The retail sector has been grappling with the challenge of finding an attractive and scalable business model for e-commerce for quite some time now. While rising online sales have led to increased convenience for customers, it has also disrupted traditional retail models and created a highly competitive and complex market. Retailers are struggling to strike a balance between meeting the demands of online shoppers and maintaining profitability, and many are still experimenting with different e-commerce strategies to find the right fit for their business. 

Challenging dynamics of DIY 

In the case of a DIY (do-it-yourself) retailer, e-commerce presents a unique set of challenges. DIY retailers often deal with large and bulky items that can be difficult and expensive to ship. Additionally, the assortment requires more detailed product descriptions and instructional materials than other retail categories, which can be time-consuming and costly to create. Finally, DIY shoppers tend to be highly engaged and knowledgeable, making it critical for retailers to provide a high level of customer service and support to ensure customer satisfaction. 

Looking into shopping baskets and product-specific costs 

Having experienced significant growth in recent years, most noticeably during the Covid pandemic, Intergamma did not get a grip on the underlying cost and witnessed a drop in profitability. The company was unsuccessful at truly understanding where the costs were coming from, focusing only on profit/loss generated by the front margin. There were several insights to be gained by looking into shopping baskets and product-specific costs such as inventory, logistics, returns, waste and the call center. Yet, these insights were not readily available due to a lack of an integrated data landscape. 

Algorithms and business intelligence pinpoint improvement potential 

The IG&H team started by building a pipeline that connected 20 data sources (including sales data, shop floor plans, logistics costs and promotion folders) to a data warehouse based in the Google Cloud. On top of this data warehouse, the team developed 25 cost and revenue allocation algorithms and rules. The data results were published in a well-known self-service BI (business intelligence) dashboard.  

Dashboards per user type 

Next, dynamic dashboard views were created based on four types of users: merchants, online team, supply chain/logistics, and finance. The dashboards provided insights into profitability metrics from multiple angles including time periods, banners, product categories, order level and fulfillment streams. Outliers were automatically visualized to provide focus and direction for improvement initiatives of all sorts. 

Once the dashboards were ready to go, the system was rolled out to over 100 users from different disciplines who received hands-on training to understand the methodology behind the dashboard and how to use it. The users were also empowered to define their own profitability improvement initiatives. The critical final step was to ensure the insights generated by the dashboard would be used. To that end, IG&H and Intergamma jointly defined clear roles, responsibilities and governance, with a meeting structure and KPIs that would enable the teams to achieve their profitability goals.