Automotive analytics and BI solutions
Enhance your automotive business with our expert analytics and business intelligence solutions. Gain actionable insights, optimize operations, and drive success with our tailored data analytics services.
A data-driven GPS monitoring and routing system that helps sales teams work with higher efficiency and lower operating costs.
behaviour analysis and buying‑pattern discovery journey mapping and interaction optimisation audience segmentation for targeted marketing loyalty and retention evaluation customer lifetime value modelling
real‑time fleet and vehicle performance inventory tracking and optimisation dealership and supplier efficiency assessments diagnostics and predictive maintenance equipment utilisation and fuel‑efficiency insights quality control analysis and regulatory compliance views
Show us the sources – vehicles, customers, operations – and we will scope dashboards your teams will act on.
The same delivery discipline on every engagement – from the first map to a handover your team runs.
We start from the decisions that matter – pricing, stocking, fleet allocation, workshop loading – and trace which systems hold the numbers behind them. KPIs are baselined, and a first domain is chosen where better numbers change behaviour fastest.
A warehouse sized to real volume rather than aspiration, with pipelines written to be read, run and extended by the team that inherits them. Telemetry gets its own cleansing layer, so utilisation and efficiency metrics are computed from traces you can defend.
Dashboards ship per role and per decision, with alerts on thresholds agreed with operations. Forecasting and scoring follow once the descriptive layer is trusted in daily use, and every increment lands in production, not in a staging graveyard.
Handover is continuous, not a closing ceremony: your analysts pair with ours from the first increment, documentation grows alongside the build, and training happens on real work rather than in a classroom. From there we extend into new domains with you, or step back to support.
A mobility business produces data with every enquiry, lease application, service booking and mile driven – and most of it never reaches a decision. Managers price stock, allocate vehicles and plan workshop capacity from exported spreadsheets that were stale before they were opened. The cost is rarely the reporting effort itself; it is the decisions made late or by feel: vehicles held past their economic point, funnels leaking quietly between enquiry and contract, sites that underperform for months before anyone can prove it.
We build the analytics layer that closes this gap, across three territories: customer behaviour, vehicle utilisation and telemetry, and operational decision support. The pattern repeats across our work. When a leasing provider asked us to digitise manual workflows, the portal and dealer area we delivered did more than cut staff workload by 68% – the analytics over the new data exposed funnel performance, approval cycle times and revenue leakage that had been invisible while the same work lived in inboxes and spreadsheets.
This page covers the analytics layer; for the portals, dealer systems and integrations that generate the data, see our automotive and mobility practice.
Telemetry is the least forgiving source in the analytics stack. GPS units drop signal in depots and underground car parks, report impossible jumps after reconnecting, and duplicate events when the network returns. Diagnostic feeds differ by manufacturer, model year and firmware. A mixed fleet usually means several telematics providers, each with its own format, sampling rate and idea of what a trip is. Any utilisation or efficiency metric computed straight from the raw feed will be confidently wrong.
- De-noising and gap-filling of GPS traces before any distance or utilisation figure is computed
- Trip and stop detection tuned to the operation, not library defaults
- Entity resolution so one vehicle stays one vehicle across telematics, DMS and finance
- Aggregation that reads driver data as patterns, not surveillance
This discipline is not theoretical for us. We have put GPS, geospatial data and routing to work in production systems where the numbers drove daily operating decisions, and where a bad trace meant a wasted journey rather than a blip on a chart.
To see what that discipline delivers in the field, read the GPS monitoring solution for sales agents case – a six-month build that raised field efficiency and lowered operating costs.
Most BI initiatives fail quietly: the dashboards exist, attendance at the demo was good, and six months later everyone is back in spreadsheets. The root cause is usually the same – views were built around available data rather than around decisions. We design the other way round. Every view we ship is anchored to a decision someone makes on a known cadence: which vehicles to remarket this week, which leads to call first this morning, which sites need intervention this month. If a chart cannot change an action, it does not ship.
Decision support also means meeting people where they already work. Exceptions and threshold alerts go to the channels managers actually watch, not into yet another portal login. Thresholds are agreed with operations rather than invented by analysts, so an alert carries authority instead of noise. And every number on a managed view has an owner who can explain it – because the moment two meetings quote two different versions of the same KPI, trust collapses and the spreadsheets come back.
We pick boring, proven components and spend the innovation budget where it pays: a warehouse or lakehouse sized to your actual volume rather than your aspirational one, pipelines engineered as production software from the first commit, and the BI tool your team already licenses wherever it is good enough. Geospatial processing is treated as first-class engineering, not an afterthought – map-matching, geofencing and spatial joins sit in the pipeline, not in a notebook on an analyst's laptop.
We also do not sell a platform. Everything is a bespoke implementation inside your estate – your cloud accounts, your access model, your vendor contracts – so there is no licence dependency on us after handover. Machine learning enters the stack only where it demonstrably beats the heuristic it replaces; forecasting and scoring are earned, not assumed. On the application side the same pragmatism applies: the leasing provider's portal and dealer area were built with Java, Spring and React – mainstream stacks your own engineers can hire for and maintain.
The platform engineering underneath – warehousing, pipelines, governance – is shared with our cross-industry data analytics practice, applied here to mobility data.
Analytics engagements earn trust incrementally, so we deliberately start small. A scoping phase fixes the first increment: the priority domain, the decisions and KPIs it must serve, the sources involved and the acceptance criteria. Where the value case needs proving first, we run a proof of concept – the AI route-optimisation work a European pharma distributor describes in their review on this page went from brief to working PoC in three weeks, which is the shape we aim for when feasibility is the question.
