Re: 5 year projections?
- While it is true that nonlinear models are more susceptible to
unstable behavior in models that extrapolate, this doesn't need to be
the case if care is taken in building and deploying the models. Some
model types extrapolate more conservatively than others (linear models
for example), but even with wildly nonlinear models, you can prevent
them from behaving badly by restricting the extent of their
extrapolation--this is a topic that would take more time to develop
But even this, in my opinion, misses the real issue. Any quantitative
decision-making process (business rules, rules of thumb, predictive
models) take observed data and turns them into predictions. That is
why the most important step with any quantitative analysis is the
validation of the model/rules/guesses. Validation can be done from
data that is set aside purely for validation, by simulation, by
inspection by those who know the domain area. If the predictive model,
then, finds interesting predictor variables and is not overfit, and
validates well, there is no reason not to use it. (It may be that it
isn't worth the investment in time and resources to build the model to
begin with, but that is entirely another question).
- Man, am I with you on that. It drives me crazy, and this will
definitely be part of the doc I send to them. Thanks for the backup!
--- In email@example.com, "Debbie Pascoe" <dpascoe@...> wrote:
> I'm fascinated with the phrasing - "5 year goals for website growth" -
> this assumes that growth, not stagnation or deterioration, will occur.
> Redesign and hiring of a full-time editor is no guarantee of
> improvement. As an example, I reviewed a site just last week that has
> been recently redesigned and relaunched, using .net technology. The
> way this site has been designed has made all but the first page
> invisible to Google. To get visibility for their content, they will
> have to pay for it, losing the cost advantage derived from good
> natural search placement.
> Any assumption of increased traffic can not be made without taking
> into account site structural and quality aspects, privacy,
> accessibility (an increasingly important issue), and utilization of
> techniques and technogies that result in good natural search
> placement. All these issues can impact the ability of the site to
> help you meet the ultimate objective - raise more money. On- and
> off-line activities can drive more people to the site, but if the
> experience is bad, and donations do not increase, the objective will
> not have been achieved.
> The question seems to imply that simply having more visitors is the
> objective....it has been my experience that when somebody asks you a
> question like this one, and you answer it, you will get to answer FOR
> it later :-)
- Wow, I've finally been able to go over all of your comments and I have
to say, I'm overwhelmed and in a bit of a jam. Last June we started
using GA (previously we were using WebTrends, which overinflated our
numbers due to poor configuration). So, this means we really only have
less than a years worth of web data to go on and any sort of
predictive modeling will be tricky, no? I can do my best with what I
have, but as someone who has never undertaken a task like this, my gut
says to use the data from the past year and try to project over the
next five years assuming everything is the same (which as we all said,
will definitely not be) and make it clear that this data is bogus.
Stephen, I'm definitely with you on making this look less scientific
for fear that they may take it more seriously.
I'll let you know how it goes!
--- In firstname.lastname@example.org, "Stephen Turner"
> -- In email@example.com, MM <bristolnational@> wrote:
> > Effectively, you are getting involved in a very interesting project
> > around Predictive Modeling and Predective Analytics. What you need
> > first and foremost is the predictors, or facts that are likely to
> > influence your traffic. For example, depending on your site, the
> > demographics and siteographics (like Gender, Age, Purchase History,
> > Online Campaigns etc). Hopefully, you will be able to capture a
> > majority or some of these predictors and come up with a llinear
> > equation around the behavior. We have independently worked on a
> > similar modeling methdology for a B2C client and were able to come
> > up with a model (using linear, logarithmic, quadratic, and
> > exponential functions) ofcourse factoring seasonality (christmas,
> > halloween), key media events (anagelina's baby, cricket world cup)
> > etc. to stabilise the data points. The point is, it can be done,
> > once you have your factors established it is a matter of having some
> > data mining people rolling up their sleeves and getting it done.
> Maybe I'm just ignorant about the power of these methods, but I have
> to say, I would be very skeptical about using any such high-powered
> mathematical models in this sort of problem. Using them for a
> short-term estimate is one thing, but for a long-term estimate the
> inputs to the equations are so much guesswork that the outputs are
> going to be complete guesswork too. Especially if you have quadratic
> and exponential functions in there, your guesses are going to be
> multiplied upon guesses.
> The problem is, the more sophisticated the mathematical model (1) the
> more wrong it's likely to be given slightly wrong inputs; but (2) the
> more people are likely to trust it because it looks clever. If you use
> rough estimates of the sort described earlier in this thread, I think
> you are at least as likely to get good answers, and as a bonus people
> will have a good feeling for how wrong the answers might be.
> Stephen Turner
> CTO, ClickTracks http://www.clicktracks.com/