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Modern BI & Analytics
We design interactive dashboards for any type of employee to democratize insights throughout the company.
Embedded BI, also called Integrated Insights, helps to improve data usability and decision-making. In order to accomplish this, reports, dashboards, and data visualization are integrated and placed directly within the user interface of an application. Users (data consumers) can be clients, partners, and employees.
Set up alerts so you and your team can be notified when goals are reached or when issues need to be addressed. You can define thresholds and receive alerts when data deviates from expectations.
We enable developers to build any data-powered application, workflow, or tool.
Communicating with known and unknown customers, audiences and partners is more effective when your interactions are relevant to them. You want to serve the customer at the right moment with the right information in the right context. Personalization is possible in owned, earned, and paid platforms and channels and can be targeted to individuals and segments.
In our marketing analytics section, you find how you can become smarter and more relevant as a result. You will find many use cases that will inspire you divided into three parts:
Example marketing activation in a Customer Data Platform:
Firstly we ingest 1st party data. Server-side tagging is preferably used to enrich website data. A server-side persistent cookie is giving more control to the data we share with Google Analytics and other third parties. From here we build out the identity graph of clients and users in a global identifier linking all customer interactions with your channels and applications. Other sources we can ingest are campaign performance metrics, market share data, geospatial, and so on. It all comes together in a unified analytics environment where the key product is the analytical data store. Views of customers and also campaign performance for instance.
We apply different types of modeling using Google AI Platform such as conversion models to predict conversion intent, engagement models, and also models that predict Customer Lifetime Value. We score the sessions. The models are retrained automatically so we can apply different kinds of audience and bidding strategies.
Audiences can be users who are likely to convert, or, in the upper funnel high engagement score or audiences who predict if people buy more on discount or at full price. The predictions can be shared with the advertising platforms in bidding strategies.
We score the sessions. Also, new customers get a score. Usually, platforms optimize on revenue but in the example in the image above, we want to be more precise and optimizer on net profit. Not only product margin but also product returns are estimated related to the products in the basket. Next to that, if people wish to pay later with afterpay-like options, this can be added as an indicator (higher return rate).