Forge Canvas Template: Product repurchase reminder (with timing and channel testing)
Hello fellow Braze orchestrators, I didn't make it to Forge in person this year, but it was great tuning in to the keynotes and catching up on all the latest Braze innovations. 🔥 Here is a quick Canvas that we are about to launch to drive repurchase of two of our products that customers need to by on a regular basis. Entry criteria: purchase of a certain SKU Experimentation (both set up as Personalized Path): Message timing (30 days vs 45 days after the purchase) Message channel (email vs SMS, in this case) Message sequence: A user gets a reminder to repurchase the product she might be running low on; If a user engages with the message from step 1, but does not purchase, he receives a reminder 3 days later with a special offer, to incentivize the purchase. Let me know if you see any opportunities for improvement. I imagine, soon we will be able to simplify this flow by leveraging all the upcoming Braze AI tools. Cheers, Olga Baker Charlie Banana / P&GForge Canvas Template: Frequency/Channel mix Experiment Setup”
This Canvas serves as a starting point for setting up an experiment aimed at sending specific sub-groups of a user population down the engagement journey that has the highest likelihood of converting them. The criteria that differentiate the journey in this Canvas are tied to two variables: 1. Frequency: n times over n periods depending on the use case, industry, and type of product. For example, n times per week for n weeks. Both the 'times' and 'periods' should be customized according to the use case. 2. Channel mix: combinations of at least 2 channels based on the use case, industry, and type of product. For example, a push notification followed by an email or an IAM followed by an email. The channels to test should be customized based on the use case. The goal here is to leverage Braze's personalization systems to take experiments to the next level: no more simple A/B tests where all users are sent down the engagement journey that converts the most overall. Instead, specific sub-groups of the population will be sent down different, specific paths based on their unique characteristics/preferences. This allows us to avoid a 'one size fits all' approach and instead create multiple journeys that users can experience based on what they prefer (and what tends to convert users like them better 😉 )." I’ve been using this setup for a while, and the uplift compared to a single experience for the entire population (chosen after multiple iterations of a classic A/B test) is significant. Let me know what you think and if you have any ideas for improving this setup!Question on testing for BlaBlaCar - Test and Learn AMA
GuendalinaIn the intro to this AMA session, I saw that you've been testing different "From" names in different markets. We've done this as well in multiple markets across Europe and North America. I'm curious to learn which types of names worked better than others, how many variations you tested and how long the tests lasted for.
