Acquisition is typically led by a data-driven cross-functional team that focuses on scale , measurability , and predictability .
REQUIREMENTS : 1 Ability to predict the likelihood of a new user to engage with our product .
2 Measurement mechanisms to allocate our marketing budgets across different internal and external channels .
3 Levers to deploy these budgets across thousands of ad campaigns .
Marketing performance data contributes to a feedback loop to constantly nourish the reinforcement-learning system .
Here are examples of problems we needed to automate :
• Updating bids across thousands of search keywords .
• Turning off poor-performing display creative .
• Changing referrals values by market .
• Identifying high-value user segments .
• Sharing learnings from different strategies across campaigns .
And so , we created Symphony — an orchestration system that takes a business objective , predicts future user value , allocates budget , and publishes that budget to drive new users to Lyft .
The Symphony architecture consists of three main components : lifetime value ( LTV ) forecaster , budget allocator , and bidders .
LTV FORECASTER BUDGET ALLOCATOR BIDDERS
Our tech stack comprises Apache Hive , Presto , an internal machine learning ( ML ) platform , Airf low , and third-party APIs . A light front-end feeds in business targets and launches creatives . The architecture has a lot of moving parts and dependencies and requires rigorous logging and monitoring . We dive deeper into each component below .
LIFETIME VALUE ( LTV ) FORECASTER
Understanding the potential value of a user is critical to every business . The goal of this component is to measure the efficiency of various acquisition channels based on the value of the users coming from those channels . Budget can then be allocated based on the expected value for users coming from a given channel and the price we are willing to pay in a particular region for those types of users .
PROJECTED NET REVENUE PER RIDE
DRIVER OR RIDER SPLIT
The above diagram portrays at a high level how we calculate a user ’ s expected LTV while accounting for supply and demand in our twoway marketplace . We try our best to predict LTV accurately , as it helps us set mid- to long-term strategic goals .