How a commercial weather data and forecasting business gained full visibility into a 3.5M-user consumer business over four years

Without disrupting its dominant B2B operation, without a new product team, and without waiting years to see results.

3.5M+ MAUs tracked at peak · iOS, Android, and Web covered from a single model · Xero-integrated automated financial reporting · 4-year analytics partnership

The bridge. The business had a consumer product used by millions of Australians and almost no reliable way to understand how it was performing. Four years later, the finance team had automated reporting tied directly to Xero, the product team had behavioural data going back to 2017, and leadership could make product and pricing decisions with confidence. That shift is the work.

The client is a leading commercial weather data and forecasting business in Australia, with operations spanning consumer apps and an established B2B intelligence business. The B2B side of the business had been the primary focus for years: it had clear commercial structures, dedicated resources, and reliable reporting. The consumer product, covering iOS, Android, and Web, had grown to reach more than 3.5 million combined monthly active users at peak. But it had been running largely on autopilot.

The consumer business lacked an analytics layer. Product decisions were being made without reliable behavioural data. Revenue performance was tracked loosely through Xero, with no connection to what was actually happening in the product. There was no standard way to measure or compare engagement across platforms, and no systematic view of what was driving growth, retention, or monetisation.

The question the business brought to SeidrLab was direct: we have a large consumer product and almost no visibility into how it is performing. What would it take to change that?

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How we built the cross-platform measurement foundation

Before any analysis could be useful, the measurement infrastructure had to be rebuilt. The consumer product spanned three distinct platforms, each with its own data characteristics, engagement patterns, and revenue models. A useful analytics system had to cover all three from a single coherent model, not three separate reports that required manual reconciliation each month.

We started by defining the core metrics that mattered: Monthly Active Users, Daily Active Users, DAU/MAU engagement ratios, page views per user, session lengths, and year-on-year growth. These were the indicators that would tell product and leadership whether the consumer business was healthy, growing, or in decline. None of them were being tracked consistently before the engagement.

The model was built to cover iOS, Android, and Web, with tracking back to 2017 where data existed. At peak, the system was measuring more than 3.5 million combined MAUs, with iOS alone reaching 1.8 million. The data pipeline ran on an automated monthly cycle, removing the manual effort of assembling platform data and giving the product, finance, and technology teams a shared, reliable view of the business.

The architecture was built to be maintainable. Each subsequent month’s reporting ran automatically, with the same definitions applied consistently across platforms. Leadership could compare this month to last month, or this year to last year, with confidence that the numbers reflected the same thing each time.

Key takeaway. Measurement that requires manual assembly is measurement that gets skipped or estimated under pressure. The first job of analytics is to make the right numbers available without effort.

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How we connected consumer performance to financial outcomes

Getting behavioural data and financial data to talk to each other was the step that changed how the finance team could work. Before the engagement, Xero held the financial records and the product analytics lived separately. The finance team could see revenue. The product team could see engagement. Neither could see both together.

We integrated Xero with the analytics layer, benchmarking LTV (lifetime value) and CPA (cost per acquisition) against actual product performance. For the first time, the finance team could connect revenue data to user behaviour, and the product team could connect feature decisions to commercial outcomes. A change in engagement rates could be tracked through to its financial effect. A shift in acquisition costs could be weighed against the LTV of the users being acquired.

The automated reporting ran on a monthly cycle, covering month-to-date performance, month-on-month trends, and year-on-year comparisons. Reports that previously did not exist, or existed as partial manual documents assembled from different sources, became standard operating rhythm. The finance team had automated reports delivered without manual intervention, covering the consumer business at the level of detail needed for planning and board reporting.

Key takeaway. Integrating financial and behavioural data is harder than building either layer separately, but the combination is what enables the questions that actually matter: not just “how many users?” but “what are those users worth, and are we acquiring them efficiently?”

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How we built the B2B enablement layer alongside consumer analytics

The B2B business served enterprise clients across Australia. The sales team needed fast, reliable account health information to manage relationships and identify at-risk accounts before they became problems. That required different reporting to the consumer analytics work, but it ran on the same data infrastructure.

We built a “Top 10 accounts” dashboard that gave B2B sales reps a clear view of their most strategically important clients: usage trends, engagement signals, and account health indicators that made it possible to prioritise account management time and prepare for conversations with context rather than guesswork. The dashboard surfaced changes in behaviour that might indicate a client was underusing the product or considering alternatives.

The effect was that the B2B team, which had been managing account relationships largely on instinct and call history, gained a data-informed layer to their outreach. Sales reps could look at a client account before a call and see exactly how it was trending.

Key takeaway. When B2B and B2C data live in the same infrastructure, the investment in consumer analytics becomes a capability that the whole business can draw from, not just the product team.

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How we sustained and extended the analytics model over four years

A three-year embedded partnership followed by a year of strategic advisory reflects how this kind of work actually compounds. The initial build established the measurement foundation. The years that followed were spent extending it: adding new reporting requirements as the product evolved, incorporating new product lines into the same reporting standard, troubleshooting data connections, and refining the model as needs became clearer.

In the advisory year, SeidrLab worked with leadership to identify the initiatives most likely to drive growth and clarity in the consumer business: where to invest in new features, where the analytics were pointing to unmet user needs, and how to prioritise the product roadmap against commercial objectives.

The relationship worked because the analytics model was treated as a living system, not a one-time build. As the product changed, the tracking changed. As the reporting needs evolved, the dashboards evolved. The goal throughout was to ensure that leadership always had an accurate, current picture of the consumer business.

Key takeaway. Analytics infrastructure is only valuable while it accurately reflects the product. Maintaining and evolving the model is not a secondary task: it is the ongoing work that makes the initial investment worthwhile.

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What changed

Before. The consumer product was reaching millions of users with no coherent analytics layer. Product decisions were made without reliable behavioural data. Revenue was tracked through Xero with no connection to what was happening in the product. Reporting, where it existed, was assembled manually from disparate sources. The B2B team had no systematic view of account health. Leadership lacked the visibility needed to make confident product, pricing, or investment decisions about the consumer business.

After. A single automated analytics model covered iOS, Android, and Web, tracking MAU, DAU, engagement ratios, page views, session lengths, and YoY growth back to 2017. Xero integration benchmarked LTV and CPA against product performance. Monthly reporting ran automatically across Finance, B2B, and B2C without manual assembly. The B2B team had account health dashboards and a Top 10 accounts view. Leadership could see what was driving engagement, where monetisation opportunities existed, and how the consumer business was trending.

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Does this fit your situation?

If your business has a consumer product with significant user numbers but limited visibility into what is driving engagement, retention, or revenue, and your financial and product data have never been connected: that is the foundation we build.

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