.economic_stability;
667 let volatility_penalty = 1.0 - market.market_conditions.market_volatility;
668 let adjustment_reasonableness = if multiplier > 0.5 && multiplier < 2.0 { 1.0 } else { 0.7 };
669
670 (data_quality + volatility_penalty + adjustment_reasonableness) / 3.0
671 }
672
673 async fn record_price_update(&mut self, region_id: &str, price_adjustment: &PriceAdjustment) {
674 let history = self.price_history.entry(region_id.to_string()).or_insert_with(Vec::new);
675
676 let old_price = history.last()
677 .map(|update| update.new_price)
678 .unwrap_or(self.global_baseline.base_storage_price);
679
680 let new_price = self.global_baseline.base_storage_price * price_adjustment.final_multiplier;
681
682 let price_update = PriceUpdate {
683 timestamp: Instant::now(),
684 old_price,
685 new_price,
686 reason: format!("Optimized pricing: demand={:.2}, competition={:.2}, cost={:.2}",
687 price_adjustment.demand_adjustment,
688 price_adjustment.competition_adjustment,
689 price_adjustment.cost_adjustment),
690 impact_assessment: PriceImpactAssessment {
691 expected_demand_change: self.estimate_demand_impact(old_price, new_price),
692 expected_revenue_change: self.estimate_revenue_impact(old_price, new_price),
693 competitor_response_likelihood: 0.7,
694 customer_satisfaction_impact: if new_price < old_price { 0.1 } else { -0.1 },
695 },
696 };
697
698 history.push(price_update);
699
700 // Keep only last 100 price updates per region
701 if history.len() > 100 {
702 history.drain(0..history.len() - 100);
703 }
704 }
705
706 fn estimate_current_demand(&self, region_id: &str) -> f64 {
707 // Placeholder implementation
708 self.regional_markets.get(region_id)
709 .and_then(|market| market.demand_patterns.historical_demand.last())
710 .map(|dp| dp.demand_volume)
711 .unwrap_or(1000.0)
712 }
713
714 fn estimate_competitor_price(&self, _competitor_name: &str) -> f64 {
715 // Placeholder implementation - would query competitor pricing APIs
716 self.global_baseline.base_storage_price * 1.1
717 }
718
719 fn calculate_optimal_price_range(&self, sensitivity: f64, pressure: f64) -> (f64, f64) {
720 let base = self.global_baseline.base_storage_price;
721 let range_factor = 0.2 * (1.0 - sensitivity) * (1.0 - pressure);
722
723 (base * (1.0 - range_factor), base * (1.0 + range_factor))
724 }
725
726 fn generate_pricing_recommendation(&self, sensitivity: f64, pressure: f64, elasticity: f64) -> PricingRecommendation {
727 if sensitivity > 0.8 && pressure > 0.7 {
728 PricingRecommendation::AggressivePricing
729 } else if sensitivity < 0.3 && elasticity < -0.5 {
730 PricingRecommendation::PremiumPricing
731 } else if pressure > 0.6 {
732 PricingRecommendation::CompetitivePricing
733 } else {
734 PricingRecommendation::ValueBasedPricing
735 }
736 }
737
738 fn generate_competitive_recommendations(&self, our_price: f64, avg_price: f64, position: &CompetitivePosition) -> Vec<String> {
739 let mut recommendations = Vec::new();
740
741 match position {
742 CompetitivePosition::PriceLeader => {
743 recommendations.push("Consider gradual price increases to capture value".to_string());
744 recommendations.push("Monitor competitor responses closely".to_string());
745 }
746 CompetitivePosition::Premium => {
747 recommendations.push("Justify premium with superior service quality".to_string());
748 recommendations.push("Consider value-added services".to_string());
749 }
750 CompetitivePosition::BelowAverage => {
751 recommendations.push("Opportunity to increase prices towards market average".to_string());
752 }
753 CompetitivePosition::AboveAverage => {
754 recommendations.push("Monitor price sensitivity closely".to_string());
755 recommendations.push("Emphasize quality and reliability".to_string());
756 }
757 }
758
759 let price_gap_pct = ((our_price - avg_price) / avg_price * 100.0).abs();
760 if price_gap_pct > 15.0 {
761 recommendations.push(format!("Significant price gap of {:.1}% - review pricing strategy", price_gap_pct));
762 }
763
764 recommendations
765 }
766
