Challenge

Geomotiv brought strong delivery experience in complex AdTech platforms, analytics, and custom solutions for marketplaces. They still lacked a repeatable enterprise go-to-market motion built specifically for Retail Media.

Before the project, marketing followed a classic lead-gen model. There were no strict ICP boundaries, little role-based personalization, and no structured TOFU–MOFU–BOFU journey. That made it difficult to warm up enterprise buying committees and read signals by stage, especially in Retail Media, where decision-making is distributed across CEOs, CMOs, CTOs, and product leadership.

At the same time, the market was crowded with well-known ready-to-use platforms (Criteo, Mirakl Ads, CitrusAd, and others). Generic messaging pushed the conversation into feature comparisons, which is a tough fight when established vendors already set buyer expectations.

So the hypothesis was specific: if Geomotiv packaged its value as a persona-based ABM narrative and ran it through a TOFU–MOFU–BOFU funnel, the team could validate demand, map real decision dynamics, and build a scalable enterprise pipeline motion.

Approach

We treated ABM as a learning system rather than a targeting setting. The goal was to define the right accounts and decision roles, translate Geomotiv’s offer into persona-based value narratives, and run a TOFU–MOFU–BOFU program that generates clear signals about relevance and readiness—so the team could iterate the GTM motion with confidence before scaling.

ICP and account selection

Geomotiv led the ICP work using internal delivery insights and focused the experiment on large retailers and retail media platforms with $1B+ revenue and mature digital infrastructure. The selection logic prioritized accounts with the ability to invest in platform-level initiatives and clear monetization potential, so the test stayed focused on realistic enterprise buyers.

Buyer personas and decision logic

We structured the strategy around three core decision roles and treated them as different entry points into the funnel. For CEOs, the narrative centered on revenue diversification and data monetization. For CMOs, it focused on ROI and media efficiency. For CTOs, it addressed integration complexity, scalability, and privacy requirements. This role-based framing prevented the campaign from collapsing into one generic message that fits no one.

GTM messaging built as value threads

Instead of listing features, we packaged Geomotiv’s offer as a set of value threads that could be unfolded differently depending on persona and funnel stage. The spine of the narrative focused on custom Retail Media platform development as an alternative to off-the-shelf solutions, supported by data analytics and AI to improve monetization, personalization, and decision-making. Supporting directions like omnichannel integration, consent management and cookieless readiness, and personalization and loyalty systems were positioned as add-on modules. We brought them in only when the role or funnel stage called for them.

Content architecture: TOFU → MOFU → BOFU

The content plan was built as a staged demand engine. TOFU assets were educational and designed to capture fast resonance signals around retail media trends, monetization models, and ecosystem logic. MOFU content went deeper through expert narratives and technical explainers that reduced perceived risk and supported internal evaluation. BOFU assets like checklists, focused landing pages, instant forms, and consultation offers were used selectively. We introduced them mainly after engagement signals appeared, so the next step felt earned, not forced.

Channel strategy: LinkedIn as ABM core, Meta as support

LinkedIn served as the core ABM environment because it allowed direct mapping to accounts and roles. We ran campaigns based on the ABM account list and, in parallel, tested Look-Alike audiences on LinkedIn as a faster feedback loop for messaging and formats. Meta played a supporting role: scalable top-of-funnel testing with Advantage Audiences, plus retargeting sequences that let us re-engage people who had already interacted with TOFU or MOFU content.

Funnel sequencing and retargeting logic

BOFU content was not treated as the default conversion layer. We introduced it mainly through retargeting once we saw engagement signals. This kept the funnel coherent and reduced friction for colder accounts. It also made results easier to read. We could separate early interest from real readiness and use BOFU only when it made sense, instead of pushing it broadly from day one.

Zones of responsibility

Geomotiv owned the domain inputs. They defined the ICP and selection criteria, validated persona assumptions, approved key messaging directions, and reviewed lead quality and qualitative feedback.

Dzeya owned the execution system. We turned those inputs into a go-to-market strategy, built the TOFU, MOFU, and BOFU content framework, produced the visual and written campaign assets, ran LinkedIn and Meta campaigns, and used campaign signals to shape optimization, retargeting, and budget recommendations.

Collaboration ran through shared documents and team chats to keep iteration fast and feedback loops short.

Results

We evaluated the campaign through funnel-stage signals: what showed relevance, what showed early readiness, and what still needed warming, instead of pushing everything toward immediate conversions.

Expanded audiences (Look-Alike on LinkedIn and Advantage on Meta) produced the highest volume of interactions and the first leads, which made them especially useful for fast validation of themes, messages, and formats. The ABM account list reacted more cautiously. We didn’t treat it as underperformance. We saw it as a sign of early-stage interest, which fits how enterprise accounts act when they’re still exploring the category and aligning internally.

One of the most practical insights was that BOFU content could trigger interest even among colder audiences, but conversion became more reliable when audiences were prepared through an educational sequence. This reinforced the strategy of sequencing BOFU through retargeting and treating TOFU and MOFU as conversion infrastructure rather than “branding content.”

After the test, Geomotiv split the budget by funnel stage, about 40% TOFU, 30% MOFU, and 30–40% BOFU then expanded retargeting and added softer touchpoints through content and direct outreach.

Next Steps

The next iteration is a short BOFU sprint delivered primarily through retargeting to users who already engaged with TOFU/MOFU assets. The goal is to turn proven interest into a clear next step by using instant forms and tightly matched offers, instead of blasting conversion assets to cold accounts.

In parallel, ABM-list accounts should continue to be warmed through lower-friction touches: educational content sequences, email nurturing, and role-based LinkedIn outreach. Over time, this creates a cleaner enterprise motion: TOFU and MOFU build awareness and alignment, retargeting shows who’s engaged, and BOFU plus personal outreach turns interest into meetings and pipeline.