How Life360 Cut Analysis Time by 75% and Unlocked $35M Revenue with Scalable Data Infrastructure
About Life360
Life360 is a leading family safety app with over 50 million active users, providing location sharing, driving safety features, and emergency assistance. They have been partnered with Aryng since 2020 to execute data science, analytics, and experimentation projects on lifecycle marketing, product, and growth hacking use cases with an impact greater than $13 million.
Nicholas Goffeney
Director - Product & Marketing Analytics, Life360
"I couldn't have asked for a better partner than Aryng. They added bandwidth, velocity, and analytical excellence to my team at Life360, helping us achieve specific business outcomes. Aryng's ability to provide additional resources and thought leadership, and track record of delivering positive business impact make them invaluable consulting partners.
Their expertise helped us design campaign experiments and rework data to allow for more intricate analysis, leading to specific business outcomes. Life360 now has dashboards, KPI definitions, and analytics that it has never had before in its established history.
Their consultants are strong performers who can operate independently and work directly with important cross-functional partners, which was extremely helpful for me.
I highly recommend Aryng for their strong consultants who are consistently available during US hours and their promising thought leadership consulting for leaders seeking reliable and effective consulting services for data engineering and beyond.”
PROBLEMS
Life360, a family safety app, was struggling to track and analyze its net subscribers on a daily basis. This was hindering strategic decision-making, leaving marketing and product teams without the insights needed to optimize campaigns or address real-time issues.
- Visibility: Life360 wanted to understand different subscriber segments, including new, returning, and churned, and existing subscribers. This was vital for analyzing how each of the segments affected changes in net subscription.
- Insights: Various teams required real-time subscription insights into surges and declines in subscriber growth to assess the performance of their efforts and track issues.
CHALLENGES
- Complexity:
With around 1.8 million daily subscribers,
Life360 was dealing with highly complex subscription
segmentation. Subscribers needed to be categorized by:
- Segments, including new, returning, churned, and existing subscribers.
- Tiers, which included Silver, Gold, and Platinum.
- Geography, as they were spread across 250 countries.
- Payment sources, such as Apply Pay, credit card, etc.
- Subscription duration, including monthly and yearly durations.
- Scale: As a result of all the categories, billions of records that required tracking and analyzing exploded to an unprecedented scale of over a trillion records very quickly.
This staggeringly massive and complex dataset was presenting a significant challenge that required a robust and scalable SaaS data pipeline capable of high-volume data analysis.
PROCESS
Aryng used its proprietary BADIR framework to create the required data infrastructure. The entire process took half the expected time, thanks to the efficiency of the framework which minimized the need for iterations
- Business Problem Identification: Identified the core business problem to be the need for the ability to track and measure the drivers of net subscriber growth or decline to empower stakeholders to leverage opportunities for optimizing subscription growth.
- Analysis Plan Creation: Developed a thorough plan to identify the key segments to track in the new data infrastructure. The plan included definition of the metrics, various data engineering best practices such as designing of the data pipeline according to the identified use cases, and alignment with several stakeholders on timelines and deliverables.
- Data Collection: Gathered raw data from various tables, using AWS S3 for storage and AWS Athena for querying.
- Data Engineering and Testing: Developed Python scripts to create intermediate tables, followed by SQL queries for refinement. Tested these tables rigorously for accuracy before passing them to the data engineering team. Automated data transformation at scale with AWS Glue.
- Implementation and Validation: Tested and refined the tables and data pipelines before moving them to production. Used real-time data visualization with Tableau to create intuitive dashboards for end users.
SOLUTION
Aryng’s team used their expertise to build a robust data infrastructure that aggregated key subscriber metrics. It was designed to be a future-proof data solution that could adapt seamlessly to new subscriber categories or regional expansion, ensuring scalability for future growth.
- Flexibility: The aggregated table required the creation of several intermediate tables and this entire structure served as the backbone of a customizable data pipeline. By making the intermediate tables available to other teams, they could create custom reports based on their unique needs.
- Scalability: The resulting data infrastructure was scalable and adaptable, with the ability to handle future growth and new subscriber segments seamlessly.
IMPACT
The infrastructure benefitted not only the leadership team but also the product, marketing, and finance teams, enabling faster and more accurate data insights that drove business growth across the board.
- 75% Reduction in Analysis Time: Aryng reduced analysis time by 75% by streamlining the data collection, transformation, and analysis process. For example, if measuring subscriber churn impact took 6 hours pre-implementation, it only took 1.5 hours post-implementation, allowing the marketing team to act faster.
- $35M in Unlockable Revenue:
The new infrastructure
unlocked new business opportunities that increased data-driven
decision-making ROI, including:
- A major experiment generating $25M in revenue.
- A churn analysis project with $1M in projected impact.
- Another churn-related project that drove an additional $2M in revenue.
- Single Source of Truth: It also eliminated data silos and inconsistencies, ensuring all teams worked with the same accurate information, minimizing bottlenecks and errors, and driving smarter and more profitable decisions.
Want to reduce analysis time and unlock hidden revenue?
Let us help you build a robust, flexible, and scalable data infrastructure that empowers your teams to make faster, smarter decisions.
