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Brightloom Drives Sales For Brands With Predictive Platform

Brightloom Drives Sales For Brands With Predictive Platform

San Francisco-based company Brightloom’s SaaS Customer Growth Platform works behind the scenes to turn a brand’s online customer transaction history into a predictive tool to drive personalized recommendations and promotions.

Using artificial intelligence and proprietary models built by its internal team of data scientists, Brightloom takes in historical transaction data from online businesses, along with catalog and menu items and marketing campaign KPIs, and analyzes the collected dataset using machine learning to break apart each customer’s personal history and compare it to similar customers in order to then predict future behavior. The approach is similar to the process undertaken by audio streaming and media services provider Spotify with its music recommendation engine; and allows Brightloom to provide the users of its Customer Growth platform with a personalized approach to each customer, making each interaction more actionable by highlighting customer preferences with the end goal of increasing transaction frequency. For example, if a customer at a client partner Jamba® orders a specific menu item consistently, and then one day switches things up and orders a different smoothie or a new item, the Brightloom platform is able to predict where they might land next by fitting this behavior into an overarching, patterned archetype that the juice shop can then activitate with customer-specific promotions or product recommendations. 

Brands big and small have reached a so-called tipping point where the primary relationships with their customers have become digital or moved online. As a result, companies are sitting on troves of consumer data. But this data is hard to leverage, which is where Brightloom’s platform comes in. Whereas generally companies would push out a one-size-fits all message to their customer base, Brightloom’s Customer Growth solution allows brands like Kickee, Evergreens, Ruby Tuesday, and El Pollo Loco to tailor messages to customer behavior, breaking customers down into “smart segments” which are tackled individually to provide the right personalized recommendations and offers, leveraging this bulk of collected data to create stronger customer relationships. 

The trend of dynamic promotions, based on the application of deep learning and AI technology to the collection and processing of real-time data, is becoming more important to retailers, especially online retailers, as the advantages of behavioral predictions and tailored interactions become clearer across brands’ bottom lines and they see a marked increase in customer engagement and spend. 

Brightloom, for example, originally offered its predictive platform and best-in-class digital solutions just to the restaurant industry. However, the company caught the attention of multinational coffee chain Starbucks, who provided funding in exchange for an equity stake and seat on the Brightloom board of directors. With this new capital injection and executive oversight, Brightloom expanded its services to online retailers in general, as well as entered into an agreement with Starbucks to power the cafe chain’s mobile app and customer loyalty program with its technology. 


This article originally appeared in PSFK’s research paper, AI-Supported Conversational Commerce