Ramp empowers finance teams with data science via our revenue forecasting web application.

Our approach is cohort-based forecasting on steroids. It makes the forecasting process easier, faster, and more accurate.

Our app makes cohort based forecasting easy

High forecasting accuracy

CLV prediction

ROAS analysis

Goal seek + scenario planning

How Ramp is more accurate

Higher temporal resolution

Cohorts are calculated on a daily basis - makes trends easier to spot and predictions more accurate

Multi segment approach

Discrete customer segments (eg. geographic /demographic) are modelled independently - This detects and predicts underlying shifts in the customer base

Full cohortised retention & monetisation

Every daily cohort, for every segment follows an independent ”retention" curve and independent ”monetisation curve" - increasing model sensitivity to near term changes

Cohort cooling and extrapolation

We detect and project how subsequent cohorts (typically) degrade over time

Machine learning applied to inputs

We can apply machine learning to inputs - the primary use case of which is to simulate seasonal effects or sale events

Proven methodology

Our team deployed our proven methodology at over 20 organizations in eCommerce, gaming, and fintech. Select clients include:

client
client

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