Groupe Rocher how we made our CRM data platform greener
Gwenola Cadic is a Data Architect at Groupe Rocher & Nathalie Husson is a Data Product Manager at Yves Rocher & Arnaud Col is a Data Architect at Ippon Technologies. This article discusses how they made their CRM platform greener.
The Yves Rocher brand was started in 1959 and was a creator of beauty and personal care products. It is now part of Groupe Rocher. Groupe Rocher is composed of nine brands and is present in 120 countries.
The values we share within the group are very important for all of us. These are – a commitment to nature, respect for our communities, demanding value creation, and passion for wellbeing.
Connected to our commitment to nature, we are committed to finding and implementing the best possible solution to reduce our environmental impact. That’s why when we decided on brand transformation, we decided to have data at heart and build a centralized view of our customers crossed with our products and services. Our job is to collect raw data and convert it to smart data. We want to be an enabler for business uses monitoring, targeting, data science, etc. We wanted to get free from our historical silos and provide an omnichannel view of customer knowledge. Business concepts are key to improving data culture and generating business value.
Raw data is of little value. The data transformation process transforms it from simple to smart data, which means giving sense to data to transform it into a major indication. When you are part of a business team, no matter if you’re a data scientist or global manager, you have no time to lose on understanding the meaning of a KPI. The meaning of turnover should be the same for everyone. That’s why business concepts are key to improving data culture and generating business value. And this is what we mainly do.
In this project, we designed an enterprise model by defining the business content concepts viewed without the division. So, there is one concept, one definition, and the same meaning for everyone.
Key concepts of the CRM data platform
- Organize your data – Collect, Unify, Specialize, and Share. This should focus on business goals. We must also focus on “collect” and “share” areas because they often generate energy consumption and unnecessary data storage. These areas are often neglected because they require a global vision. But this is important for improving the overall sustainability of the company. If you want to be sustainable, integrate only useful data.
- Manage your data – Data management implies defining and writing processes and identifying and monitoring components and solutions in your platform and governance. Defining and writing business and technical processes is important. They are linked but important in their way. Identification and monitoring components and solutions depend on your requirement. There may be 10 or 1000 components or solutions but concentrate on the important used components. Creating a simple governance module and automating it is an important part of creating a data platform. Managing data is the key to the lifecycle of the data platform.
- Understand your data – We can have a lot of technical and functional information on the data. Start with the classification and qualification of the entire data. Motivate reuse rather than consumerist creation.
Our CRM data platform (Key points)
The three main business issues are targeting, monitoring, and data science.
Targeting – Providing a data set for another system, the Salesforce Marketing Cloud.
Monitoring – Provides data set to the dashboard for commercial and marketing uses, campaign monitoring, and central monitoring.
Data science – All data produced on the platform is shared with the data science team.
While building the platform, we must consider energy resources. We also have to keep in mind the IT department. The platform needs to be built using powerful and flexible technologies.
We chose a layered architecture. Each layer has a meaning and architectural rules. We have identified three layers –
- Integration layer – Space of integration
- Processing and raw layer – Space of normalization and processing. It gathers the technical components necessary for the functioning of the data platform.
- Enterprise and business layer – Space of Modulization and sharing
Our choice was motivated by GCP, focusing on sustainability.
- Continue to advance in our data governance
- Implement more metrics on our energy consumption
- Set up an end-to-end data retention process
- Change our data provisioning in our test environments.