See how Analytics, Product and Growth teams leverage Kubit’s superior architecture to measure the full lifecycle of their users by integrating behavior data with their rich data model.
One social app customer fully unleashed the power of their attribution data with user behavior through Kubit. They are able to measure and compare the long term retention and engagement between different acquisition channels and campaigns. This is far beyond the previous practice of making spending decisions simply based on CPI/CPM metrics. They were able to quickly adapt to market shifts and acquire more valuable users with 9.64% higher ROI within three months.
One education customer's product team analyzed their users' onboarding funnel with Kubit and identified several friction points with high drop-offs. Leveraging Kubit’s ability to integrate with their internal A/B testing platform, as well as their machine learning generated user persona mapping, they managed to improve the "D1 retention rate" of their new users by 12.7%. Leading to millions of more engaged learners and hundreds of thousands in revenue.
Because Kubit's subscription covers all the computing cost, one e-commerce customer shifted 19.3% of their Business Intelligence operation costs to Kubit. Now their ad-hoc, self-service analyses don't burden their cloud data warehouse and data engineering team. Kubit offers unlimited seats and usage, and analysts don't need to hesitate on query costs when they need to investigate product issues.
One streaming customer sent out a large-scale push campaign with the wrong channel ID. Resulting in all users being taken to the fallback channel. There weren't any errors and all of their KPIs looked normal. The customer ran some ad-hoc queries to analyze the number of streams broken down by channels. Only Kubit made this level of detail possible by joining behavior events with their internal transaction data. This issue was caught within hours of launching the campaign saving them from spending hundreds of thousands of dollars on a mistake.