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Probably the best way to think of PropheZy in action is to look at an
application. We have numerous ones, but predicting television audiences is a
relatively easy example to understand. Our data comes from Peaktime, a French
company, who are sort of the "Reuters" of television data in Europe. The data
covered 1 December 2002 to 15 December 2002 06.00-24.00 on a day-by-day basis
for UK television channels BBC1, BBC2, ITV1, Channel 4, Five and Sky 1. The
available columns were Channel, Title, Date, Time of Broadcast, Duration, Genre, TVR (televisualrating), Audience in 000's and Share %.
We started prototyping predictors
one afternoon. We built four different predictors from
the data that afternoon. Each predictor used a
different prime dimension:
-
audience numbers;
-
broadcast time;
-
channel;
-
genre.
Each predictor tries to predict "audience share
%". Naturally the desired predictor can change. Audience numbers is not a particularly good prime
dimension, as you'll see later, unless we do some analytical work.
The predictors work in Excel. For instance, having built the predictor
and saying you want the most confidence about "broadcast
time" you can then alter one or more parameters and get the new predicted
audience share %. You can partially supply data and get that filled in, rather
than audience share %.
Four output sheets from each of the four predictors
are attached. For the output sheets we took 8
programmes - Bagpuss, Breakfast, Animal Hospital, As Time Goes By, Arrest and
Trial, Art Now, Cash in the Attic and Casualty - and
used them to play with the predictors. You will see that the four output sheets
are broadly in agreement, indicating that the data is
probably quite good for this type of application. For ease of reference:
- black text comes from Peaktime;
- blue text are numbers we altered;
- red text is predicted by PropheZy.
Peaktime Forecasts - BCastTime as Class.xls
Peaktime Forecasts - Channel ID as Class.xls
Peaktime Forecasts - Genre ID as Class.xls
Peaktime Forecasts - Audience Number as Class.xls
Taking each programme in turn:
- Bagpuss - we used this to predict itself
, thus no blue numbers
- perfect;
Breakfast - we changed the day of broadcast from Sunday to Monday -
decreased share from 8.2% to 7.6%, except when audience numbers was the
prime dimension where share went up to 11.7%;
Animal Hospital - we changed the day of broadcast from Wednesday to Sunday
- share increased from 4.6% to 7.05%, except when audience numbers was the
prime dimension where share went down to 6.65%;
As Time Goes By - we changed show time from 14:45 to 14:00 - share went up
from 9.4% to 10.5%;
Arrest and Trial - we didn't fill in the channel and PropheZy rightly
predicted Channel 5;
Art Now - we changed the broadcast time from 17:11 to peak time 21:00 -
share went up from 11.9% to 18.05%, except when audience numbers was the prime
dimension where it hit 24.9%;
Cash in the Attic - we moved from BBC1 to C4 - was 40.4% and then
decreased markedly to 4.8%;
Casualty - like Bagpuss, a direct test - hit 26.0% accurately.
However, Excel is really just the development environment. You can build an
HTML front-end to interrogate your model in a few minutes and roll it out
globally across the internet. We have built numerous predictors
- straight through processing exceptions for investment bank trading, also
very similar to anti-money-laundering applications;
- trading price anomalies to benchmark against quantitative trading units;
- price-your-audit for accountancy firms and finance
directors;
- customers likely to buy or prospects worth mailshots;
- data cleansing (using PropheZy to fill in blanks in data);
- credit risk predictions;
- reserve level requirements;
- grant-giving success;
- medical analytics;
- performance measurement setting (PropheZy can predict what you "ought" to
achieve despite variables such as geography, assets, input quality) for
property managers and, full circle, television producers.
Anyway, this gives you some background to open up discussions with Z/Yen! |
