How Data Pilot helped a fast fashion brand grow its market share & increase revenue

Tools and Technologies

Company Background

Since 2017, Lulusar has been serving today’s modern and independent woman with functional and affordable fast fashion. To focus on quality over quantity of their apparel, the company employs a digitized product development cycle in every step of the fashion value chain. This enables them to stay adaptive to consumer trends, accurately forecast fashion trends, produce quality products with short lead times, and avoid overproduction and waste.

Challenges

Manual data entry

Exclamation triangle – data consulting company Manual data entry The company was manually entering data into Google Sheets from various sources, including Facebook data like post likes, shares, and comments, as well as sales data. This manual process was time-consuming, prone to errors, and unable to scale with the business’s needs.

Siloed and inaccurate data

Because of the manual process of data entry, the company grappled with significant issues related to data accuracy and consolidation. As the volume of data increased, the risk of errors grew, making it difficult to rely on this information for business decisions.

No Time Available for Making Key Decisions

Inaccurate data and the inability to consolidate information from various sources made it hard for the company to decide when and where to sell their products, which cities to focus on, and which channels would bring in the most revenue for them.

Solution

Data Pilot engineered an omnichannel analytics solution that connected the company’s online and offline data. The solution was divided into 2 stages:

By integrating information from sources like Meta (Facebook and Instagram), Shopify, Google Analytics (their website), and their point-of-sale systems, the solution provided the company with a 360° view of its sales across both its e-commerce and physical stores.

The analytics dashboards were built on Tableau to better make sense of this huge amount of data. The dashboard enabled a granular analysis of sales, customer demographics, and market trends on a city-by-city basis so that marketing and inventory management teams can develop targeted marketing strategies and manage stock levels more effectively in different locations. This ensured that the right products were available where they were needed most, ultimately improving customer satisfaction and sales performance.

Single source of truth across the enterprise, eliminating data fragmentation and drift.

With the help of the dashboard, Data pilot was able to offer:

The Impact

Increase company revenue

Increased Revenue by data-driven driven decision-making

1.12% website conversion

Website conversion rate (2022-23)

39.5% website bounce rate

Website Bounce rate (2022-23)

Enhanced innovation of product lines

The designers in the company were able to examine the success of products by city, channel, quantity sold, and revenue generated in granular detail. This enabled them to make targeted innovations in the designs of their apparel and maximize returns on their apparel.

Increased overall productivity

By eliminating countless hours of manual data reconciliation, the solution enabled the company’s teams to use their free time to focus on core initiatives rather than drowning in spreadsheets.

Enhanced A/B testing of marketing campaigns

The solution enabled the company to A/B test its marketing campaigns across channels, helping it maximize its ROI on promotional spend.

Revealed hidden sales trends

The solution helped unveil hidden sales trends through advanced time-series analysis, enabling the team to adapt to emerging consumer preferences and stay ahead of seasonal shifts.

Industry Applications

Further Data Uniformity in Retail

Future efforts can focus on enhancing the uniformity of product-related data between Shopify and Odoo. Ensuring accurate product category level tagging on both platforms will improve data consistency and reliability.

Geospatial Analysis in Logistics

The omnichannel solution can have geospatial map charts to display sales figures by geography, down to the neighbourhood level. This will provide deeper insights into regional performance and help logistics companies tailor their supply chain strategies accordingly.

Incorporating Machine Learning

A machine learning model can be built alongside this solution to predict marketing spend across different channels. This will ensure that companies can optimize their budget allocation and achieve their target revenue more efficiently.

Our Clients Come First

Data Pilot is helping Lulusar leverage data to create the optimal experience for its customers & business. Together, we are pursuing in-depth analytics and predictive modeling to optimize how we grow & retain customers.

Data Pilot dashboard interface showcasing data analysis tools and visualization features.

Saif Hassan

Director Lulusar

Saif Hassan

Director Lulusar

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