The team is sized to the increment, not the other way round: typically an analytics lead who owns the decision map and KPI definitions, data engineers on pipelines and the telemetry layer, and UI/UX and testing for the dashboard and portal surfaces. The first increment usually runs on a fixed scope and price, because scoping has made it estimable; continuing domains run time-and-materials against a shared backlog. Either way, you see working software at every demo, and you can stop at any increment boundary with everything delivered so far in production.
We collaborated with WislaCode on a route-to-market optimisation project. Working with WislaCode was effective, transparent and predictable, which is especially critical for AI and ML projects. We provided them with six months of anonymised data, and within just three weeks, they delivered a proof of concept that already showed...
An engagement covers the full path from raw operational and vehicle data to dashboards managers trust, scoped around a priority domain first and extended from there. Each item below is a production deliverable, not a slide.
A decision and KPI audit that maps who decides what, on which numbers, and where those numbers currently come from.
Secure development and production environments provisioned under your ownership, with automated deployment so releases are repeatable rather than dependent on individual engineers.
A history layer that backfills data from legacy systems and previous telematics providers, so trend and seasonality analysis starts with depth from day one.
A telemetry processing layer covering trip detection, map-matching and utilisation metrics computed from cleaned GPS and diagnostic feeds.
Role-based dashboards for executive, site and team audiences, each anchored to the decisions that role makes on a known cadence.
Forecasting and scoring models proven against your own historical data before they influence a live decision, then rolled out gradually by site or domain.
Metric definitions, runbooks and training so your analysts extend datasets and views without a dependency on our team.
Everything runs in your estate and transfers at handover: code, infrastructure definitions, metric documentation and admin access. There is no proprietary platform sitting between you and your own data.
What outcomes can we expect from an automotive analytics initiative?
Most programmes aim to increase sales velocity, reduce overage stock and improve aftersales margins. Typical gains include better enquiry‑to‑sale conversion, tighter days‑to‑turn, higher finance and add‑on penetration, and improved technician efficiency. You should also see fewer manual reports, faster month‑end, and clearer accountability by site, team and channel. We baseline KPIs early, then deliver in short increments so value appears within weeks, not months, while maintaining data quality and governance.
How do you integrate with our existing DMS, CRM and finance systems?
We start with a connectivity audit, mapping authoritative sources for vehicles, customers, stock and transactions. Where APIs exist, we use secure, rate‑aware connectors; otherwise, we implement CDC or scheduled extracts with validation and deduplication. A canonical data model standardises entities across vendors. Lineage, access controls and error handling are built into the pipelines, so downstream dashboards and machine learning stay consistent and auditable.
Can you help us move away from spreadsheets without disrupting the business?
Yes. We catalogue critical spreadsheets, reverse‑engineer logic, then replace them with governed datasets and parameterised reports. During transition, we run both paths in parallel to reconcile results and build trust. Users keep familiar filters and views, but gain version control, refresh schedules and role‑based access. This approach reduces key‑person risk, eliminates conflicting numbers, and shortens the time from data refresh to decision.
What AI use cases deliver the fastest return in retail automotive?
Quick wins typically include lead scoring to prioritise follow‑up, demand forecasting to guide ordering and transfers, and price elasticity models for used stock. In aftersales, capacity forecasting and no‑show prediction stabilise workshop loading. We pair models with clear actions – contact cadence, pricing bands, transfer suggestions – so teams can act immediately. Each model is monitored for drift and fairness, with human override where appropriate.
How do you ensure data accuracy and trust in dashboards?
We implement validation at ingestion, schema enforcement, and business rule checks for KPIs. Data quality metrics – completeness, freshness, duplication, are tracked and surfaced in the BI layer. Every metric has documented definitions and lineage from source to visual, so finance and operations reconcile to the same number. Alerts flag anomalies or late data, preventing decisions on stale or partial information.
How quickly can we see results and what does the delivery approach look like?
We usually target a first slice of value within four to six weeks: high‑impact dashboards and a stable data pipeline for a priority domain, such as sales funnel or used‑car stock. Delivery is iterative, with fortnightly demos, shared backlogs and clear acceptance criteria. As trust builds, we add domains – aftersales, parts, finance and introduce ML where it brings tangible benefit. Knowledge transfer and documentation are continuous to embed capability in your teams.
What drives the cost of an automotive analytics engagement?
The main drivers are the number and condition of source systems, telemetry volume and latency requirements, how many business domains and dashboard audiences are in scope, and whether forecasting or scoring models are included. We do not quote from a rate card against that uncertainty: the scoping phase produces a fixed price for the first increment, and you decide on each further domain with real delivery data in hand.
Can you work with telemetry from mixed fleets and several telematics providers?
Yes – mixed fleets are the normal case, not the exception. We normalise each provider's feed, with its own format, sampling rate and trip definition, into one canonical vehicle-and-trip model, and apply cleansing before any metric is computed. Utilisation, routing and efficiency figures then stay comparable across the whole fleet regardless of which unit is fitted to which vehicle, and a provider change later does not invalidate your history.
How do you handle driver and customer privacy in vehicle data?
Location traces and driving behaviour are personal data and we treat them that way from the first pipeline. We design for minimisation: aggregated and pattern-level views by default, role-based access to anything identifying, and retention windows agreed with you up front. Dashboards answer operational questions – utilisation, routing, workload – without becoming surveillance tools, and your legal team confirms the lawful basis for each use before it ships.
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Name the numbers you cannot see today and we will plan the analytics to surface them.