How Astara Poland connected marketing and sales in one automotive funnel.

Introduction

Marketing sees campaigns, clicks, and forms. Sales sees leads, contacts, offers, and orders. The board wants to know something much simpler: which actions actually sell cars?In a distributed importer ecosystem, the answer to this question is not obvious. Data comes from ad systems, the importer’s central website, dealer sites, the Lead System, call centers, Salesforce, and DMS systems. Each of these systems describes only a fragment of the customer journey. Each department can also understand terms like lead, MQL, SQL, conversion, or sale differently. As part of our collaboration with Astara Poland, we defined a single sales funnel covering the entire customer journey: from viewing an ad to selling a specific car. We then established KPIs for each phase and clearly split accountability between Marketing and Sales. This was not merely a reporting change. It was a shift in how results are managed.

The problem wasn’t a lack of data. The problem was that it wasn’t connected

Astara Poland manages an extensive ecosystem covering the Nissan, Mitsubishi Motors, and Isuzu brands. Datadise implemented a central importer website and an interconnected dealer website ecosystem for the Mitsubishi brand. This enabled common measurement standards, a unified Tracking Plan, and a consistent data structure across the entire network. However, data was still being generated in multiple places:

media systems measured reach, clicks, and campaign costs,
the importer website and dealer sites recorded user behavior,
forms generated MQLs,
the Lead System analyzed lead quality,
the call center verified reachability and customer intent,
Salesforce handled the ongoing sales process,
the Salesforce system contained information on orders, VIN numbers, and final sales.

Each system correctly described its own part of the process. Yet, the company still needed a single model showing what happens to a customer between seeing an ad and purchasing a vehicle. That is why we started not with a dashboard, but with definitions.

MQL and SQL must mean the same thing to everyone

A key element of the project was introducing shared definitions for MQL and SQL. MQL — Marketing Qualified Lead is generated when a customer submits their details in a form. They have performed a measurable action and expressed interest, but their data and intent have not yet been verified. The MQL then passes through the Lead System and the call center verification process. SQL — Sales Qualified Lead is a lead that has passed quality control, been verified for contactability and purchase intent, and subsequently transferred to Salesforce. As a result, a form submission is no longer automatically treated as a full sales opportunity. The flow is unambiguous: Form → MQL → Lead System → Call Center → SQL → Salesforce → Sales Process → Sale

One key decision: Marketing is not responsible for every form submission

Marketing is responsible for acquiring the MQL, guiding it through the quality control process, and delivering a verified SQL to Salesforce. Sales takes over responsibility from the moment the SQL appears in Salesforce and is assigned to the appropriate dealer or sales representative. The boundary is structured as follows:

Marketing: from ad impression to delivering a verified SQL to Salesforce.
Sales: from accepting the SQL in Salesforce to vehicle ordering and final sale.

This seems like a simple division. In practice, it completely changes how both teams are evaluated. Marketing is not judged solely on clicks or form volume. Sales is not held accountable for spam, duplicates, or unreachable contacts. Each team receives its own clear scope of ownership, while both operate on a single funnel.

Part one: the marketing funnel

1. Ad impression

The process begins in media systems such as Google Ads or Meta Ads. The advertisement is displayed to a target audience user. At this stage, we measure reach scale and attention acquisition costs. Key KPIs we can measure:

impressions,
reach,
ad frequency,
CPM,
impression share.

An impression alone is not proof of interest. It merely indicates whether the campaign is reaching the intended audience at the desired intensity.

2. Ad interaction

The next phase begins when a user reacts to the ad and clicks through to the importer or dealer website. Here, we verify whether the creative, offer, and targeting method generate genuine interest. Key KPIs we can measure:

clicks,
CTR,
CPC,
share of high-value visits,
traffic quality by campaign, model, brand, and dealer.

A click still does not guarantee purchase intent, but it serves as the first active signal of engagement.

3. Importer or dealer website visit

Upon entering the site, the journey is tracked using a unified Tracking Plan, dataLayer, GTM, and GA4. A user may start on the importer’s site and navigate to a selected dealer page, or land directly on a local dealer website. Connecting the entire ecosystem ensures that traffic source, campaign, model, and downstream actions are preserved regardless of where the conversion occurs. Key KPIs we can measure:

sessions,
engaged sessions,
engagement rate,
specific model page visits,
transitions between importer and dealer sites,
dealer searches,
form starts,
performance by dealer site,
share of users granting consent.

