RAPP - Retention and Churn App - How to use
RAPP is an analytics tool for e-commerce businesses based on mathematical models.
#How does RAPP work?
RAPP analyzes millions of data points and gives you easy-to-understand graphs and tables to compare products, subscription periods and analyze users retention.
#What is RAPP:
Find important subscription-related metrics on one place:
Users Churn Rate
Users Retention Rates and Graphs
Product and User Lifetime Value (LTV)
Average order Value (AOV)
New Users Count
Month over month Users Growth
#Why do I need RAPP?
The most important metrics to achieve sustainable, profitable and scalable growth are customer lifetime value (LTV), churn rate and retention rate. Yet those metrics are one of the hardest to analyze the e-commerce store.
RAPP offers the most precise, clean, and correct way to analyze those metrics and understand your product ecosystem.
Identify opportunities for customer acquisition cost decrease, revenue increase and retention improvement.
#What can I do with RAPP?
Using RAPP you can:
Compare product AOV, LTV, Churn and Retention
Identify top products
Understand your weekly and monthly churn rates
Calculate correctly cost per acquisition (CPA) and customer acquisition cost (CAC)
Distinct recurring revenue and first-purchase revenue
Calculate conversion rate correctly on a subscription-based store
Get a detailed picture of which products are most profitable and which are hurting your bottom line
Maximize the value of your product with advanced data analytics designed specifically for online retailers
Quickly act on opportunities to allocate more ad spend to high-value products
Get a clearer picture of how to improve performance and increase gross profit
Discover which products and product variants generate the highest customer lifetime value and have the lowest customer acquisition cost
Receive and act on valuable insights regarding customer purchase behavior, lifetime value, and more
Optimize marketing spend for profitability with advanced customer analytics at multiple levels of granularity
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