B2B SaaS · Search Infrastructure · France
How Meilisearch Stopped Buying Noise and Started Building an Enterprise Pipeline
PMAX campaigns were driving self-serve signups with high churn and zero sales pipeline. We rebuilt the entire demand generation motion, separating enterprise-fit acquisition from self-serve noise, and turning ad spend into qualified enterprise meetings for the first time.
Meilisearch had product-market fit. Its ad campaigns did not.
Meilisearch is a global search and AI retrieval platform with 57,000+ GitHub stars, a self-serve cloud product, and an enterprise motion targeting engineering teams, SaaS companies, and large-scale digital platforms across the US, UK, and Europe. The product was validated. The developer community was growing. But the paid acquisition strategy wasn't keeping pace with the company's commercial ambitions.
When ads.expert came on board in December 2025, Meilisearch was running Performance Max campaigns that were generating sign-ups, but the wrong kind. The leads coming through were self-serve users with low intent and high churn rates, not the engineering leads, technical decision-makers, or enterprise buyers the sales team needed to build a pipeline. There were no meeting bookings from paid leads. There was no deal creation tied to ad spend.
Compounding the problem: the PMAX campaigns were operating with minimal audience signal and no conversion architecture tailored to Meilisearch's dual go-to-market motion. The algorithm had no way to distinguish between a developer exploring a free tier and a VP of Engineering evaluating a six-figure contract.
Restructure Meilisearch's paid acquisition to separate high-intent enterprise pipeline from self-serve volume, building a demand generation engine that produces qualified meetings, measurable pipeline, and a clear CAC-to-LTV story for the sales team and board.
Nilay set up an entire performance ads initiative for us at Meilisearch, end to end. Setting up the campaigns and running the ads was honestly just one part of what he does. Just as importantly, Nilay took full ownership of the infrastructure and reporting around the ads, including connecting our paid data to HubSpot, building dashboards that track LTV and payback over time, and benchmarking our pipeline results against industry standards to put everything into context.
I also appreciated that Nilay is just easy to work with. He shows up to meetings already a step ahead, asks good questions, and flags things before they become problems. With that level of ownership and proactivity, it genuinely felt like having a full-time in-house performance specialist.
If you need someone who can run paid acquisition and help you make sense of it with a high degree of autonomy and proactivity, I cannot recommend Nilay enough.
Four structural problems standing between ad spend and enterprise revenue
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01
PMAX campaigns optimizing for the wrong conversion signal
The existing Performance Max campaigns were driving self-serve cloud sign-ups (a valid top-of-funnel metric), but not the conversion event the sales team needed. With no separation between self-serve and enterprise conversion goals, Smart Bidding was optimizing toward low-value signups while systematically under-investing in the demo request path that actually produces revenue.
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02
No meeting bookings or deal creation from paid channels
Despite ongoing ad spend, zero paid leads were converting into booked meetings or progressing into CRM deals. The funnel had no mid-stage logic: no nurture sequences, no retargeting audiences segmented by intent, and no lead routing that would trigger sales follow-up on high-intent signals.
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03
High churn from brand-misfit sign-ups
The leads converting through PMAX were not matching Meilisearch's ICP. Developers signing up for free-tier exploration with no enterprise use case were inflating signup volume while contributing to high churn rates. Without audience exclusions and ICP-layered targeting, acquisition costs were being wasted on users who would never convert to paying accounts.
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04
No attribution architecture connecting spend to pipeline
There was no consistent UTM taxonomy, no conversion value mapping, and no reporting layer that connected Google Ads activity to opportunity creation or revenue in the CRM. Leadership had no visibility into which campaigns, geographies, or audience segments were producing commercial outcomes.
We didn't just fix the campaigns. We redesigned how Meilisearch buys demand.
ads.expert came in as a full performance partner, not a campaign manager. The first task wasn't launching new ads. It was diagnosing exactly where the system was failing and designing the infrastructure to fix it permanently. What followed was a ground-up rebuild of Meilisearch's paid acquisition motion, with clear separation between self-serve and enterprise demand generation from day one.
PMAX Restructure and Search Campaign Build-out
Audited and restructured the existing PMAX campaigns with asset groups segmented by buyer intent level. Launched dedicated Search campaigns targeting high-intent, bottom-funnel queries: engineering teams evaluating search infrastructure, companies migrating from Elasticsearch or Algolia, and developers building production-scale retrieval pipelines.
Dual-Track Conversion Goals and Value-Based Bidding
Separated conversion tracking into two distinct goal hierarchies: self-serve cloud registration events (volume signal) and enterprise demo request completions (revenue signal). Configured value-based Smart Bidding so the algorithm could distinguish between a $50 self-serve user and a potential $50K enterprise contract.
ICP Audience Layering and Churn Suppression
Built audience segments targeting engineering decision-makers, CTOs, and technical leads at mid-market and enterprise companies across the US, UK, and EU. Simultaneously created suppression lists from Meilisearch's CRM, excluding existing free-tier users, recently churned accounts, and non-ICP job functions.
Retargeting Sequences and Mid-Funnel Pipeline Ops
Designed and launched Google and YouTube remarketing audiences segmented by engagement depth: pricing page visitors, documentation browsers, and demo abandoners each received different creative and messaging. Built lead routing logic so high-intent signals triggered immediate sales follow-up.
Weekly Pipeline Reporting and Spend Accountability
Established a weekly performance cadence with reports covering spend, CPL, cost-per-meeting, opportunity pipeline created, self-serve revenue, and break-even period, all tied to market-level views for UK, US, and EU. Introduced the pipeline-to-spend multiple as the primary efficiency metric.
UK and US Market Prioritization
Built market-specific campaign variants for the UK and US, reflecting differences in search behavior, competitor positioning, and enterprise buyer language across geographies. Weighted budget toward the US market's higher ACV while maintaining UK enterprise coverage.
Google Ads
Google Analytics
HubSpot CRM
Notion
Google Sheets
For every $1 invested in Google Ads, Meilisearch received $3.50 in qualified, CRM-attributed opportunity pipeline. Up from zero enterprise pipeline from paid channels before the rebuild.
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Getting sign-ups but not pipeline?
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We help B2B SaaS and developer-tool companies build paid acquisition systems that reach economic buyers, not just free-tier explorers.
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