A prerequisite for reliable measurement is the proper setup of Consent Mode v2 and standardized event definitions across all sites.

4. Form submission and MQL creation

The user submits a contact form, request for proposal, test drive request, or inquiry for a specific vehicle model. At this point, an MQL is created. An MQL signifies that Marketing successfully prompted an action and acquired contact details. It does not guarantee that the lead is valid, unique, or ready for Sales. Key KPIs we can measure:

MQL volume,
form starts,
form submissions,
form completion rate,
abandonment rate,
MQL conversion rate,
cost per MQL,
MQL breakdown by brand, model, campaign, and dealer.

Along with the MQL, we capture its marketing context: campaign, medium, creative, vehicle model, landing page, and target dealer.

5. MQL analysis in the Lead System

Every MQL passes through the Lead System, which acts as the initial quality gatekeeper. The system verifies data integrity, identifies spam, and detects duplicate submissions. Invalid records are filtered out before reaching Salesforce. Key KPIs we can measure:

analyzed MQL volume,
rejected submissions,
spam rate,
duplicate rate,
share of valid data forms,
valid MQL rate,
cost per valid MQL.

Thanks to this implementation, spam and duplicate records entering the CRM were reduced by 30%. If junk leads reach Sales, the issue isn’t in Salesforce—it originated upstream and must be resolved before handoff.

6. Call center verification

MQLs passing technical checks in the Lead System are vetted by the call center. We verify contactability, contact information accuracy, model preference, and genuine buying intent. Key KPIs we can measure:

contactability rate,
contact attempt count,
time to first contact attempt,
verification rate,
share of confirmed-intent leads,
reasons for failed verification,
MQL-to-SQL conversion rate,
cost per SQL.

Only leads passing verification are converted into SQLs.

7. SQL creation and Salesforce handoff

Verified leads receive SQL status and are pushed into Salesforce along with their full marketing context. This marks the end of the marketing funnel and formal handoff of responsibility. Marketing’s ultimate performance metric shifts from click volume or form count to the volume and cost of verified SQLs successfully delivered to Salesforce. Additionally, we measure:

MQL-to-SQL conversion rate,
SQL delivery rate,
time from form submission to Salesforce handoff,
data completeness,
cross-source match rate,
cost per SQL,
SQL breakdown by brand, model, campaign, and dealer.

Standardizing identifiers and data pipelines increased data matching accuracy by 40%.

Part two: the sales funnel

8. SQL intake and assignment

Once inside Salesforce, the SQL is routed to the designated dealer or sales representative. From this moment, Sales assumes full ownership. Key KPIs we can measure:

SQLs ingested by Salesforce,
assignment speed,
correct assignment rate,
dealer response time,
SLA compliance rate,
unactioned SQL count.

The initial hours directly determine contact probability and downstream conversion. Response speed must not be hidden in aggregate CRM metrics.

9. Initial rep contact

The sales rep initiates contact with the prospect. Salesforce records response times and outcome metrics for every attempt. Key KPIs we can measure:

speed-to-lead,
contact attempt count,
contact rate,
average time to successful contact,
share of SQLs handled within SLA,
share of uncontacted SQLs.

If verified SQLs are not acted upon quickly, increasing marketing budgets won’t solve the issue—it will only generate more lost opportunities.

10. Needs discovery

Upon establishing contact, the rep clarifies buyer needs, purchase timeline, model interest, financing preference, and readiness to move forward. Key KPIs we can measure:

conducted discovery calls,
share of confirmed-need prospects,
time from SQL intake to call,
churn/drop-off reasons,
share of model-specific leads,
share of leads with defined buying timelines.

This stage evaluates sales execution on pre-verified prospects rather than form quality.

11. Showroom appointment or test drive

The buyer moves to active engagement: showroom visit, vehicle presentation, or test drive. Key KPIs we can measure:

booked appointments,
scheduled test drives,
SQL-to-appointment rate,
appointment show rate,
test-drive completion rate,
time from SQL intake to appointment,
cancelled or missed appointments.

In automotive retail, this marks the transition from intent declaration to real customer commitment.

12. Quotation / proposal

Following the visit, the prospect receives a formal offer covering vehicle pricing, financing options, insurance, add-ons, or trade-in terms. Key KPIs we can measure:

issued proposals,
appointment-to-offer rate,
average proposal creation time,
proposal value,
projected margin,
share of proposals with financing or add-ons,
proposal rejection reasons.

