NICK MIHAILOVSKI: The main premise of analytics is that
traditionally, the way that we measure success events is by
looking at different clicks.
You get cookied.
You come back the site multiple plot times and you
get a conversion.
But when you take a look at the click and you look at an
organization from larger picture, take a step back, you
see that clicks to the site is only a very small amount of
traffic that's coming there.
Rather, when you take a step back, you're saying people
were engaging with content outside of your site.
They're looking at videos online.
They're looking at banners.
They're looking at other display media and so forth.
When you go back another step.
you see that there are also a lot of other touch points that
are happening as well from TV, print, radio, events, PR and
so forth as well.
When we take a step back even further, you're starting to
try to understand what's the word of mouth.
How are people talking about my brand outside of any of the
media that we're putting in there?
With social networking and the communication online, people
are talking about, is this the right car for me to buy?
Is this the right medicine for me to be taking
based upon my illnesses?
And so forth.
So if we're only looking at clicks to the site, we're
really missing out a bigger picture of measuring what are
the other impacts of all these other media together.
And so instead of looking at the traditional way, it's very
good to take a look at how many people click and convert
and so forth.
We spend a lot of time looking at, can we correlate different
activities, different market inputs with a bunch of
different responses?
In this case, what it translates to, instead of
looking at a click and being stored with a cookie and
converting, we're looking at total number of clicks over
time, total conversions or revenue over time, and
layering on the different market inputs.
So with this example, it's more of a framework.
Then we can apply and layer on different media.
So in this case, it would be we're having search, we want
to layer on audio and see what's the
incremental impact of audio.
And not just between the two media, but the interactions
between the two media.
So again this brings out a new type of framework where we can
start to measure a lot of different inputs happening
from all over the web to a lot of different response metrics.
In the case that we'll show here, some of them are
conversions, some of them are revenue, some of them are
traffic to the site.
But again, these response metrics could take the shape
of anything.
They could be how many people came into our store?
What's our offline revenue?
How does online and offline revenue for a large retailer
correlate together as we're doing different marketing
impact or marketing inputs into different geolocations
and so forth?
So with this framework, we went out, we talked to a bunch
of different clients and said, how could we start applying
this together?
And try to see if this even makes sense.
And we found some really interesting results that I
wanted to share with you guys here today.
We started looking at this about a year and a half ago.
The first study is CBCampus.
CBCampus is a part of the careerbuilder.com sites.
It's geared towards collegiate students.
And one of the springs, they went over to 50 campuses and
had a bunch of different campaigns.
They had a lot of different events that they did there.
Some of them were doing career fairs.
Other ones were speaking engagements.
Others were in-class presentations.
And so what we did is said, look, if you're going to these
different sites or these different campuses, each of
them have an IP address.
We can map the IP address with our network location report.
And we could see how much traffic's coming from the
individual campuses.
We then took a look at all the different event marketing that
they did, and look at before and after from the time that
they did it.
And we aggregated them together.
And we got a chart that mapped from 30 days
before to 30 days after.
What was the percentages lift in traffic because they had
these different events?
And it was quite interesting, because C did really well, B
did well, F and G pretty good.
A, D, E, H not so much.
And so when we just presented this, they took that back and
said wow, this is great.
Now we know what to spend our time in all these offline
event marketing, because we're able to look at
the response online.
The interesting thing was C had such a really high spike.
And again, this is when they first told us how they ran
their campaigns.
We just did the analysis 30 days before and 30 days after.
So we went back and said look, what are you
guys doing with C?
This is really great.
Actually, it turns out it wasn't just a specific event.
Rather, when we charted the data over time, and we looked
at the web visits coming to the site and then we overlaid
the media plan against it, we saw was there's actually a
couple of events.
There was three events that happened.
The first was a print ad saying come to a speaking
engagement.
The second one was another similar print ad that they ran
the next week.
And the third one was the final campus event.
And so we took a look at this and we're like OK, so this is
why it was such a high lift, because there was a lot more
going on in the market.
But something really interesting to take a look at
here is that we put a baseline for 30 days before any sort of
marketing happened within these campuses.
We took a look at the first response, which
had a little spike.
The second one.
Of course, when people came for the event, it drove a lot
of traffic to the site.
What was really interesting is that there was actually a
sustained lift afterwards.
So not only are they coming to the site and there's a little
spike, that people continuously came back to the
CBCampus site over time because of
the successful campaign.
Again, this is really easy to do.
This is just looking at visits over time, trying to tie it to
a location that these events happened.
And then overlaying what happened in the media, similar
to what we did before.
So for them this was really successful.
It was a great study in understanding how we can use
online metrics to drive offline decisions of where we
should do event marketing.
The second study that we did was with our
Google audio platform.
And again, you can do this with any audio platform.
It's easier for us to do it because we have
all the data together.
A pharmaceutical company was selling a pain
medicine to help pain.
And what they did is they ran a radio campaign in over 50
markets for about 7 different weeks.
They had two different airtime spots and with
different calls to action.
One was to call an 800 number.
Another one was to go to the unique URL.
It was 4painx.com.
And so using this correlation technique--
it's nothing new, just applied in a different perspective--
we took a look at the total number of impressions.
And so these are calculated as listeners per
average quarter hour.
So for every 15 minutes, we use Arbitron data to estimate,
based upon the time of day, how many people actually
listen to it.
So we were able to plot this data over time.
And we took a look at traffic from the unique URL.
It correlates very closely with impressions.
So when people heard the ad go to this unique URL, people
were actually going to the unique URL.
What was really interesting is we start taking a look at some
of the other metrics that people could respond to.
