This luxury fashion house deals with billions of dollars in sales from a range of channels: their own bricks and mortar retail stores, their online stores, and third-party retailers. They needed to cut through this complexity to get a clearer picture of omnichannel sales performance across widespread regions to make more informed decisions about what to sell, where to sell and to whom, and how to promote their products.
The company’s marketing department was experiencing the paradox of having vast volumes of data at their fingertips but getting few reliable insights from it. They had all the data they needed to see the complete picture of sales performance across regions and sales channels, but lacked the ability bring it all together to get the timely insights they needed to improve on all aspects of their marketing efforts.
The problem was that their data was in multiple systems and databases and didn’t follow a common format or reside on the same platform. Further, their existing manual extraction process and Excel-based analysis created two problems: first, it consumed an enormous amount of time and effort to extract insights, and second these insights were unreliable as they were subject to human error. Often, they were full of anomalies, incomplete records, and duplication. By cleaning, deduplicating, and integrating their data in an efficient way, they would gain the insight they needed for a better understanding of which product lines were selling well, where they were being sold, and through which channels. This would help them focus their marketing efforts so they could reach the right customers with the right products at the right time. To accomplish this, the client knew they needed to find a better way to integrate their complex data sets than to continue with a time-consuming manual process of creating reports in Excel.
They knew that Google Cloud could provide a cost-effective means of integrating data efficiently. They also knew that Google Cloud would allow them to scale on demand, collaborate more effectively around data, and focus on getting insights rather than managing infrastructure. And they understood the power of Google BigQuery to provide the insights and integration with Tableau, a tool they had recently decided to adopt for better visualizations.
The client didn’t have in-house expertise in big data integration or in Google Cloud. They needed to partner with a company with deep Google Cloud expertise and experience integrating complex, multisource data from a range of on-premises databases and external systems. Pythian was the logical choice.
