How a Global Enterprise Gained Real-Time Visibility Into Digital Performance Across Every Channel
There is a version of digital marketing that most enterprises are running and a version that is actually useful. The version most organisations are running involves pulling reports from five different platforms, combining them in a spreadsheet, waiting two days for the data to be complete, and then making decisions based on numbers that are already out of date by the time anyone sees them.
The version that is actually useful is the one where the team can see what is working and what is not, across every channel, in near real time, and make decisions on the back of that while it still matters.
The gap between those two versions is almost always an infrastructure problem. Not a strategy problem, not a talent problem, not a budget problem. The data exists. The insights do not, because nobody has built the layer that connects the data sources into something the team can actually use.
This is the problem a major global enterprise came to Dygital9 to solve.
The Situation
The organisation had a sophisticated digital marketing operation spanning multiple channels — social media across LinkedIn, Facebook and Instagram, digital advertising, owned content and direct outreach. Each channel was generating data. None of it was connected.
The marketing team was making decisions about channel budgets, content strategy and customer acquisition based on incomplete information. They could see individual channel metrics in isolation but had no unified view of which channels were actually driving customer acquisition, what the cost per lead looked like across the full mix, and where the marketing spend was generating return versus where it was going into a black box.
The business had an existing data and analytics environment, a Qlik deployment, but it was not connected to their social and digital channels. The data that would have answered the questions the marketing leadership was asking existed in the platforms where it was generated and nowhere else.
What Dygital9 Built
The scope of the engagement was to connect the organisation's key digital channels to their Qlik analytics environment, build the data pipelines that would keep everything current, and deliver a reporting layer that gave the marketing team the visibility they needed to make faster, better-informed decisions.
Custom API connectors for every major channel
The first step was building custom connectors between Qlik and the organisation's LinkedIn, Facebook and Instagram accounts. This is not a point-and-click integration. Each platform exposes its data through APIs with different structures, different rate limits, different authentication mechanisms and different data schemas.
Building connectors that are reliable, that handle API changes gracefully, and that capture the right data at the right granularity requires engineering work that is specific to each platform.
The connectors built for this engagement captured performance data across all three channels, post performance, audience data, engagement metrics, reach and impression data, and the advertising performance data that connects channel activity to acquisition outcomes.
A unified data integration pipeline
The connectors alone produce data. The integration pipeline is what makes that data useful. Dygital9 built a daily data pipeline that ingested the channel data, normalised it into a consistent schema, stored it in a structured data layer, and made it available to the analytics environment in a form that could be queried and visualised.
The daily cadence was designed specifically to meet the team's decision-making cycle. Data from the previous day's activity was available each morning, giving the marketing team current information for their daily decisions rather than week-old information for their weekly reviews.
New Qlik reporting environment
The final layer was the reporting environment built inside Qlik. This is where the data became insight. New dashboards were built to surface the metrics that actually mattered for the organisation's decision-making, not the vanity metrics that social platforms display by default, but the connected view that showed channel performance in relation to customer acquisition outcomes, cost efficiency across channels, and the content and audience patterns that were driving results.
The reporting environment was designed to be used by the marketing team directly, without requiring data team involvement for routine reporting. This was a deliberate choice, the value of near real-time data is only realised if the people making decisions can access it without delay.
What Changed
The most significant change was the speed at which the marketing team could respond to what was happening in the market.
Before the engagement, a campaign that was underperforming would continue running for days or a week before the data was assembled and reviewed in a meeting. After, underperformance was visible the following morning. Budget could be reallocated, creative could be tested, and targeting could be adjusted before the spend had accumulated further.
The unified view across channels also changed how the organisation thought about channel mix. With individual channel metrics in isolation, each channel appeared to be performing reasonably well. With the connected view, it became clear that some channels were driving a disproportionate share of the customer acquisition at a lower cost per lead, while others were generating engagement but not contributing meaningfully to acquisition outcomes. This visibility directly informed channel budget decisions that would not have been possible without the infrastructure to see across the full picture.
The cost per lead and cost per acquisition metrics improved not because the organisation suddenly became better at digital marketing but because they could finally see what was working and allocate budget accordingly.
The capability was already there. The visibility was not.
What This Engagement Demonstrates
There is a pattern in enterprise digital marketing that this engagement illustrates clearly. Most organisations have more data than they can use, not less. The constraint is not the data, it is the infrastructure that connects the data to the people who need to act on it.
Custom API connectors, data integration pipelines and unified analytics environments are not glamorous technology investments. They do not generate a press release or a product launch. But they are the infrastructure that makes everything the marketing team does more effective, because every decision is made with better information than the one before it.
The other pattern worth noting is the relationship between data freshness and decision quality. Day-old data produces different decisions than week-old data. When the marketing team can see yesterday's performance this morning, they are operating in a fundamentally different mode than a team working from last week's report in this afternoon's meeting. The cadence of insight shapes the cadence of action, and the cadence of action shapes the results.
The Technical Stack
For organisations considering similar engagements, the technical architecture involved:
Data sources: LinkedIn Marketing API, Facebook Graph API, Instagram Graph API Analytics environment: Qlik Sense with custom data models Integration layer: Custom Python-based API connectors with daily automated data refresh Reporting: Qlik dashboards with CPL, CPA, channel performance and audience analytics
The specific technology choices reflected the organisation's existing environment. Different organisations with different analytics platforms would require a different implementation. The principle, connecting the channels that are generating data to the environment where decisions are made, applies regardless of the specific stack.
Working With Dygital9
Dygital9 designs, builds and operates data integration and analytics infrastructure for global enterprises. Our work spans financial services, retail, logistics, travel and digital marketing, anywhere organisations are sitting on data they cannot effectively use.
The Gresco engagement reflects a capability we apply across a range of industries: identifying the gap between where data is generated and where decisions are made, and building the infrastructure that closes it. The result is not a new strategy. It is the ability to execute an existing strategy with better information, faster.
For organisations whose marketing teams are working from incomplete or delayed data, the question is not whether better visibility would improve outcomes. It is how quickly the infrastructure to provide that visibility can be built.
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