so i've typically you know as a retail executive rated this in arl
workspace rita workspace to dashboard
right edits it's because it is in the sixty and sixty degrees view of her
retail bank operations
pretzels completely self-service
web based
out like a civil context multi prizes what its historic data real-time data
plan predictive and it's a simple location awaited integrate the output
from odds analytic capabilities
so if we look at the seas key performance indicators i consistently
this major problem you're happy in the northeast region i dot speed ready to
cater
looking up profitability for the current period i can see that the northeast
region is indeed
uh... in danger of missing their numbers
so i can do something like drilling delicate job down on the northeast
region right down to the branch level
and what i do not i can see this one branch particular this bayside branch
which is severely underperforming right
so this is you know this chilling down or
filtering
g_g_ try searching to colleges are just examples of some of the basic reported
activity that's available to you in this workspace
you know what happens if the user wants to do more than this what happens if
they want to be some sort of deeper analysis you know me don't look at
products uh... by branch right
so for those more advance users we've got this convenient um... do more but
not every widgets offically commercial gear but here here
this transitions me it would only the browser into this more advanced offering
both i've got the concert reiterate working used occupied chart
and i've got his wife variety of analysis options
threats of this case i swear update us a look at products by branch
some scope of the cartagena find those those product lines and sky
drag-and-drop added to the chart know what you're gonna see here
if that's not opted to chart in real time
and now this updated i can click on the storm button gold transition back will
be the browse again back to my regular workspace road workin' uh... save that
for for future viewing
i'll for anyone else who might be consuming this workspace
so this is great read i could see pickups problem with the credit products
but it's kinda leads smeal more questions right so wise is happening
which they do about it
i could do a couple things like you'd look at saket jill down so the regency
how they're doing
i could look at
the work that is doing and look at the industry as a whole
what you think about a retail bank like in the eye
has so much customer data
exiting i know this mountain of customer data at their disposal but when you take
that customer data you combine it with ibm business analytics
uh... predictive and statistical
capabilities
we could turn back history data into meaningful inside
so me show you how we're gonna do that
well how in the eye does that
so that's the second happier
uh... the first or did you go because we've identified early so we have a
problem bayside breaks wide open up the squabble filter window
i'm a select the base that branch of what this is going to do this is going
to filter the entire workspace
just on that bridge
so now which is looking on the left once that's done
i concede the
predicted profitability
for the base of threats at look at bat bhi
our household level
the in the the individual level or even the individual account level right
this is the output of predictive models of the model stop it's the output double
b_ and c_ here is that this model is built on customer data
uh... you know things like they're they're aged other sorsa dot the
graphics
were mining that data looking for patterns using these statistical
clustering competence and here we get this output
sony like it might sound complicated but in reality it it really is a kiss
the designed in such a way that it's been mid-may easy to use for your
typical business user or business analyst radio need a p_h_d_ in
mathematics race or fancy degree to get the do this
and this is just one application one example recipe for the possibilities
you know you could take out a matching you could he could predict
alma customers
propensity for cross also offers
or a customer's propensity to charity at a_b_c_ things like that then you combine
ahead
we've
um... business rules
and you could make use of ideas decision met out analytical decision management
capabilities
then you have the ability to deliver optimized realtime decisions right so so
passionate we never never never every big has our website a magic us for a
logging in and when the login there immediately presented
with an offer a such as any offer
it's the offer that you know that they're likely to accept
an offer that span optimize to maximize your profitability right that's pretty
cool
so this time so they can do with this is individual
this this individual information that that that somebody's predicted in
uh... molly capability right but they still
i need to look sometimes how more agar at levels at the customer
um... i'd review of the customer so for example i could take the same customers
data
apply different set of predictive
algorithms and i could determine um... customer different esp segments a
logical division of my customers and what that's gonna do it and how we
better understand better serve them
bliss case titled in our model we've we discovered three different esme segments
looking down pat
um... profitability by credit law i a product line for these
um... free said as i can see that the mass market segment is uh... is
performing
proportionately much lower than it should be
check until debits that here look at it right at the individual product level
exit off route one product in particular either basic wrecker products
that are severely other performing
or so
so let me look let's just do something about that when we transition over to
the third tap your do a little bit of profit billy modeling optimization
unless you would consider income statement right from the thought of
previous period
and already it's i've got this interest rate table
that's why why do so to figure out what i could decrease the secret crepe record
interest rate
uh... what effect might that not right so i can do that and find an multiple
scenarios i can't compare the sinners
uh... and i can do that using these things because sam boxes or which are
all for it will work on a serious here
already have a so much for your call decrease interest costs like that
and comical head of a change that interest rate for that basic redeker
product from twenty one point nine percent to ten percent right and so i
said the u_s_ what i'm doing this is this is writing back into the model
and what i do that i can
update that apply that change
let me move or to being from statement
let me retaliated but said he could see in real time it's updated to show me the
effect of that change
but i think it's a positive fact on are
net income packing up i've actually see it
all the different values that were affected by changes at all they all
turned blue
wetzel uh... at this point what to do you like it
create more scary as a more sam boxes i could be deferred interest rate changes
and then i could compare
these diverse am boxes of scenarios and an image that she was the one um... that
i think is going to see it our business these bats