With a previous marketing vendor, The AA faced significant challenges in achieving its ambitious campaign goals related to new customer acquisition.  

Running campaigns to acquire new customers for its award-winning breakdown service, The AA team focused on a combination of prospecting, winning back former customers, and cross-selling to existing product holders. However, its existing marketing tools presented limitations, including slow data processing times, lengthy campaign execution, and an inability to easily test and optimize multi-channel strategies. 

The team needed much better speed-to-market time and needed the ability to test the omnichannel impact of campaigns on the customers being acquired.

In North America, Havanianas relies on effective email marketing to engage customers and drive sales through its direct-to-consumer ecommerce business.

Previously relying on an external agency to manage its ecommerce operations and email marketing capabilities within its Salesforce Marketing Cloud (SFMC) instance, Havaianas sought the flexibility that would empower its team to take greater control of its email marketing internally and scale the program more effectively.

As Hobbycraft worked to double down on its ecommerce strategy, the brand came up against several challenges in delivering a seamless search and merchandising experience. Prior to Bloomreach, Hobbycraft leveraged a rule-based solution that relied heavily on perfectly structured and enriched product data — something that was difficult to scale or even maintain. With 27,000+ SKUs spanning dozens of arts and crafting verticals, even small gaps in data could break the experience, which made the on-site search bar inconsistent and unreliable. 

A query like “paint” might return oil sets, watercolors, and kids’ kits all in one product grid without any logic behind the order or mix of products. Similarly, “stickers” surfaced only children’s products, completely overlooking other subcategories like scrapbooking supplies. Due to these issues, Hobbycraft sought to better support shoppers in navigating to the most relevant options for their specific project. 

Overall, traditional merchandising tactics, like pinning products or boosting by attributes, offered some control but also came with major trade-offs, including constant manual updates and challenges with out-of-stock items. The workarounds to these issues were resource-intensive and difficult to scale, particularly for a small team responsible for a large catalog. Hobbycraft needed a next-generation AI search and merchandising tool to help manage the experience by exception, without sacrificing relevance or performance. This would allow them to showcase their full product assortment, accommodate different types of shoppers, and reduce the burden of constant manual updates.

To best serve its customers and continuously meet their short-term coverage needs, Dayinsure strives to build an ongoing relationship with its audience. 

But doing so was a challenge with its previous marketing technology. Dayinsure needed to fully understand its customer base — what they wanted, what incentives they looked for, their preferred way to connect with Dayinsure, and more — to build customer relationships that went beyond the first purchase. 

At the same time, the brand needed to automate reengagement campaigns and effectively leverage its customer data. Manually segmenting audiences and building workflows took up too much time and team bandwidth, driving Dayinsure to seek out a more advanced solution.

TFG has always had a customer-first approach, which is why it wanted to find new ways to create personalized experiences for its customers without being intrusive. The company was aware of recent advancements in AI technology that would open up new ways to connect with customers. In particular, the organization wanted to explore using conversational shopping as a solution.  

However, since conversational AI is still a new technology, TFG had concerns: What would it be perceived as assisting with? Would the solution just provide stock answers (that any algorithm could spit out)? Would it hallucinate and provide inaccurate results? Would it be a closed system? 

TFG needed a solution that could properly address these concerns while also driving consequential business impact.