Valk Exclusief is a chain of 4-star+ hotels with 43 hotels in the Netherlands. The hotel chain wants to offer guests a personal experience, both in the hotel and online.
Valk Exclusief wanted to set up a customer data platform to personalise and segment the marketing campaigns. They enlisted our help to clarify the requirements and to establish the link between Bloomreach (CDP system) and the data platform. The ultimate goal of this personalisation and segmentation was to offer customers a personalised experience by creating a 360-degree customer view.
During the project, our Data Analyst discovered several pain points: the available data was stored in a SQL server that functioned as a data warehouse, but this setup did not work as expected. This made the data unstable, and we couldn't retrieve it in large numbers. In addition, different sources did not connect with each other because different definitions were used. As a result, there were different databases within Valk Exclusief with different dashboards and values. We also discovered differences between the data in Google Analytics and the data in the hotel occupancy tracking system.
Our Data Engineers set out to work to solve these pain points. We integrated the data sources and created a single source of truth. We built a data infrastructure suitable for use in the customer data platform.
The data infrastructure project consisted of two steps:
Validation: we spoke with stakeholders about how the current data infrastructure was set up and discussed the marketing department's wishes. In addition, we asked which business requirements and technical requirements existed. In this phase, we looked at what data was present in the SQL server, how we wanted to link it, and how we wanted to put it in Bloomreach (CDP system).
Creating and implementing the plan: The data from the existing SQL server is copied daily via Stitch and stored in a data warehouse in Google BigQuery. Using DBT, we transform the data into the correct format for Bloomreach, which is then loaded via an API request. Google BigQuery is the single source of truth and has two-way communication with Bloomreach.
We used Airflow to trigger daily processes. In addition, we used Terraform as Infrastructure as Code for easy customisation of the data infrastructure. The advantage of this is that the data is more stable, less prone to errors and that it is easy to adjust the infrastructure.
The data infrastructure is ready, future-proof, and scalable, and because it is modular, it is also easy to adapt. For example, in the event that Bloomreach will no longer be the right tool, it can easily be replaced. After all, the processes are in place, and BigQuery is fully implemented.
We made significant strides towards a 360-degree customer view for Valk Exclusief. Because the data from the data warehouse is of high quality, the hotel chain has more insight into marketing campaigns' performance. In addition, the marketing team was able to draw up the right KPIs using the data. In combination with Bloomreach, the team is gaining more and more insight into who their customers are, how they behave and which channels they are active on. Bloomreach is also mainly used to send newsletters, personalise them and perform A/B tests.
Receive data insights, use cases and behind-the-scenes peeks once a month?
Sign up for our email list and stay 'up to data':
You might find this interesting too
Insight into the complete sales funnel thanks to a data warehouse with dbt
Our consultants log the assignments they take on for our clients in our ERP system AFAS. In our CRM system HubSpot, we can see all the information relevant before signing a collaboration agreement. When we close a deal, all the information from HubSpot automatically transfers to AFAS. So, HubSpot is mainly used for the process before entering a collaboration, while AFAS is used for the subsequent phase. To tighten our people's planning and improve our financial forecasts, we decided to set up a data warehouse to integrate data from both data sources.
A standardised way of processing data using dbt
One of the largest online shops in the Netherlands wanted to develop a standardised way of data processing within one of its data teams. All data was stored in the scalable cloud data warehouse Google BigQuery. Large amounts of data were available within this platform regarding orders, products, marketing, returns, customer cases and partners.
Reliable reporting using robust Python code
The National Road Traffic Data Portal (NDW) is a valuable resource for municipalities, provinces, and the national government to gain insight into traffic flows and improve infrastructure efficiency.
A fully automated data import pipeline
Stichting Donateursbelangen aims to strengthen trust between donors and charities. They believe that that trust is based on collecting money honestly, openly, transparently and respectfully. At the same time effectively using the raised donation funds to make an impact. To further this goal, Stichting Donateursbelangen wants to share information about charities with donors through their own search engine.
A scalable data platform in Azure
TM Forum, an alliance of over 850 global companies, engaged our company as a data partner to identify and solve data-related challenges.
Valuable insights from Microsoft Dynamics 365
Agrico is a cooperative of potato growers. They cultivate potatoes for various purposes such as consumption and planting future crops. These potatoes are exported worldwide through various subsidiaries. All logistical and operational data is stored in their ERP system, Microsoft Dynamics 365. Due to the complexity of this system with its many features, the data is not suitable for direct use in reporting. Agrico asked us to help make their ERP data understandable and develop clear reports.