National Clothing Retailer

About the Business

 This U.S.-based specialty retailer carries casual apparel and accessories designed for women ranging from their teens, up to their mid-30’s. Focused on fun, bold, and relevant, the company carries a massive product catalog with new arrivals released daily. With 655 brick and mortar stores in the U.S., and over 5.5M active members in their loyalty program, the company is one of the fastest growing specialty retailers succeeding at omnichannel growth.

The Business Challenges

 Not only did the clothing retailer need a way to provide customers with personalized recommendations based on predictions from Google Analytics, but they needed to unify customer experience across recommendations. Users were seeing different recommendations and product information based on the marketing channel they were using, rather than a unified message in both email marketing and Google Ads, for example.

At the time, the company was in the middle of building an enterprise- wide data strategy and data roadmap. From that overhaul, it was also revealed that the marketing team, which works primarily with the eCommerce site, would significantly benefit from specific data tools and solutions. They had been relying on out-of-the-box SaaS solutions and limited IT resources. Cloud native solutions that provide centralized dashboard insights, automatic report generation, and democratized data analytics would enable the marketing team to better shift and accommodate changing buyer behavior. Unfortunately, they did not have the internal resources necessary to take full advantage of these opportunities.

The 2nd Watch Solution

2nd Watch worked with the retailer to understand their unique requirements for a recommendation engine for their use case. After evaluating their needs and goals, the choice fell between building a custom recommendation engine, or using Recommendations AI. The already-productized, retail industry-focused Recommendations AI stood out as the best choice for cost and speed.

The retailer lacked historical product catalog data and there were mismatched product IDs between GA and the Merchant Center. 2nd Watch imported the Merchant Center product catalog into BigQuery and Recommendations AI. We ingested and performed minor processing of the GA data into BigQuery, and cleaned the data before it was fed to Recommendations AI. Two years of historical GA data was imported into Recommendations AI. 2nd Watch trained four different recommendation models, including an ‘Others you may like’ model that shows users additional complementary products based on user behavior, and demographics. 2nd Watch performed training and turning of the parameters before handing over the project playbook to the retailer to implement the solution into their applications.

The Business Benefits

With the help and guidance provided by 2nd Watch, the retailer now has an API endpoint that they can use to fetch recommendations across various applications. Having the ability to accurately match user behavior, the company can predict product interests and make the right buying recommendations based on a number of factors. This allows the retailer to improve customer experience across platforms and marketing channels, which leads to better conversion rates and increased profits.

As a Google Cloud Partner, 2nd Watch was able to complete the proof of concept at no initial cost to the retailer, which made production cost-efficient. We worked collaboratively with the Google team throughout the process – from pre-sales, all the way to presenting our findings and POC results. Using the knowledge and expertise of our cloud experts, the retailer’s marketing team had the support and best practices necessary to build and utilize a cloud native solution for business growth. They have the dashboard, reporting, analytics, and technology support they need to focus on business growth. The retailer plans to continue developing the successful data strategy they were able to prove with 2nd Watch.