Impacture brought CentreBlock's data sources, definitions and reporting logic together in one managed environment. The result is a secure multi-tenant platform that translates digital activity into concrete commercial signals — for every customer, based on the same reliable definitions.
CentreBlock helps organisations understand which digital interactions actually create value.
Traditional web analytics mostly shows how much traffic a website or application receives. CentreBlock goes further. Its patented Impact Score weighs the actions of individual visitors and surfaces which visitors, companies, pages, campaigns and digital journeys deserve attention.
By combining impact measurement with company recognition, UTM information and visitor behaviour, CentreBlock helps marketing and sales teams not only look back, but decide where to act next.
The distinctive technology and the underlying visitor data were already in place. The challenge was making that value available at scale.
Digital events, visitors, company information, pages, campaigns and weighted actions had to be linked together correctly. Definitions such as Impact, Reach, Active Visitors and New Prospects had to mean exactly the same thing in every report, export and customer environment.
At the same time, the platform had to:
Without one central information architecture, expansion would have led to ever more one-off calculations, report variants and manual checks — costly, and ultimately corrosive to trust in the outcomes.
Impacture did not begin by designing a new dashboard. The first step was to establish which data and definitions were needed to give reliable answers.
The various data sources and relationships were consolidated in one controlled architecture, connecting among others:
The key business definitions were then standardised, so Impact, Reach, New Prospects and Impact per Active Visitor mean the same thing everywhere — in dashboards, periodic updates, exports and future AI applications.
This is essential. When definitions differ per report or per customer, apparent precision is what you get. Automation or AI on top of inconsistent definitions only amplifies the problem.
The sequence was therefore: centralise → standardise → optimise → automate → share.
The solution is built around a managed data backbone in Microsoft Azure and Microsoft Fabric.
Digital events are linked to visitors, customers, pages, sources, campaigns and relevant company information.
Identities, dates, campaign fields and customer relationships are processed according to fixed rules. One central semantic layer guards the meaning of every KPI.
The weighted Impact logic distinguishes traffic that only generates volume from traffic with real commercial meaning.
The information is made available through dashboards, weekly management updates and exportable visitor lists. From an overview, users can click through to the underlying visitors, companies, pages or campaigns.
Automated refresh processes ensure the environment is updated in a controlled way every day. Capacity is only scaled up when processing requires it, then returned to its regular usage level.
What used to require technical analysis and additional explanation is now automatically available in dashboards, periodic updates and concrete visitor lists. Our customers can click straight from a development to the evidence behind it. — CentreBlock
The CentreBlock environment combines several analytical levels.
The Impact analysis shows whether meaningful visitor activity is growing or declining. Acquisition insights show which sources deliver not only traffic, but quality. Page analyses reveal which content contributes to impact and where visitors drop off or move on.
UTM analysis compares campaigns, sources, media and content variants. Status overviews flag which campaign components are growing, declining, stable or need attention.
From these overviews, users can click through to Visitor 360 and Page 360, where the underlying visitors, recognised companies, interactions and journeys become visible.
The analysis therefore does not claim unproven causality. CentreBlock shows what happened, where impact emerges and which visitor behaviour lies behind it. That gives users concrete evidence to underpin a commercial or marketing decision.
CentreBlock serves multiple end customers from one central environment. That places high demands on security and data separation.
Through the Impacture Sharing Engine, users gain access to embedded reports after a secure login. Row-level security then checks, on every data request, which customer the user belongs to. A customer therefore sees only their own visitors, companies, campaigns and results.
The same architecture serves different audiences:
Authorisation is not configured manually per individual report or file. The access rules are part of the central architecture and therefore also apply to drill-through, exports and automated information products.
CentreBlock has a scalable, managed foundation for delivering Impact Analytics.
All customers are served from the same central definitions and analysis architecture, while their data remains strictly separated. New reports, KPIs and outputs can be developed once and then made available to the right customer groups in a controlled way.
Customers do not just get a dashboard of totals. They get a coherent picture of:
Periodic updates surface the most important developments automatically. Visitor lists can be exported in a controlled way for follow-up. The information is therefore not only insightful, but operationally usable.
With this, CentreBlock has evolved its patented analysis technology into a scalable data product: one platform that delivers reliable impact information securely to multiple customers and audiences.
Impacture did not just renew the reporting. It created the information architecture with which CentreBlock can reliably, securely and scalably deliver its distinctive technology as a service.
| Then | Now | |
|---|---|---|
| Architecture | Product logic spread across database, report and calculation layers | One managed data and semantic architecture |
| Interpreting visitor activity | Specialist analysis needed to interpret underlying visitor activity | Direct access to standardised KPIs and visitor-level evidence |
| Reporting scope | Reporting mostly focused on isolated insights and technical analysis | One coherent environment for Impact, acquisition, pages, campaigns and journeys |
| KPI definitions | Risk of divergent interpretations of the same KPI | Central definitions reused across every information product |
| Tenant separation | Customer scope had to be carefully bounded per output | Customer scope structurally enforced with row-level security |
| Management information | Management information had to be actively sought out | Automated periodic updates with the most important developments |
| Data export | Data export required additional technical steps | Controlled visitor and company exports per customer and period |
| Scaling | Scaling meant more report maintenance and specialist work | New customers use the same scalable platform structure |
