Thoughtful brands make a point of putting customer experience at the forefront of their marketing strategy—after all, many customers believe that their experience with your app or site is as important as the actual product or service you provide. When online study platform Quizlet began planning a campaign that required a more efficient way for them to collect customer data, they made sure that those potential complications didn’t get in the way of serving up a better customer experience. Instead, they used Braze to break down siloed workflows and establish a new approach to data collection to make more relevant messaging possible.

Optimizing Data Collection For Feature Suggestion

Created for students, Quizlet enables studiers to review information through flashcards, tests, and games. By providing various forms of interactive studying tools and games, Quizlet actively stands by their mission of helping both students and teachers practice and master the topics they are reviewing. To further aid in the studies of its users, Quizlet currently offers five study modes and two studying games for its customers to choose from, including:

• Learn: In this mode, Quizlet creates a personalized study plan based on student familiarity with their study set
• Flashcards: This mode provides digital flashcards to help students quiz themselves on terms, definitions, and answers
• Spell: In this mode, Quizlet uses text-to-speech audio to read terms/definitions and asks students to write what they hear
• Write: This mode allows students to type out written answers and then review wrong answers in a follow-up study session
• Test: In this mode, Quizlet mimics a real in-class assessment to help students assess how they would perform if asked to take a quiz or test on a given subject

To help users find the right modes and games for their specific needs, Quizlet focused on nudging customers to check out different studying modes and games that they weren’t already utilizing.

Effective Data Management Takes Successful Cross-Team Collaboration

In modern customer engagement, it can be tempting to track every piece of customer data possible—but that’s a temptation brands should resist. Collecting customer data that isn’t essential for your customer efforts burns engineering resources, drives up costs, and makes it harder for marketers to sort through the information at their disposal to take effective action.

When collecting the kinds of highly nuanced engagement data needed to accurately target its campaigns, Quizlet ran into two main issues: Accuracy and data usage. They wanted to retain the full-spectrum of historical data for every user’s mode usage and efficiently collect new information, but struggled to ensure data accuracy without collecting far more data than they needed.

Quizlet’s engineering and product teams first tackled this issue. One proposed solution was to run queries on the modes being used by each customer. However, because only the last 100 modes came up in the query, this method didn’t accurately represent historical data for individuals who consistently used one mode or game in their recent study habits. Another proposed solution was to set up a boolean custom attribute based on if a person used a specific study mode or not (e.g. Customer X has/has not used Quizlet’s “Learn” mode). But because Braze would be pinged every single time a mode was used by any customer, even if they had already used that mode, this approach was rejected because it would lead Quizlet to collect far more data than needed for the campaign.

The engineering team at Quizlet took an agile, collaborative approach when determining their next steps, turning to their marketing team for further help on optimizing data collection. By bouncing ideas off of each other, Quizlet landed on using webhooks to effectively address their data issue.