ladies and gentlemen Sean from sgb
analytics I have an interesting story to
discuss today and it specifically
revolves around target and kind of what
they've been doing they've always been a
very proactive company with their data
and how they analyze it and specifically
they made a data mining model that we're
gonna talk about today this was back in
2012 so not the most recent case however
it was something that kind of cell set a
bell curve for these types of models and
for this type of thing and it was
actually pretty in the infancy of its
time and pretty early on but definitely
something I want to bring up because it
leads to these types of discussions that
later happened and later on it's these
types of things we'll talk about false
negatives and false positives and all
that kind of stuff and but this one was
very interesting and really set the set
the trend if you will so what happened
with target back in early 2012 was they
started to understand that there were
trends with what females were buying and
if they were pregnant so for example
they essentially found out after
collecting enough data that people some
seem to end up being pregnant after
buying this certain lotion or they had a
high probability of being pregnant if
they were purchasing this lotion or if
they were purchasing this rug or this
color rug and I think at one point they
were establishing that the color of the
rug actually a lot of times was able to
predict the the gender of the child or
the sex of the child child as they come
out and so that's very interesting and
something that I would love to know more
about but nonetheless so they had this
model rain but they were using this
model for was to get ahead of it so a
lot of times they would know this before
the expecting mother or non expecting
mother in this case before they knew
that they were pregnant and the reason
for that is to send the advertisement
they find out they're pregnant and it's
already relevant in their mind as all
this is happening so a very smart idea
very you know intelligent and and
something that was well ahead of its
time they started doing that and you
know it seemed to be very very clear and
there there's certain times anytime you
have a predictive model we now know
after this
what to expect and specifically what you
should be most scared of and I'll
explain that more after I finish the
story but so what happened is they send
out these advertisements to people who
tested positive as a subjective of
pregnancy and this didn't really cause
any issues because no one knew that they
were like oh we're expecting to be
pregnant because this model or anything
like that no one knew that target was
doing this however there came
implications when they sent one out to a
high school student and obviously when
you're talking with underage that's
gonna upset some parents all that stuff
this one dad of this daughter was really
upset that target was sending his
daughter advertisements for baby stuff
formulas cribs all that the clothes so
he went to this to this target and you
know the Astra manager and freaked out
at the manager and all that stuff
thing was his he eventually called back
and said I apologize they were stuff
going on in my house that I didn't know
about yeah the idea and my daughter's
pregnant so the model was correct but
it's that ethic standpoint you know it
and when you're talking about when
you're talking about an underage or
anything like that
there's the ethics that comes behind the
data and I did target do anything wrong
no not not really not unless you know
you consider that necessarily overly bad
it's the idea that if you mess up what
what case are you most scared of are you
scared of being right and then you know
sending it out and then having the
implications are you are you worried
about being right and having and being
right in a sense to where they were
actually negative where they weren't the
case or in my opinion the worst case for
this one would be the missed opportunity
the false negatives because that means
that they were actually pregnant and you
missed an opportunity there to me that's
typically the one I'm most scared of
because I think anytime you get an
opportunity you got to reach out and get
it and this
case they were right and that's
fantastic but they had that they had the
straight backlash from it because they
sent it out and the person was young and
that that's an ethnic type of thing so
what everyone kind of learned from this
experiment everyone kind of learned from
this model is that you have to have the
most certain you you possibly can when
you're dealing with anything kind of
like a pregnancy something that's a
life-changing event something that's
important and something that is very
personal you have to have some type of
accuracy to your model that you feel
comfortable with as a company and this
may get a little too technical here but
there's things that you can test your
model and see how accurate they are like
an r-squared or an ROC curve things like
that can help you understand how
accurate your model is and so for
example if you're if you have an
r-squared of 0.9 that's fantastic you
want it to be closer to one I would say
go with that model however with
something so so lightly to tread on like
pregnancy I would say that it's got to
be high up there proper in my opinion
probably I could point eight five or
better because when you get to those
points six 0.55 becomes wishy-washy on
how accurate your data really is for
these kinds of things and you want to be
accurate if you're doing things that you
know it doesn't really matter you're
sending generic advertisements you know
the but the closer you get to one the
better but it's not gonna hurt anyone if
you if you don't get it so there's a lot
of things that were learned from this
experiment targets still obviously
pressing on still viewing a lot of data
stuff companies are now emulating that
model and using very similar models to
do very similar things but yes so that
was definitely a case where everyone
kind of learned something from it and
kind of set a curve so I want to share
that with everyone because not everyone
knows that story and I hope you learned
something if you think someone else
could learn from this or would be
interested in this and what targets
doing share it with them comment like
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thanks guys
