Criminal lawyer David Sans · PPC + GEO

The legal sector is one of the most competitive in digital marketing. Fierce competition, high cost per click and ad saturation make standing out on Google Ads a real challenge, especially in big cities like Barcelona. This case study shows how a criminal lawyer managed to multiply the number of calls from potential clients, significantly reducing cost per conversion in a sector where CPC can reach €10.

The challenge

The client was already investing in Google Ads campaigns before our intervention. However, results were not sustainable due to high cost per click and per conversion. At some points, clicks were costing between €7 and €10, which translated into an unsustainable cost per call in the medium term.

The main goal was clear: get direct calls from qualified clients, as each confirmed call represents a big business opportunity for a criminal lawyer. The key was adjusting strategy to stay competitive in such a saturated market without acquisition cost going through the roof.

The strategy

The first action was to rethink bidding and ad position. Traditionally, the client insisted on always appearing in the first position, which unnecessarily increased cost. We showed that in this sector, it's often more profitable to appear in second or third sponsored position, keeping visibility while reducing cost per click. Then:

• Keywords were optimised, prioritising high-intent terms related to criminal offences, setting aside generic and costly keywords.

• Conversion values were adjusted in Google Ads, giving more weight to confirmed calls from the ad.

• Detailed conversion tracking was implemented, distinguishing between real calls and unqualified clicks.

• A progressive scaling plan was structured, investing more only when cost per conversion stayed within established targets.

The results

The impact was notable within a few months:

• Total investment: €11,000.

• Conversions: 553 confirmed calls.

• Cost per conversion reduction: -€5 vs previous history.

• Average CPC reduction: -€0.50, a major accumulated saving in a sector where every cent counts.

Beyond the metrics, what really mattered was that the client got high-quality calls, from users who immediately needed criminal defence services. This translated into a direct increase in cases handled and higher profitability for the firm.

GEO case · AI visibility baseline

Elevam carried out an initial visibility measurement in generative AI environments to evaluate the entity's presence in answers related to its specialisation area and geographical context. The evaluation was done using a proprietary methodology for comparative analysis across different conversational models, using multiple query scenarios, independent sessions and manual qualitative review of results.

Initial baseline result: the initial measurement reflected practically zero visibility in generative AI.

At that time, the entity did not appear stably as a direct recommendation in relevant professional-intent queries. Its presence, when existing, was residual, inconsistent and without real capture capacity. The measurement assessed, among other factors:

• Presence of the entity in recommendation answers.

• Frequency of appearance vs competitors.

• Mention stability across different models.

• Level of prominence within the response.

• Context in which the appearance happened.

• Entity's ability to be recognised as a legitimate option in its category.

The initial diagnosis was clear: the entity existed digitally, but was not structured to be strongly interpreted by generative AI models. There was a site, there was information, but there wasn't a clear, citable and consistent enough architecture to turn that presence into a recommendation.

Interventions executed by Elevam

From the baseline, Elevam deployed a GEO intervention focused on improving the entity's interpretability, citability and authority within the ecosystem AI models use to build answers. Work streams executed:

• Digital entity restructuring: redefined content architecture to reinforce the relationship between name, specialties, geographical context and topic positioning.

• Semantic and structural optimisation: reorganised key assets to make entity reading clearer for retrieval, extraction and synthesis systems.

• Content rewriting with extractable logic: more declarative language, less ambiguity, higher density of recognisable information and better semantic organisation.

• Citable content generation: pieces aimed at reinforcing context, specialty and legitimacy, with a format designed to facilitate extraction and reuse by AI models.

• Reinforcement of external authority signals: coherence between own presence and external references to increase the probability of positive association between entity, category and expertise.

• Technical optimisation of the digital environment: adjustments in performance, indexability, semantic hierarchy and internal structure.

• Monitoring and evolutionary analysis: periodic tracking to detect behaviour changes in models and adjust strategy.

GEO project evolution

The project advanced in phases, from an initial situation without relevant visibility to recurring presence in answers linked to the entity's specialty:

• Initial phase: absence of consistent visibility in AI models.

• Structuring phase: first reinforcement of internal signals (architecture, topic clarity, semantic hierarchy, specialisation).

• Expansion phase: increase in interpretable and citable assets, together with consolidation of external signals.

• Consolidation phase: progressive and then recurring appearance in recommendation answers, with improved stability and comparative presence.

Post-intervention result

After executing the project, Elevam observed a clear improvement in the entity's visibility within AI-generated answers on relevant business queries. The entity went from a practical invisibility to a recurring presence in a significant percentage of analysed scenarios. Also, that visibility wasn't just nominal: in a relevant part of cases, the entity started to appear as a priority recommendation or as one of the highlighted options. What changed:

• Increased mention frequency.

• Higher stability across models.

• More frequent appearance in highlighted positions.

• Growth in comparative presence vs competitors.

• Better association between name, specialty and location.

• Increased capture potential from AI environments.