767 fn estimate_demand_impact(&self, old_price: f64, new_price: f64) -> f64 {
768 if old_price == 0.0 { return 0.0; }
769 let price_change = (new_price - old_price) / old_price;
770 -1.2 * price_change // Assume price elasticity of -1.2
771 }
772
773 fn estimate_revenue_impact(&self, old_price: f64, new_price: f64) -> f64 {
774 let price_change = (new_price - old_price) / old_price;
775 let demand_change = self.estimate_demand_impact(old_price, new_price);
776
777 // Revenue = Price × Demand
778 // Revenue change = (1 + price_change) × (1 + demand_change) - 1
779 (1.0 + price_change) * (1.0 + demand_change) - 1.0
780 }
781
782 fn initialize_regional_markets() -> HashMap<String, RegionalMarket> {
783 let mut markets = HashMap::new();
784
785 // Add major regional markets
786 markets.insert("us-east".to_string(), Self::create_us_east_market());
787 markets.insert("us-west".to_string(), Self::create_us_west_market());
788 markets.insert("europe".to_string(), Self::create_europe_market());
789 markets.insert("asia-pacific".to_string(), Self::create_asia_pacific_market());
790 markets.insert("south-america".to_string(), Self::create_south_america_market());
791 markets.insert("middle-east-africa".to_string(), Self::create_mea_market());
792
793 markets
794 }
795
796 fn create_us_east_market() -> RegionalMarket {
797 RegionalMarket {
798 region_id: "us-east".to_string(),
799 region_name: "US East Coast".to_string(),
800 market_conditions: MarketConditions {
801 market_maturity: MarketMaturity::Mature,
802 competition_level: CompetitionLevel::Competitive,
803 customer_segments: vec![
804 CustomerSegment {
805 segment_id: "enterprise".to_string(),
806 segment_name: "Enterprise".to_string(),
807 price_sensitivity: 0.3,
808 quality_preference: 0.9,
809 adoption_rate: 0.8,
810 average_spend: 5000.0,
811 growth_potential: 0.4,
812 },
813 CustomerSegment {
814 segment_id: "startup".to_string(),
815 segment_name: "Startups".to_string(),
816 price_sensitivity: 0.8,
817 quality_preference: 0.6,
818 adoption_rate: 0.9,
819 average_spend: 500.0,
820 growth_potential: 0.9,
821 },
822 ],
823 growth_rate: 0.15,
824 market_volatility: 0.2,
825 seasonal_patterns: SeasonalityData::default(),
826 economic_stability: 0.9,
827 },
828 price_adjustments: PriceAdjustment::default(),
829 economic_factors: EconomicFactors {
830 gdp_per_capita: 65000.0,
831 purchasing_power_parity: 1.0,
832 inflation_rate: 0.03,
833 currency_stability: 0.95,
834 internet_penetration: 0.95,
835 digital_adoption_index: 0.9,
836 business_environment_rank: 15,
837 technology_readiness: 0.95,
838 },
839 infrastructure_costs: InfrastructureCosts {
840 datacenter_costs: DatacenterCosts {
841 real_estate_cost_per_sqm: 500.0,
842 construction_cost_multiplier: 1.0,
843 equipment_import_duties: 0.0,
844 maintenance_cost_multiplier: 1.0,
845 },
846 network_costs: NetworkCosts {
847 fiber_deployment_cost: 50000.0,
848 international_bandwidth_cost: 1.0,
849 local_peering_costs: 100.0,
850 routing_equipment_costs: 10000.0,
851 },
852 energy_costs: EnergyCosts {
853 electricity_cost_per_kwh: 0.12,
854 renewable_energy_availability: 0.6,
855 grid_stability_score: 0.95,
856 carbon_tax_rate: 0.0,
857 },
858 labor_costs: LaborCosts {
859 average_tech_salary: 120000.0,
860 benefits_multiplier: 1.4,
861 training_costs: 10000.0,
862 turnover_rate: 0.15,
863 },
864 regulatory_costs: RegulatoryCosts {
865 compliance_costs: 50000.0,
866 licensing_fees: 10000.0,
867 audit_costs: 25000.0,
868 data_protection_costs: 20000.0,
869 },
870 total_cost_index: 1.0,
871 },
872 competitive_landscape: CompetitiveLandscape::default(),
873 demand_patterns: DemandPatterns::default(),
874 supply_characteristics: SupplyCharacteristics::default(),
875 regulatory_environment: RegulatoryEnvironment::default(),
876 }
877 }
878
879 // Simplified implementations for other regions
880 fn create_us_west_market() -> RegionalMarket {
881 let mut market = Self::create_us_east_market();
882 market.region_id = "us-west".to_string();
883 market.region_name = "US West Coast".to_string();