When we looked at direct traffic to the site, it
corresponded very closely, again, with the same peaks
that we had from when the audio played.
What was even more interesting is when we looked at branded
search terms-- so this was a search term around a company
name, the product that they were doing, any cues that we
could hear in the audio ad when we listened
to it in the future--
when we looked back at it, we saw that there was also a
correlation with search.
So here is a campaign driving people to go to a unique URL.
We're able to look at spill over into other different
mediums that people were responding to.
I think this is really interesting.
If you're doing online marketing, and your company's
only spent what, 10% on online marketing, and then you have
your offline team doing big TV and radio spots, you should
probably take a look at how is that impacting
your site as well.
Because not all your traffic is just coming from your
advertising.
Are there some sort of a correlation between the two.
You cam see after they ran the ads, traffic to the site
declined significantly.
And so beyond that, because radio is very localized, we
took also a look at the where the traffic was coming from.
And when we looked afterwards, after the radio ad played, we
saw that there's a lot more cities that traffic came to
the site as well, a 270% increase in traffic.
So again, this is offline advertising
driving online response.
Here's another study that we did with Blue Nile, a really
large online jeweler.
This is supposed to be a nice image of their ad creative.
I'll just talk you through it.
I've been having some computer troubles.
I don't know if you saw earlier.
And so what the image was, was it was ran through a bunch of
different markets and newspapers.
And the call to action was go to Google, search
for Blue Nile 100.
It was a unique key word.
I just did a search this morning.
Again, they come up as number one here, and they give you
discount, save $100 bucks if you use this key term.
Nobody else is bidding on it, because why would anybody bid
on this unique key term?
But it gave them a mechanism to measure the response when
they ran this ad in all these papers.
So again, we took a look at the correlation between these.
And in this case, the blue line represents the total
number of ad impressions, so people actually going online
and searching.
The red is the number of people clicking.
And then the green is product views as well.
And so we charted that out, and it correlates very
closely, as you see, with all the dates that the print
advertising ran.
So this is a company that
traditionally, they're all online.
They're a very large jeweler.
They spend most of their budget
online for online marketing.
And they were doing this test with print.
And all of a sudden, they're like, oh wow, that's great.
And we're able to drive new traffic to our site.
And some of the things that came out was like the
correlation with the total number of papers, so six
papers, or six markets that they had, had a
higher spike than four.
So again, they were able to better understand what the
impact of that advertising is.
They also did a PR campaign.
And my computer also had a pretty graph of that.
You can just imagine there that happened.
But just imagine if I did my finger, what it was, was a
graph of traffic here.
And then it came up and spiked right here.
And although I don't have any of the graph and prettiness
that you can take a look at there, I think the most
important part of this is, yes, there was a spike in this
graph between the two times.
But more importantly, they were able to correlate this PR
impact in The New York Times to x million of revenue.
So now they're not only looking at business and
traffic to the site, but they're actually saying this
PR release drove revenue to our site that was incremental
if we didn't have this PR in The New York Times.
Unfortunately, you can't see it, but again, this is a
simple graph that we pull out of analytics-- you use it with
any product, too--
but is bottom line revenue that they were able to
correlate to that as well.
It was really interesting.
So the final example I have--
and I know I'm running through this quick.
This took a lot of time to analyze and put
all the data together.
But I think it's pretty interesting
how you can do this.
It's a white labeled account where it's a very well known
OEM automobile dealership--
or not dealership, I should say, OEM.
They showed up number one in all the organic results.
And our sales team was like, whoa, you should buy keywords
to drive more people to go to you dealerships and so forth.
And they were really hesitant.
Said OK, we'll put together a simple test or we will run our
campaigns as such where we'll pulse on and off our different
paid campaigns to see what impact does
it have on the market.
Again, these are different intensities of how much they
spent as well.
And so when we took a look at the total number of
conversions and plotted them, instead of just looking at
offline how it impacts online, we did a hybrid approach here
of looking at the correlation methodology here.
We see that the red correlates very closely to the same type
of input that they put in for paid search.
We also overlay organic on there as traffic as well.
We saw that, over time, there was a little bit
cannibalization of the organic.
But not so much as how much incremental clicks they got to
actually convert to go to dealer.
So when you're looking at that, you're OK, well, they're
not taking away from organic clicks.
Where are they getting their clicks from?
And so when we look back at it, they're actually competing
against the other people in the same auction.
So they'll have like an edmunds.com, go here to learn
about this brand.
Or Consumer Reports, go here to learn about a car.
And so instead of sending traffic to those other sites,
which doesn't get to their main objective of getting
people to find a dealer and to go test drive a car, they're
actually taking those clicks and bringing them back to
their site and people are actually signing up.
And so for them, buying these keywords incrementally,
although over time, potentially, it decreases a
little bit of organic, for the most part, overall they're
getting new clicks that they wouldn't have been getting
from paid search.
I think just, in thoughts, with the web becoming a
centralized communication channel, any of these external
inputs you're just going to start to be able to measure
them, whether beyond site activity, outside of site
activity as well, and definitely understanding what
are the market inputs that you're putting in there.
If you guys are strictly online, but have an offline
team, making sure that you guys are communicating
together to understand what relationship marketing things
are you guys doing, because it's definitely impacting my
numbers on my site.
People are coming to my site from the
email you've sent out.
You're running a huge TV spot.
Of course people are going to be searching for the
brand and so forth.
You did a great Golden Globes or Emmy sponsorship, people
are going to be searching for the brand, trying to find out
more information.
And with the web, people will turn to the web to get more
information.
We're going to start to be able to look at some of these
responses in more of a real time.