August 23, 2019
Back to Basics: Understanding Identity, Data, Attribution and Platforms

Back to Basics: Understanding Identity, Data, Attribution and Platforms


(upbeat music) – [Dave] Alright, welcome everyone. – [Carl] Hello. – Alright, this is a
back to basics session. If you were expecting
Will Smith to walk out, you’re in the wrong room. This is really broken up
into four different sessions today, so we’re gonna do
kind of 30 minute modules. They get better and
better, so I encourage you to stay on. They do fit together into a story, but they’re also broken
up into four components. I’m gonna take half of them and Carl’s gonna take the other half, so my name is Dave Scrim. I’ve been working with Conversant Epsilon for almost 10 years now,
and we do kind of identity and measurement and all
those kinds of things, database management. And prior to that, I used
to work for a company called Experience, some
of you guys might know, running databases for them. – I’m Carl Madaffari. I’ve been with Epsilon
Conversant for 19 years, actually this is my 19th year. I started off as a very technical person managing databases, system integrations, those types of things. In the last three or
four years, I’ve migrated to more of the marketing
side, helping tell our story to our clients. – Awesome, there’s four pillars
we’re gonna talk about today as I mentioned. We’re hearing a lot here at this session and they’re all cool stuff, right? Everything from voice
activation to AI to social media to addressable TV, but
really at the end of the day, you kind of need the
pillars and the fundamentals to drive that, so this is the
basics for some of those folks who are, who haven’t spent
as much time in the basics areas, and Carl and I have 20 years each experience in this space, so we can go as deep as
you want when we get to the Q and A, but we designed
this at a base level for the new users mostly. Also, goes without saying,
ask questions throughout. We have someone here who
can take your questions through the app. What’s it called? Slideos, Sledo? And he can, through the
conversation if you want, ask a question, or if you
want to save them till the end we’ll leave five minutes at
the end of each presentation. Alright. – Great, I’ll step aside.
– Thanks Carl. With that I’ll start with identity. Really one of the cornerstones
of what you want to do. And when we talk about identity, these are some players in the market. Signal, Neustar, Drawbridge,
Epsilon and Conversant, LiveRamp, just to get a
sense of this audience, how many people are
familiar with those names? Yeah, show of hands. Okay right, this is a
pretty good audience. You guys know what’s going on. So these are essential
people for managing identity. Now I’m gonna define identity. I might not have the perfect
answer to what identity is, but this is the definition
I’m gonna be using throughout the presentation. Identity resolution,
what do we mean by that? The ability to accurately,
super important word, and persistently, so over time, identify real people, not just cookies, not just devices, not email addresses, real people across channels. And then the last word, that
if any of you are trying to do this today, you
know is a big problem, doing that at scale, alright? And so, as we dive into it,
why is identity important? It really is the roots or the foundations to everything you’re doing. So if you’re doing targeted marketing and you bought into personalization and you know that you need
to get the right message to the right customer at the right time, well how do you know that
that’s the right customer? How do you know that you are
messaging the right person? What if you are getting
the right message to the wrong person at the right time? And so this why it’s so
fundamentally important, and then, if you’re gonna measure anything and you’re gonna do
close loop measurement, and you’re gonna try to
figure out what’s working and what’s not working, well how do I know that the
message I sent to this person is the person who converted,
maybe on a different device or different channel. And so do I know what’s working if I don’t have true identity? And so, we really think
it’s the cornerstone of what you’re doing, and if you did anything, first, try to optimize your
identity before you put a lot of special tools and
additional things on top of it. Why is it so hard? Why is identity so hard? Why is it identity so hard? When we are running direct mail campaigns and sending mail and postcards to people’s houses and catalogs, it wasn’t that hard. We had their name and address. When we were sending emails, we might not have known
exactly who the person was, but we had a place to send them. The problem is there’s no
post office for the internet. There’s no post office for digital, and so there’s no way to
look up the Dave Scrim is this ID. And so we spent a lot of time
trying to join the PII world, name and address and email,
with the non PII world. And what makes that super hard as well is we got to take people’s