884 market.infrastructure_costs.total_cost_index = 1.2; // Higher costs
885 market
886 }
887
888 fn create_europe_market() -> RegionalMarket {
889 let mut market = Self::create_us_east_market();
890 market.region_id = "europe".to_string();
891 market.region_name = "Europe".to_string();
892 market.economic_factors.purchasing_power_parity = 0.85;
893 market.infrastructure_costs.total_cost_index = 1.1;
894 market.regulatory_environment.compliance_complexity = 0.8; // GDPR complexity
895 market
896 }
897
898 fn create_asia_pacific_market() -> RegionalMarket {
899 let mut market = Self::create_us_east_market();
900 market.region_id = "asia-pacific".to_string();
901 market.region_name = "Asia Pacific".to_string();
902 market.market_conditions.market_maturity = MarketMaturity::Developing;
903 market.market_conditions.growth_rate = 0.25; // Higher growth
904 market.economic_factors.purchasing_power_parity = 0.6;
905 market.infrastructure_costs.total_cost_index = 0.8; // Lower costs
906 market
907 }
908
909 fn create_south_america_market() -> RegionalMarket {
910 let mut market = Self::create_us_east_market();
911 market.region_id = "south-america".to_string();
912 market.region_name = "South America".to_string();
913 market.market_conditions.market_maturity = MarketMaturity::Emerging;
914 market.economic_factors.purchasing_power_parity = 0.5;
915 market.economic_factors.currency_stability = 0.6;
916 market.infrastructure_costs.total_cost_index = 0.7;
917 market
918 }
919
920 fn create_mea_market() -> RegionalMarket {
921 let mut market = Self::create_us_east_market();
922 market.region_id = "middle-east-africa".to_string();
923 market.region_name = "Middle East & Africa".to_string();
924 market.market_conditions.market_maturity = MarketMaturity::Emerging;
925 market.economic_factors.purchasing_power_parity = 0.4;
926 market.infrastructure_costs.total_cost_index = 0.9;
927 market.regulatory_environment.regulatory_risk = 0.7;
928 market
929 }
930 }
931
932 #[derive(Debug, Clone, Serialize, Deserialize)]
933 pub struct PriceSensitivityAnalysis {
934 pub customer_sensitivity: f64,
935 pub competitive_pressure: f64,
936 pub price_elasticity: f64,
937 pub optimal_price_range: (f64, f64),
938 pub recommendation: PricingRecommendation,
939 }
940
941 #[derive(Debug, Clone, Serialize, Deserialize)]
942 pub enum PricingRecommendation {
943 AggressivePricing, // Low prices to capture market share
944 CompetitivePricing, // Match competitor prices
945 ValueBasedPricing, // Price based on value delivered
946 PremiumPricing, // High prices for premium positioning
947 }
948
949 #[derive(Debug, Clone, Serialize, Deserialize)]
950 pub struct CompetitiveBenchmark {
951 pub our_price: f64,
952 pub average_competitor_price: f64,
953 pub price_range: (f64, f64),
954 pub market_position: CompetitivePosition,
955 pub price_gap: f64,
956 pub recommendations: Vec<String>,
957 }
958
959 #[derive(Debug, Clone, Serialize, Deserialize)]
960 pub enum CompetitivePosition {
961 PriceLeader, // Lowest price in market
962 BelowAverage, // Below average price
963 AboveAverage, // Above average price
964 Premium, // Highest price in market
965 }
966
967 #[derive(Debug, Clone, Serialize, Deserialize)]
968 pub struct GeographicPricing {
969 pub region_prices: HashMap<String, f64>,
970 pub price_rationale: HashMap<String, String>,
971 pub optimization_score: f64,
972 pub last_optimization: Instant,
973 }
974
975 impl Default for SeasonalityData {
976 fn default() -> Self {
977 Self {
978 monthly_factors: [1.0; 12],
979 weekly_factors: [1.0; 7],
980 holiday_factors: HashMap::new(),
981 business_cycle_impact: 1.0,
982 }
983 }
984 }
985
986 impl Default for PriceAdjustment {
987 fn default() -> Self {
988 Self {
989 base_multiplier: 1.0,
990 demand_adjustment: 1.0,
991 competition_adjustment: 1.0,
992 cost_adjustment: 1.0,
993 regulatory_adjustment: 1.0,
994 currency_adjustment: 1.0,
995 final_multiplier: 1.0,
996 confidence_score: 0.5,
997 last_updated: Instant::now(),
998 }
999 }
1000 }
1001
1002 impl Default for GlobalBaseline {
