High-Volume Data Processing for a Vehicle Valuation Platform

THE CLIENT

A Leading Vehicle Valuation Platform in the UK

Our client operates one of the UK's most recognized online vehicle valuation platforms, processing thousands of vehicle enquiries daily across a nationwide network of collection centers and partner dealerships. Their business model depends on providing instant, accurate valuations to vehicle owners looking to sell quickly, competing in a fast-paced market where speed and pricing precision directly impact conversion rates and customer satisfaction.

PROJECT REQUIREMENTS

High-volume Data Processing within Tight SLAs

Their internal team was stretched thin, managing over 100,000 monthly vehicle valuation enquiries while balancing customer service, logistics coordination, and vehicle inspections. Ensuring data accuracy, regulatory compliance, and rapid turnaround times was becoming increasingly challenging for them, and so, they decided to outsource data processing (standardization, validation, and data cleansing services) to SunTec India.

Here’s the scope of the project:

Data Quality: Validate vehicle owner details, mileage, and vehicle registration information, remove duplicate entries, and fix postcode formats.
Speed: Deliver valuation-ready outputs within 24 hours, enabling sellers to make informed decisions quickly.
Accuracy: Prevent pricing errors by cross-checking data sources, validating VRM/mileage, and sending outliers for further review on a per-vehicle basis.
Traceability: Maintain end-to-end audit trails linking every enquiry ID to its valuation, timestamp, and processing notes.

OUR SOLUTION

End-to-End Data Processing with Integrated QC

We deployed a dedicated 15-person data processing team operating as an extension of the client's team. Our role was to transform raw enquiry data into clean, actionable vehicle valuations at scale.

1

Data Standardization, Cleansing, & Validation

We received daily data batches containing vehicle registration marks (VRM), customer details, mileage readings, and location data, ranging from 500 to 5,000 records per batch from multiple acquisition channels (web forms, phone enquiries, third-party lead sources). Our team -

Standardized UK postcode formats (e.g., "SW1A1AA" → "SW1A 1AA") and flagged invalid postcodes (non-existent areas, discontinued codes).
Removed duplicate entries across batches using multi-criteria data matching (VRM + email, VRM + phone number, email + postcode combinations).
Flagged invalid VRMs based on DVLA (Driver and Vehicle Licensing Agency, UK) format rules (e.g., year identifiers, prohibited combinations).
Validated email syntax (presence of @, valid domain extensions, no spaces or special characters) and identified .
Cross-checked mileage against vehicle age, flagging suspiciously low readings (e.g., 5,000 miles on a 10-year-old vehicle) or impossibly high figures to detect outliers.

2

Querying for Real-time Valuation

Our team manually queried each vehicle through the client's proprietary valuation platform—the same interface used by their internal staff—to retrieve accurate, real-time market pricing.

Entered VRM, mileage, and postcode into valuation form.
Selected vehicle condition tier (based on client guidelines), and retrieved the valuation figure displayed by the system.
Logged each query in a shared tracking spreadsheet (timestamp, VRM, mileage entered, postcode entered, valuation amount returned, etc.)
Flagged issues, like "VRM not found," "System error," "Manual review required."
Ambiguous cases (e.g., modified vehicles, import models) were sent to the client's internal team.

3

Multi-point QC within Workflow

Quality checks were embedded at every processing stage to prevent errors from compounding and eliminate end-of-day bottlenecks. Errors got caught immediately when they happened, enabling our team to maintain the 24-hour SLA.

We ran automated macros (Excel formulas/scripts that automatically double-check the cleaned data) to ensure accurate data formatting.
Each team member cross-checked their entries against source data before clicking "submit" in the client’s valuation platform.
Every 10th record processed by one team member was reviewed by an adjacent team member in real-time (so rotating review assignments prevented blind spots).
The supervisor reviewed complete exception reports, metadata completeness, and CRM import format before handover.
Errors caught at any stage triggered immediate correction and same-day retraining, preventing pattern repetition.

4

Data Delivery, Formatted as per Client Preferences

We returned production-ready, standardized and normalized datasets that could integrate seamlessly into the client's CRM, analytics workflows, and operational systems, eliminating manual rework. We delivered:

Spreadsheets with side-by-side comparison displaying original enquiry fields alongside returned valuation data.
Detailed match logs linking enquiry IDs to valuations for the client’s QA teams and auditors, with complete metadata (processor initials, query timestamps, system response codes).
Timestamps proving compliance with 24-hour SLA.
A separate exception report with a list of unmatched VRMs (expired registrations, typos, foreign plates) and recommended actions (e.g., "Contact customer for verification").
Excel format with standardized data types and header structure, ensuring error-free import into the client’s CRM.

EXTENDED PROJECT SCOPE

Data Enrichment Services for Lead Lists

After three months of successful operations and accurate, timely outcomes on the baseline requirements, the client expanded our scope to include data enrichment services for seller lead lists. This enabled them to prioritize outreach and allocate sales resources based on data rather than guesswork.

Here’s how we helped:

Contact Verification: Validated email addresses and phone numbers, removing invalid or disconnected contacts to improve outreach success rates.
Seller Intent Scoring: Assigned 0–100 intent scores derived from response velocity, repeat enquiry patterns, and vehicle model liquidity to identify hot leads.
Vehicle Intelligence: Performed data appending for MOT status, advisory count, age/mileage band, and derivative liquidity tier to support accurate pricing and inventory decisions.
Availability Intelligence: Inferred preferred contact times and earliest inspection/collection slots from seller inputs, enabling the client team to schedule outreach strategically.
Logistics Profiling: Calculated distance to nearest hub, assigned postcode cluster IDs, and assessed collection route fit for operational planning.

All enriched data was delivered in standardized Excel format with field mapping documentation for error-free Salesforce data entry, eliminating the need for manual data formatting by the client's CRM team.

Project Outcomes

Enabled Next-Day Engagement with Sellers

All data batches processed and delivered within 24 hours of receipt.

Consistent Pricing Integrity

Minimum 99.98% data accuracy maintained throughout data processing.

Peak Throughput without Backlog

Handled up to 5,000 records per day accurately, without any backlogs.

Exactly what we asked for- fast turnarounds, precise data. They took the noise out of our process. Our advisors could focus on conversions instead of fixing spreadsheets. Very helpful!

- Operations Director

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Don’t Let Data Operations Slow You Down

Get a dedicated data team that works like your own, handling data cleansing, data validation, data enrichment, custom list building, web research, or any other backend data support you need. From automotive to finance, from traditional data processing services to data support for SaaS products- whatever your business model may be, we deliver precise data services with scalable capacities that keep your operations running smoothly, even during peak volumes.

How We Became the Data Engine behind a Leading Automotive Valuation Firm