Lulusar did not have any reporting for analyzing business performance. The business required both descriptive and predictive analytics for decision making.

The project started with developing dashboards for sales, products, and customer analytics to enable data-driven decision-making.

Phase 2’s scope was the optimization of marketing expenditure and consolidation of data from various sources(e-commerce sales, website, Facebook, Instagram and google ads) to analyse the effectiveness of each campaign on sales and see which marketing channel produces a better ROAS (Return on Ads Spend). Moreover, this involved building a machine learning model to predict marketing expenses across different channels which will help them achieve their target revenue. The spending was based on intuition before with no data backing. Phase 3 consists of building a design analytics dashboard which contains analytics of revenue and orders  

Moreover, we are also working on enhancing the marketing spend prediction model with some variables. In addition to that, the customer segmentation use-case is also being worked on to segment Lulusar users in different segments and send them targeted emails and SMS.

The team has a singular platform for analytics, which helps with data-driven decision-making across departments and increases visibility to the entire team. Lulusar can improve shipping or customer experience and even other operations through insights, for instance, how did the current month perform vs. last month in terms of lost revenue? How is the shipping and production team performing? Which designs or products are doing better in certain areas than others are? How to predict how much to spend on Paid Media on one channel vs another?