INTERSPORT wants to use its own data to increase performance, but this data is only available sporadically and locally.
A cloud-based business intelligence solution enables INTERSPORT and its retailers to make the best possible use of their data and thus establish a future-proof, data-driven working culture.
A strength of the solution is not only the visualization of the data, but also the solution of data problems. Furthermore, it provides a holistic approach and covers the broadest range of applications with technology and tools in one solution. From standard and custom integrations, a high-performance ETL line and a flexible analytics database with a built-in commerce data model to the tools needed for a data-driven work culture, everything is included. A further differentiation is achieved by the fact that the solution is ready for use virtually from day 1 by means of straightforward onboarding via standardized interfaces. Desired individualization and extensions can then be implemented parallel to productive use. Thus business users from all areas can work data-driven - without explicit IT knowledge. A technological unique selling point is also the best-practice data model: Through the implementation of numerous use cases from the retail sector, the software has a uniform definition of key figures. The unique, comprehensive commerce data model comprises more than 500 attributes and key figures and serves as the basis for all tools and uniform communication - a perfect fit for every retail organization, regardless of industry and business model. In order to meet specific customer requirements, additional interfaces, tools or features are developed together with the customer on the basis of use cases, which are then available to all users. In this way, the company regularly supports its customers with new features and lays the foundation for being able to compete in the market.
For over 6 years and with the extensive expertise from 150+ implemented projects in retail, eCommerce and Omni-Channel, the provider supports its customers in data-based decision making. Already various companies from the fashion industry, such as Philipp Plein, Betty Barclay or Scotch & Soda, high-turnover retailers like the ANWR Group, Depot and Decathlon, but also innovative fashion start-ups like SugarShape and Avocadostore rely on the "Plug & Play" BI solution and thus control their business in a data-driven way.
Support for employee acceptance
Use cases and user needs must be the focus of attention when introducing new systems in the company. It is therefore important that projects follow an interdisciplinary approach and that representatives from all user areas are involved in the overall process. The communication of goals and added value plays a decisive role in this.
Average KPIs for projects of the provider
The return rate can be reduced by 2-5%.
Customer value and turnover
The customer value (CLV) of existing customers is maximized and new customers with the highest possible CLV potential are acquired at the lowest possible cost. At a conservative estimate, an increase of 10-15% on the CTMV is possible.
At a level of contribution margin accounting with an improvement of 2-3%, 10% more contribution margin from sales can remain.
a) First, a requirements analysis with concrete use cases is developed together with the customer and potential users are identified. b) The Business Intelligence solution is presented and concrete solution approaches are visually illustrated within a demo. The customer also has the opportunity to test the solution himself via a demo access. c) After signing the contract, a workshop with the stakeholder groups takes place at the customer's site in order to implement use cases with specific users. d) Each customer has a dedicated key account manager who, in addition to technical support, is also available to advise on optimization and other application areas.
Customers who make multiple purchases as guests on a website do not have a complete customer profile and can therefore only be identified by their e-mail address, for example. In the software, these purchases can be assigned, consolidated and merged into a customer profile based on the e-mail address. This leads to a better and above all complete data basis for further analysis.
Where is the data:
Access via the web frontend is https-encrypted. In order to offer state-of-the-art technology with high operational and cost efficiency, the data is hosted in a fully scalable cloud environment at Amazon Web Services in Ireland. As a result, the customer pays only a fraction of what it would cost to own a hosting environment and does not need to invest any resources in operation and maintenance.
a) First, the customer's existing technical setup is recorded, including for example the source systems. b) This is followed by the definition and setup of the interfaces. c) Then the data exports are set up by the customer d) The provider validates customer data and creates user accounts e) The Business Intelligence solution is ready for use.
The solution has standard interfaces to connect systems via a simple "plug and play" approach. In addition to source systems (pixi Descartes, Spryker, Magento, Newstore, Google Analytics, etc.), outgoing interfaces to third-party systems - especially to operative systems such as email marketing or visualization tools such as Tableau - can also be connected.
The ETL process:
In the Extract phase, the data is extracted from all relevant source systems (e.g. Web Tracking, Google Analytics, ERP, marketing tools, etc.) In the Transform Phase, these different data sets are then brought into a uniform format and cleaned up, then consolidated and in the Loading Phase integrated into Elastic Search and made available for analysis. The ETL process is built in Apache Spark, an analytics engine for big-data processing that ensures high performance and efficiency and, most importantly, full scalability.
To improve data quality, validation ensures that data from the various sources is robust, reliable and meaningful. A method for the continuous improvement of the quality takes place for example in the Transform Phase. In order to avoid duplicates, it is ensured that the uniquely assigned ID of each data entry is contained only once in the database. If the data entry occurs more than once, the information of the last entry is used.