Plan Prediction Services

Any intelligent fool can make things bigger and more complex.  It takes a touch of genius - and a lot of courage, to move in the opposite direction.      Albert Einstein

Our app

Planticipate predicts how different demographic groups will rate each plan.


Earlier prototypes correctly predicted people’s top-ranked plan at least 70% of the time, and the current version should improve upon this.

It is a rare application of scientific rigor to the intensely human process of people-oriented policy making - it is:

(A)  Self improving:

Every app user is asked to consider some “situation”, comprising a client, a goal and between two and five alternative plans for achieving that goal. 

Their chosen situation can be either one of the examples embedded within the app, or a new situation entered by the app user.

App users then score their situation's plans, both for twelve, key plan-evaluation criteria and for overall desirability, and their scores are automatically sent to the cloud where they are closely protected and NEVER revealed to any third party (Privacy statement).

This enables our cloud-based dataset to slowly accumulate knowledge about relationships between plans’ criterion scores and their overall desirability.  Since every app user has also supplied their demographic details, these relationships are automatically updated for every demographic category to which the user belongs.

It follows that in ANY subsequent situation, provided that you first score alternative plans on the twelve criteria,  the apps will forecast each plan’s overall desirability according to up to 93 different demographic groups. 

The more people that use our app the more accurate it should become - it is self-improving.

(B)  Self-monitoring:

Our app graphically locates its plan-desirability forecasts within margins of error.  This makes it obvious which forecasts are statistically significantly different from each other – if two plans' error margins do not overlap they are not forecast to be significantly different from each other in terms of overall desirability.  

The app also calculates Bayesian probabilities that its plan predictions are correct – it is self-monitoring.

(C)  Self-explanatory:

Our app always suggest in graphic form why its forecasts turned out the way they did.  It uses our innovative 'face charts' method which is demonstrably superior for clarifying multi-criteria data. 

More exactly, a face chart showing the priority placed upon each criterion by the relevant category of people is set alongside all plans' face charts that show how each one scored on each criterion.  This highlights how well, or otherwise each plan scored on each statistically meaningful criterion.

What now?

To monitor whether Planticipate's accuracy is increasing, you should intermittently RUN IT  and, if you think we could improve the empathy of your forward planning, you should let us know by e-mail: