Negotiation-Based Fare Analytics Using Scraped inDrive Data A Negotiation-Driven Mobility Pricing Case Study by Travel Scrape

Introduction

Most ride-hailing platforms rely on algorithmic pricing. Fares are calculated automatically based on distance, demand, traffic, and surge logic. While this model offers predictability, it hides an important economic signal: what price the market is actually willing to accept.

Negotiation-based ride platforms operate very differently. Instead of fixed fares, riders propose a price and drivers decide whether to accept, reject, or counter. This creates a live pricing marketplace where every trip reveals real demand elasticity.

This case study explains how Travel Scrape helped a transport research firm analyse negotiation-driven pricing behaviour by scraping structured data from inDrive. By capturing user-offered fares, accepted prices, and acceptance rates across route types, the client gained insights that are impossible to extract from fixed-fare platforms.

Business Challenge

The client was a transport research and analytics firm studying real-world price formation in urban mobility.

Their core objective was to answer a simple but powerful question:

What price actually clears the market when riders and drivers negotiate freely?

However, existing mobility datasets were not designed for this purpose.

Limitations of Traditional Fare Data

Most ride-hailing data focuses on:

• Algorithm-generated fare estimates
• Distance-based pricing
• Surge multipliers
• Final charged price only

This approach ignores the negotiation process itself, which is the most valuable signal on platforms like inDrive.

Specific Challenges Faced

1. No Visibility Into Offered vs Accepted Prices

The client needed to observe:

• What price riders initially propose
• What price drivers actually accept
• The gap between offer and acceptance

Without this, price elasticity could not be modelled accurately.

2. Lack of Route Context

Negotiation behaviour varies significantly by route type:

• Airport routes behave very differently from residential trips
• CBD congestion changes driver willingness
• Residential trips show higher price sensitivity

The client needed route-level segmentation.

3. Time-of-Day Effects Were Unclear

The firm suspected that:

• Drivers are more flexible during off-peak hours
• Negotiation success rates drop during peak congestion

But they lacked structured data to prove it.

Why Negotiation-Based Fare Analytics Matters

Negotiation platforms represent true market pricing.

Unlike fixed-fare platforms:

• Prices are not pre-optimized by algorithms
• Drivers express real willingness-to-accept
• Riders reveal real willingness-to-pay

For researchers, economists, and platform designers, this creates a rare opportunity to observe price discovery in action.

Travel Scrape proposed a data framework that captured the entire negotiation funnel, not just the final outcome.

Solution by Travel Scrape

Travel Scrape designed a negotiation-centric fare intelligence system focused on capturing every meaningful pricing signal from inDrive.

Instead of treating fares as a single value, the solution modelled negotiation as a multi-stage process.

Negotiation Data Framework

Travel Scrape structured the dataset around four core dimensions:

• User-Offered Fare
• Accepted Fare
• Response / Acceptance Count
• Route Type Classification

This framework allowed the client to analyse not just prices, but behavioural responses to pricing.

Route Classification Logic

To ensure analytical clarity, each trip was mapped to one of three route types:

• Airport routes
• Central Business District (CBD) routes
• Residential routes

This segmentation revealed how negotiation dynamics shift based on trip importance, urgency, and driver opportunity cost.

Data Collection Methodology

Travel Scrape implemented automated data extraction with strict consistency rules.

Data Capture Frequency

• Multiple data pulls per day
• Coverage across:
  • Morning peak
  • Midday
  • Evening peak
  • Late-night off-peak
• Both weekdays and weekends

Data Attributes Captured

For each negotiation instance, the following fields were collected:

• City
• Route type
• User-offered fare
• Driver-accepted fare
• Acceptance or rejection flag
• Number of driver responses
• Time of day
• Timestamp

This structure allowed deep behavioural analysis rather than surface-level pricing comparison.

Sample Negotiation Data

Below is a simplified snapshot from the dataset.

This format clearly highlights price gaps and negotiation success rates.

Analytical Models Applied

Travel Scrape designed the dataset to support advanced modelling.

Key Metrics Derived

• Offer-to-acceptance premium
• Acceptance probability by price band
• Route-wise price elasticity
• Time-of-day negotiation success
• Driver response sensitivity

Key Insights Uncovered

1. Airport Routes Required a 20–30% Premium for Acceptance

Airport trips showed the strongest negotiation resistance.

• Low acceptance at initial offer prices
• Higher driver expectations due to:
  • Longer distance
  • Toll costs
  • Opportunity cost of return trips
• Accepted prices were typically 20–30% above user offers

This confirmed that airport routes have structural price floors.

2. Residential Routes Showed High Price Sensitivity

Residential routes behaved very differently.

• Small price increases led to large acceptance jumps
• Acceptance rates often exceeded 75%
• Drivers were more flexible due to:
  • Shorter trips
  • Higher trip density
  • Lower risk

These routes were ideal for studying elastic pricing behavior.

3. Negotiation Success Varied Strongly by Time of Day

Time-of-day analysis revealed clear patterns:

• Morning peak had the lowest acceptance rates
• Midday and late night showed higher flexibility
• Evening peak showed higher accepted premiums

This allowed the client to model temporal elasticity curves.

4. Response Count Signalled Market Tightness

The number of driver responses proved to be a powerful signal.

• Fewer responses indicated supply scarcity
• Higher responses correlated with lower accepted prices
• Response count acted as a real-time liquidity indicator

This insight was not available on fixed-fare platforms.

Business Impact

The negotiation-based analytics delivered unique strategic value.

1. Enabled Advanced Price Elasticity Modelling

The client built elasticity models using:

• Offer vs acceptance curves
• Route-specific acceptance thresholds
• Time-based sensitivity adjustments

This allowed far more accurate demand modelling than fixed-fare data.

2. Delivered Insights Unavailable on Fixed-Fare Platforms

Unlike Uber or Bolt-style pricing:

• inDrive data revealed real human negotiation behaviour
• Drivers actively expressed willingness-to-accept
• Riders revealed willingness-to-pay

This made the dataset uniquely valuable for research and platform design.

3. Supported UX and Pricing Strategy Decisions

The client used the insights to advise mobility platforms on:

• Suggested price nudges
• Dynamic offer recommendations
• UX design for negotiation prompts
• Price anchoring strategies

Decisions were based on observed behaviour, not assumptions.

Why Travel Scrape

Travel Scrape specializes in advanced mobility and travel data intelligence, including non-traditional pricing models.

Key strengths:

• Negotiation-based data capture
• Behavioural pricing analytics
• Route-level segmentation
• Time-series mobility data
• Research-grade datasets

Travel Scrape enables insights that go beyond averages and algorithms.

Conclusion

Negotiation-based ride platforms expose the true mechanics of pricing. They show where demand breaks, where supply resists, and what price ultimately clears the market.

This case study demonstrates how Travel Scrape transformed scraped inDrive negotiation data into actionable fare intelligence, enabling a transport research firm to model price elasticity, understand route-level behaviour, and uncover insights hidden from fixed-fare platforms.

For organizations studying mobility economics, negotiation-based fare analytics is one of the most powerful tools available.