Cohort-based forecasting platform built for finance teams at B2C companies.
Watch how we do what we do
High forecasting accuracy
Connected and kept up to date
Rapidly run complex scenarios
Get up and running within 48 hours
Accurate user and revenue forecasting.
No more missed targets. Ramp is a web-based application that increases the speed and accuracy of your forecasting process.
Based on a method called cohort-based forecasting, Ramp uses the latest data science and ML techniques and is constantly back-tested for accuracy.
Rapid scenario creation.
Our solution makes it easy to create budget scenarios are grounded in reality. Users can easily tweak forecasts by making changes to core input assumptions.
For example, you can assume future improvements to your product or service based on your roadmap.
Automated reporting for increased visibility.
Ramp’s forecasting is updated with the latest data every day. This enables you to more quickly and precisely see performance versus budget but also the reasons for any change.
Reporting can be accessed via our web application, your own visualisation system or delivered to you every day via email or Slack.
Forecast the impact of sales events and live operations.
Some services are highly dependent on live operations and events to drive performance.
Ramp allows you to easily "tag" past events to analyse their past performance and use this as the basis to plan similar events in your forecast going forward.
CPA target setting and ROAS monitoring.
Our platform has eliminated millions in wasteful marketing dollars and just as importantly corrected underspend.
Ramp calculates the CPA you can afford for highly granular customer segments and can push these targets to media buyers.
The platform finance and marketing with visibility of the predicted ROAS and payback time in months.
Find the optimum of marketing spend.
Ramp is the world’s first tool to connect marketing spend to top line revenue impact. We simulate both the diminishing returns of increased marketing spend and decreasing quality that typically results.
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
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