privacy into account. We got to be super super privacy because we haven’t see anything
yet as far as it goes on taking control and helping the customer be privacy centric and take in
the consumer’s space in mind. So it’s really hard to
kind of manage this. One of the challenges is
cookies, which was kind of the ecosystem of the digital world, they break all the time. They break for all kinds
of different reasons. People have antivirus
packages that automatically delete them, you change device,
you change your browser, you go on a different device. The cookies are always breaking. In fact, up to 40% of cookies are gone within the first 24 hours. So how do you have accurate identity if you can’t connect a person to a cookie on a consistent basis? Another problem is what
we call clustering. And clustering is when
I try to overdue it. I can’t keep track of these cookies, so I’m gonna use something else. Gonna use a wifi address at my house. And I don’t know about you guys, but I have five people living in my home, and any given month, I have 25 people that have been on that wifi. And so that’s a real
challenge because a lot of companies are combining those people, clustering them and saying
they’re the same person. So then if I see an ad
in my home on my device, any one of those 25 people convert, and that’s called a conversion. And so you get into again,
you see the measurement trouble you get in. Another story on the
targeting side that that’s a problem with. There was a media company,
and they were launching one of their big, racy kind
of shows, an adult show. Not too racy, but you know, kind of in the HBO type framework. And so they pushed out
an ad that was designed for people who were 18 plus, and there wasn’t too much bad in it, but all of these kids all
of a sudden got that ad because they’ve all been
at the person’s house with the wifi. There was a lot of complaints, and so it just shows you
how audience targeting can be affected by the clustering as well. So how do we get around that? How do we match to real people? And there’s a few ways to do that, but I’m gonna try to go
through a simple method and really foolproof and
one that we stand behind. It’s called deterministic matching. And one of the simple ways to
do that is via transaction, because when people buy things, they want it to show
up to their real house. They want to use their real name when they’re buying something. And so they don’t use
some fake email address, they’re not just a cookie,
they’re not just a device ID, they’re giving you a name and address. And so what you do is, you take a customer order online and you associate that with a cookie. And so you have an order to a cookie, you have a match. Everyone does that day and night. But at the same time what
happens is that order goes through an offline
transaction, alright? It goes through the client,
call them Signet Jewelers, or call them Victoria’s
Secret, or Home Depot. And what happens is the
name and address go to that, and they strip that off and
they turn it into a number. And your onboarder strips
that number into a common number across all clients. So if I’m the Gap, Dave Scrim
turns into the same number. If I’m Home Depot, Dave Scrim
turns into the same number. Now that number is
associated with the order ID. So over here I’ve got a cookie, and a order ID, and over
here I have an order ID and an anonymous number,
which maintains privacy. I join those two together, and that’s how you create
a deterministic math. Because now I have an
anonymous name and ID which protects privacy,
associated with a cookie. So I can do real targeting,
real measurement. And then the trick is,
we talked about how that deletes over time and gets
erased and what have you. If you’re doing that
across brands over and over and over and over again,
you hear about these ad consortiums that are
trying to get together, that’s what they’re trying to do. And so that’s kind of how you
do deterministic matching. So I think the big takeaway
there is when you’re talking to your on boarder, talk to
them about how many people they have behind those ID’s they have. ‘Cause we throw a lot of
numbers, a lot of match rates. How many of them are
backed by real people? Oldey but a goody. On the internet, no
one knows you’re a dog. How, literally from like
15 years ago when I started in this space. And so who are you matching? So the name number you’ll
hear today if you work with most companies, if
you’re like most people, you’ll hear the match rate. My match rate is 40%, my math rate is 50%. I’ve got a match rate of 70%. Well is that an accurate match rate? And so I talked a little
bit about how companies can be better at accuracy. You need to ask that question. A match is not a match. You need an accurate match, and so the next question
you say after you hear, oh my match rate is 80% is, can you tell me what the
accuracy rate is of that match? How do you prove, how do
I test your accuracy rate. Because what we’ve seen
from a lot of providers is that that accuracy rate is 60%. And so if I take my 80%
