Five businesses, one standard: +88% conversions by moving from volume to lead quality

Our client is a well-known automotive group in La Plata running a digital ecosystem with five distinct business units — used car sales, buying vehicles from private owners, aftersales and workshop bookings, car insurance and fleet auctions — each with its own audience, budget and commercial goal.

The engagement covered eight months of monthly reporting, from December 2025 to July 2026. The challenge was not just generating more traffic: it was coordinating five different businesses under a single measurement standard in Google Analytics 4, and migrating from "more volume" to "better lead quality" without sacrificing the commercial flow of any of them.

• We coordinated five business units with their own budget, audience and funnel, under a single GA4 measurement standard.
• Vehicle buying from private owners grew 88% in conversions; aftersales grew 81%, with cost per lead 28% lower.
• The insurance unit hit a record 32.6% conversion rate, and in-house SEO multiplied organic impressions almost 12×.

The challenge: three fronts, five businesses

The first front was simply organisational: coordinating five business units at once —each with its own funnel, its own budget and its own definition of a "lead"— all measured against a single standard. The second was moving from volume to lead quality: from March, migrating the main units to a Target CPA bidding strategy aimed at more qualified traffic, not just more traffic. The third was reducing dependency on paid media by building organic positioning on the group's main site.

• Technical SEO — Fixing meta descriptions, titles and ALT attributes; focusing on strategic keywords by business unit and geography.
• Segmentation by unit — Dedicated campaigns and budgets per business, instead of one generic account mixing different goals.
• Migration to Target CPA — From March, switching from "Maximise conversions" to Target CPA strategies in the main units, prioritising lead quality over raw volume.
• Cross-channel measurement (GA4) — Tracking conversion rate and engagement time by channel, to know not only how much traffic arrives but which traffic converts best.

Vehicle buying and aftersales: scaling without losing efficiency

The unit buying vehicles from private owners, with separate budgets for Buenos Aires and northern Argentina, went from 333 to 626 conversions between March and April — 88% more — while CPA in Buenos Aires dropped 13% (from $2,838 to $2,465, between June and July). Northern Argentina held a cost per lead consistently ~11% below Buenos Aires, the evidence that justified allocating more budget there: it now accounts for 57% of total acquisition. The unit's overall conversion rate also rose, from 5.6% to 8.8% between March and April.

The aftersales and workshop bookings unit was the group's most consistent: every documented month showed more volume and a lower cost per lead at the same time, without trading one for the other. Conversions grew 81% (from 144 to 260, March to July) while cost per conversion fell 28% (from $2,112 to $1,521). July's 28.7% conversion rate practically doubled March's 14.2%.

Insurance and auctions: quality as a result, not an excuse

The car insurance unit was the group's most efficient: it scaled spend month over month and still kept cost per lead inside the same band ($730–900 ARS, March to July) — the improvement came from traffic quality, not from spending less. In May it reached a record 32.6% conversion rate, one in three clicks converting, after climbing steadily from March's 26.8%.

The fleet and lot auctions unit was the most volatile, with the seasonality inherent to that business: between April and May, conversions grew 57% (from 242 to 381 leads), with CTR improving to 3.14% and conversion rate passing 20% for the first time (20.45%). Cost per click rose from $139.88 in March to $167.28 in April, reflecting a more competitive keyword auction. July was a low-demand month (47 leads, $5,667 CPA), consistent with the auction business's own seasonality; it is included transparently, without forcing a linear trend the data does not support.