1003 fn default() -> Self {
1004 Self {
1005 base_storage_price: 0.001, // ZEPH per GB per hour
1006 base_bandwidth_price: 0.01, // ZEPH per Mbps per hour
1007 base_compute_price: 0.1, // ZEPH per core per hour
1008 global_average_costs: 1.0,
1009 reference_currency: "USD".to_string(),
1010 }
1011 }
1012 }
1013
1014 impl Default for CompetitiveLandscape {
1015 fn default() -> Self {
1016 Self {
1017 major_competitors: Vec::new(),
1018 market_share_distribution: HashMap::new(),
1019 pricing_strategies: HashMap::new(),
1020 competitive_advantages: Vec::new(),
1021 market_differentiation: MarketDifferentiation {
1022 unique_value_propositions: vec!["Zero-knowledge encryption".to_string()],
1023 target_customer_segments: vec!["Privacy-conscious users".to_string()],
1024 positioning_strategy: PositioningStrategy::InnovationLeader,
1025 brand_perception: BrandPerception {
1026 reliability_score: 0.8,
1027 innovation_score: 0.9,
1028 customer_satisfaction: 0.8,
1029 market_reputation: 0.7,
1030 trust_index: 0.8,
1031 },
1032 },
1033 }
1034 }
1035 }
1036
1037 impl Default for DemandPatterns {
1038 fn default() -> Self {
1039 Self {
1040 historical_demand: Vec::new(),
1041 seasonal_factors: SeasonalityData::default(),
1042 growth_trends: GrowthTrends {
1043 short_term_growth: 0.05,
1044 medium_term_growth: 0.15,
1045 long_term_growth: 0.25,
1046 growth_drivers: vec!["Digital transformation".to_string()],
1047 growth_constraints: vec!["Economic uncertainty".to_string()],
1048 },
1049 demand_elasticity: DemandElasticity {
1050 price_elasticity: -1.2,
1051 income_elasticity: 0.8,
1052 substitution_elasticity: 0.6,
1053 quality_elasticity: 0.4,
1054 },
1055 customer_behavior: CustomerBehavior {
1056 switching_costs: 0.3,
1057 loyalty_index: 0.6,
1058 word_of_mouth_factor: 0.4,
1059 decision_factors: vec![
1060 DecisionFactor {
1061 factor_name: "Price".to_string(),
1062 importance_weight: 0.4,
1063 satisfaction_score: 0.7,
1064 },
1065 DecisionFactor {
1066 factor_name: "Security".to_string(),
1067 importance_weight: 0.3,
1068 satisfaction_score: 0.9,
1069 },
1070 ],
1071 },
1072 }
1073 }
1074 }
1075
1076 impl Default for SupplyCharacteristics {
1077 fn default() -> Self {
1078 Self {
1079 node_density: 0.001,
1080 infrastructure_quality: 0.8,
1081 node_reliability: 0.95,
1082 capacity_utilization: 0.6,
1083 expansion_potential: 0.7,
1084 technical_expertise: 0.8,
1085 }
1086 }
1087 }
1088
1089 impl Default for RegulatoryEnvironment {
1090 fn default() -> Self {
1091 Self {
1092 data_sovereignty_requirements: vec!["Local data residency".to_string()],
1093 privacy_regulations: vec!["GDPR".to_string(), "CCPA".to_string()],
1094 content_restrictions: Vec::new(),
1095 tax_implications: TaxStructure {
1096 corporate_tax_rate: 0.21,
1097 digital_services_tax: 0.03,
1098 vat_gst_rate: 0.20,
1099 withholding_tax_rate: 0.0,
1100 tax_incentives: vec!["R&D credits".to_string()],
1101 },
1102 compliance_complexity: 0.5,
1103 regulatory_risk: 0.3,
1104 }
1105 }
1106 }
1107
1108 impl OptimizationAlgorithms {
1109 fn new() -> Self {
1110 Self {
1111 demand_based_optimizer: DemandOptimizer {
1112 elasticity_models: HashMap::new(),
1113 demand_forecasts: HashMap::new(),
1114 },
1115 competition_based_optimizer: CompetitionOptimizer {
1116 competitor_monitoring: CompetitorMonitoring {
1117 tracked_competitors: Vec::new(),
1118 price_alerts: Vec::new(),
1119 },
1120 pricing_game_models: HashMap::new(),
1121 },
1122 cost_based_optimizer: CostOptimizer {
1123 cost_models: HashMap::new(),
1124 efficiency_targets: HashMap::new(),
1125 },
1126 value_based_optimizer: ValueOptimizer {
1127 value_perception_models: HashMap::new(),
1128 willingness_to_pay_curves: HashMap::new(),
1129 },
1130 }
1131 }
1132 }
1133
1134 impl MarketIntelligence {
1135 fn new() -> Self {
1136 Self {
1137 data_sources: Vec::new(),
1138 intelligence_reports: HashMap::new(),
1139 trend_analysis: TrendAnalysisEngine {
1140 trend_models: HashMap::new(),
1141 prediction_accuracy: HashMap::new(),
1142 },
1143 }
1144 }
1145 }