match rate and apply a 60% on top of that, now I’ve really
only got a 50% match rate. All of a sudden the number
is starting to fall down a little bit. And so the question to
ask, my big takeaway from this one is, a match is not a match. A match is match and accuracy, and you should ask and they
should be able to tell you how they measure accuracy,
and you should be able to prove that, alright? That’s a basic 101. You should be asking for. Scale. So couple of years ago,
I think it’s kind of gotten a little bit better now. I used to hear oh my god,
I’ve got 406 million consumers in the United States that
you can target and message. That sounds a little off
because there’s not 406 million of us here. And so you get these
numbers inflated because everyone’s trying to show
that they have scale. And the truth is, you need to not only ask how many people based matches are, because that number will come down, right? But you also have to
ask what countries does it represent. Is this a match in Europe,
is this a match in China, is this a match in Japan, or is it a match in the United States? So let’s get our universe
straight when you talk about how many ID’s I have. But the biggest point on this slide and the most important is,
so we talked about match, accuracy, the reachable
audience in a 30 day period. Because if I matched somebody to a cookie that I haven’t seen since six months ago, it doesn’t mean anything. That person’s not addressable. You need an addressable audience. So you should be asking for a match rate, the accuracy of that match rate, and how many people in that audience have been addressable in the last 30 days. That will give you a good
sense for who you can actually message to. And what I’m gonna tell you is bad news, you’re gonna get another cut. You’re gonna get another
haircut of about 50%, so now we’re down from
80, we’re down to 50, you’re probably down to 25% now. So which leaves us to persistency. From a persistency standpoint, this is 10 second Bob. Anybody watch 50 First Dates? I love that movie. So 10 second Bob says hi,
I’m Bob, nice to meet you. I say hi I’m Dave, and
what do you like Dave? And I really like watching hockey. And so Bob’s like, 10
seconds later Bob’s like, hi, what’s your name? I’m Dave. What do you like Dave? I really like watching hockey. And if you think about it on the web, that happens all the time these days. You go to a website,
you look at something, you leave, and you come
back three days later, either you have one or two things. Either they follow you
around for two weeks and just chase you everywhere you go, or they completely forget
who you are because they’ve lost that identity. You logged in on a different device, you’re at your work versus at your home. You’re on a tablet versus
your mobile device. You’re cookie got deleted. They don’t know who you
are and so they can’t have that conversation with you. So takeaway from this slide
is you need to look for the persistency of that ID. In this day and age when
we’re competing against big big brands, mass
retailers like Amazon, we need to build our brand. We need to build our
relationship with our customers and the only way you
can do that is by having a consistent conversation
over time with your customers. And to do that, you need persistency. And so all of this wraps into identity. A litmus test I use is you
should be looking for something like I can communicate
over the course of a year with 80% of my audience. I can talk to them on a regular basis. I’m not losing connection
with 80% of those ID’s. So I’m gonna talk quickly
about a little case study about a client we worked
with at Conversant Epsilon, and it’s Road Scholar. They do adventure travel,
education travel, for adults. And you know, they get
people in through all kinds of different marketing activities. They have a catalog, they bring them in, but the real sweet spot
is repeat customers. They get most, they don’t
get a lot of website traffic. They don’t get a lot of
conversions on their website. They got a lot of conversions
from people who have been there before. So they came to us and said
look, we’re not getting much traffic, we’re having
trouble reaching these people. We don’t know where to
find, there’s no directory to where to find our customers, and they’re not responding as
much to direct mail anymore. And so what we said to them is well, let’s run a match test. So give us a list of names and addresses and we’ll see how many people
we say in the last 30 days. And so we saw 70, and
a more detailed version of this is available on our website, but 71% of their offline customers that have bought before, we
were able to reach online. Most of those people have
never been to their website, ever. And so we don’t think about
identity as the web based people or the offline people. They’re really connected together. And so if you go a step further, of those people we messaged,
55% of the conversions were offline conversions. So this could of been a
