How CMOs keep performance under control while scaling past $100K/month

Hey, Artsiom here, CEO at Campaignswell.

Before building a SaaS BI for performance marketing, I spent years working hands-on with growth data — attribution, revenue, forecasts, and the messy reality behind performance reports. Since launching Campaignswell, I’ve been deep in performance setups of dozens of subscription apps. And from firsthand experience, I know that every subscription app hits the same problem. <highlight-green>Around $100K in monthly spend, the marketing stack that used to work stops being sufficient for sound decision-making.<highlight-green>

At this stage, the existing setup starts breaking in very concrete ways.

• Ad platform dashboards have always been imperfect, but at higher spend those inaccuracies stop being tolerable.
• The same thing happens with MMP attribution. Renewals, refunds, long subscription cycles, web-to-app flows — the signal gets noisier, and it becomes harder to trust performance numbers when making scaling calls.
• Manual revenue tracking starts demanding more and more effort. You spend increasing amounts of time reconciling data, double-checking numbers, accounting for taxes, fees, refunds, and timing differences.
• It’s similar with the LTV you calculate in Google Sheets. You can spend hours refining assumptions, but you still can’t fully rely on the output. In practice, predictions often turn out either too optimistic or too conservative, and in both cases, that costs you revenue.
• Once web funnels enter the picture, tracking becomes even harder. Web-to-app flows, delayed conversions, and split payment systems make it difficult to connect spend to real subscription revenue at scale.

If you recognize this, let me show you how this looked for one of our clients.

How this breaks in real life

The Dialogue team (AI companion app) was scaling paid acquisition across mobile and web, and performance had to be understood through several disconnected systems. <highlight-green>Ad platforms, MMP attribution, and subscription revenue all pointed in different directions<highlight-green>, so answering the question, how spend turns into long-term revenue, kept getting harder.

Most of the work happened in manual reconciliation. Data was constantly checked and rechecked, adjusted for attribution gaps, delayed subscriptions, refunds, and renewals. <highlight-green>As monthly spend reached $100K, this setup stopped holding.<highlight-green> Spend moved forward, while visibility into revenue and LTV lagged behind, and scaling decisions had to be made without a clear view of real subscription payback.

Dialogue had ambitious plans to keep scaling and reached a familiar decision point: either slow growth down and invest heavily in building an in-house BI, or find a way to get reliable revenue clarity without putting scaling on hold.

I’ve seen this same situation <highlight-green>repeat across many subscription apps.<highlight-green> Once teams cross roughly $100K in monthly paid spend, the tools that worked earlier stop supporting confident scaling decisions.

What changes with a setup built for scale

How did Dialogue handle this? They ran the numbers and realized it made more sense to use a SaaS BI and keep scaling now, rather than spend time and resources building an in-house system. Growth was already moving, and pausing it for a year to build internal infrastructure didn’t feel like a smart tradeoff.
That’s how they came to Campaignswell.

<highlight-green>As a result, they got:<highlight-green>

• A practical single source of truth across mobile and web, built on normalized data and real payment flows.
• A clear connection between spend and subscription revenue, without daily manual reconciliation across dashboards.
• Revenue and LTV predictions based on real user behavior, accurate even with limited or no historical data, and comparable to actual outcomes as they mature.
• Granular performance visibility for UA teams, showing how each cohort performs over time by channel and campaign, with enough depth to understand what actually scales.
• Clear revenue and LTV visibility at the creative level, making it possible to see which creatives drive long-term subscription value.
• Readiness to scale web funnels, with landing-level LTV, early churn signals, and faster payback insights across dozens of experiments.
• Probabilistic cross-platform attribution, designed to work under real-world constraints, including web-to-app flows and missing signals, while keeping users in a single funnel.
• One shared performance view for in-house teams and agencies, reducing disagreements and speeding up decisions.

With unified data and predictive modeling in place, <highlight-green>the company scaled monthly spend from $100K to $1M, while achieving 10× growth in revenue, subscribers, and profit within one year.<highlight-green>

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Campaignswell is a <highlight-pink>SaaS BI built specifically for performance marketing in subscription businesses.<highlight-pink>

Campaignswell gives performance and UA teams a clear, full-funnel view of how spend actually turns into revenue and LTV — from creative and campaign, through cohorts and payback, all the way to long-term subscription value across mobile and web.

It also surfaces early ROAS and LTV signals based on real user behavior, so teams can make scaling decisions before the full payback window has played out.

At the leadership level, scaling shifts from reconciling reports to understanding how the entire system behaves under volume.

In day-to-day execution, UA teams get clear, comparable signals at the cohort and creative level, without constant manual data stitching.

If this sounds like the stage you’re in and you want to see how this looks on real data, I'm happy to walk you through it on a quick demo. Feel free to book it here.

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