At this phase, volume gives way to deal quality and potential profitability.

13. Vehicle order

Offer acceptance results in a formal vehicle order or contract signing. Key KPIs we can measure:

order volume,
offer-to-order conversion rate,
SQL-to-order conversion rate,
average order value,
expected margin,
time from MQL/SQL to order,
order cancellation rate.

An order is a strong performance indicator, but does not always equal a finalized sale—hence it is tracked separately.

14. Final sale and vehicle handover in Salesforce

The internal pipeline completes upon vehicle delivery and confirmed sale logging in Salesforce. The record links directly to the vehicle VIN, dealer, and original acquisition source. Key KPIs we can measure:

sold vehicles count,
order-to-sale conversion rate,
SQL-to-Sale conversion rate,
MQL-to-Sale conversion rate,
time from MQL/SQL to sale,
revenue,
margin,
CAC,
sales and margin by campaign, model, brand, and dealer.

Here, we finally answer the core question driving the project: Which campaign didn’t just generate MQLs or SQLs, but actually sold cars?

One importer result. Visibility into every dealer’s contribution

Deploying central importer and dealer web assets enabled a unified reporting framework across the network. Using Google Data Studio, we surface both aggregated importer metrics and individual dealer performance. We can benchmark metrics including:

dealer website traffic,
MQL volume,
verified SQL volume,
MQL-to-SQL conversion rate,
dealer response times,
contact efficiency,
appointment and offer counts,
SQL-to-Sale conversion rate,
sales breakdown by model,
individual dealer contribution to network results.

The importer moves beyond viewing high-level campaign summaries. It can identify whether sales variances stem from traffic quality, MQL validity, vetting efficiency, response speed, or specific dealer execution. Dealers gain direct access to their performance data to benchmark against network standards. The goal isn’t ranking for its own sake—it’s identifying bottleneck locations and defining actionable next steps.

Three levels of accountability

Marketing efficiency

Marketing is evaluated from media spend to verified SQL handoff in Salesforce. Primary metrics include MQL cost/quality, MQL-to-SQL conversion efficiency, and data completeness.

Sales efficiency

Sales performance is measured from SQL intake in Salesforce through final delivery. Focus areas include speed-to-lead, contact rate, appointments, quotes, and SQL-to-Sale conversion.

Business-wide performance

Executive leadership receives a unified view connecting ad spend directly to MQLs, SQLs, vehicle orders, and final deliveries. Data can be sliced across the enterprise, brand, model, campaign, and dealer dimensions.

Measure–Connect–Improve in action

Measure

We first aligned on shared definitions for funnel stages, MQL, SQL, KPIs, and team ownership. We completed a tracking audit, refactored the Tracking Plan, and updated the environment for Consent Mode v2 compliance.

Connect

We connected the importer portal, dealer sites, GA4, Lead System, call center, and Salesforce into a unified data stream powering Google Cloud and BigQuery. Automated rules were set up to handle MQL quality filtering and direct SQL push into CRM.

Improve

We transformed data into interactive Google Data Studio dashboards tailored for executives, marketing managers, and dealer networks. Dashboards display the full MQL-to-SQL-to-Sale pipeline alongside dealer-level performance, pinpointing drop-off points and assigning clear operational ownership. Data process automation reached 100%, saving the marketing team over 15 hours per week previously spent on manual reporting.

From dashboards to shared business context

The primary outcome of this initiative isn’t just a Data Studio dashboard. It is a unified data and decision-making model linking campaigns, websites, MQLs, SQLs, dealer execution, and final vehicle sales. This embodies what we at Datadise call the Automotive Golden Context: a managed data layer enabling all teams to analyze performance using identical definitions. The next frontier involves feeding transaction and margin data into Value-Based Bidding models. Ad platforms can then optimize toward purchase likelihood and customer value rather than raw form submissions. Ultimately, the goal isn’t just generating more leads. It’s knowing exactly which activities drive sales, where prospects drop off, and what to optimize next.

Implementation results

+40% data matching accuracy
-30% spam and duplicate leads in CRM
100% data process automation
15+ hours saved weekly by the marketing team
Standardized MQL and SQL definitions
Single funnel tracking from impression to final sale
Performance visibility at importer and dealer levels
Clear accountability boundary between Marketing and Sales

Want to discover where you’re really losing customers between the click and the sale? Let’s start with a single funnel, shared definitions, and KPIs that align Marketing, Sales, and your dealer network. Let’s discuss your sales funnel.