call center conversion, they could of been a catalog or a write in or walked into the store. But again, cross channel, we
were messaging an audience who had never been on the website, and many of them, even though
they got their media online, converted offline. And identity is critical to doing that. And this is my favorite one of this. 28% of the people that did convert online, converted on a different device
then they were messaged on. So we are messaging on their mobile phone or we are messaging them on
their PC or their tablet, and they decided to convert
on a different device. And I’ll tell you the
biggest group of that is usually mobile devices. People get a lot of ads
on their mobile devices and then convert somewhere else. We have a later case study
that’s gonna talk a little bit about that. And so the quote from
the client on this one was Conversant helped us reach an audience we couldn’t find before. And so if I go through the checklist, it’s people, make sure you’re
talking to real people. Device ID in themselves
aren’t real people. Email addresses aren’t real people. I sometimes use, I don’t know about you, I have a little fake email address. I have to sign up for a newsletter, I keep it over here, right? Cookies aren’t real people. They all have to be tied
back and it’s really really hard to do. Make sure you can, they have
a way to show you accuracy and that you can test
accuracy with identity. Make sure you can get it at scale, and make sure it’s a persistent
relationship over time. Those are the four questions
you need to ask for identity. Nobody’s perfect. It’s a super super hard thing to do, and the best and smartest
companies are trying to do it, but there are better
companies then others. So if you lay those four out, and when you’re looking
at any of those vendors, you put them side by side. That’s a great metric to use. There is a more detailed blog
post we have at Conversant.com blog, which goes into, hey look, if you’re looking at DNP or a CDP, or you’re evaluating a platform, here’s some questions you want to ask. I gave you like four or five of them, has a lot more details behind it. So with that, I’m gonna pause for a second and see if there’s any
questions in the audience. Like I said, I tried to keep it simple, but if you guys want to
ask more complex questions or less complex questions,
that’s fine too. Yes. Look at, you’re jumping,
the third session here today is measurement where we’ll
talk about lookback windows for measurement, but
it’s a great question. I’m talking about, when I talk about 30, I’m talking about reachability. The ability to deliver that message, because I think you’re right. Depending on the industry
that you’re talking about, that lookback window changes, and we can have a whole
conversation about lookback windows in the third session. Yeah. Does it show up somewhere. What’s the biggest misconception
clients have with identity? That’s a great question. That the match rate will
let me talk to my audience. The match rate from what
I currently understand, you guys tell me if I’m wrong, is still the currency,
is the word that’s used. It’s the thing that’s touted by clients. But I take that waterfall we talked about, I didn’t talk about persistency. If I really cascaded that waterfall, the 80% match rate which
most people don’t start out, probably more like a 60, turns into a 30% accuracy rate, turns into a 15% reach rate, and all of a sudden the biggest problem is I can’t talk to my customers. And so that’s one of
the biggest challenges and misperceptions I think
in the industry today. And that the other one is that anybody has got this completely figured out. If anyone tells you they got
it completely figured out, they’re wrong. New channels are popping up all the time, Xbox, Alexa, everything,
and so it’s a moving target. Yes sir? That’s a great question. So social actual, people based marketing, is actually the best thing
you can do for social, because the walled gardens,
one of the benefits they have is they’re one of the few people who actually deal in real people. So Google and Facebook
are for the most part, talking about real people. There’s some measurement
challenges there that we’ll get into, the measurement section. So if you can take your real people and port them over with name and address to their social networks, then it worked, you’re in a people based environment. Again, there’s a downside of that because there’s a bit of a lack of transparency in measurement there that
we’ll talk about later. But there’s an upside, I would say they’re
one of the cleaner ways to do people based marketing. – [Man] Can you give us a
second to get the microphone? – Yeah sure. I can hear you. – [Man] We’re live streaming. – [Audience Member] First comes the money, hey I’ve got a million dollars, and then great, let’s do this. We do all this and then we have to go, you know, whoever it is, yeah we can only spend about 10 grand. That’s it? Well what do I do? So I think how do we
then work with customers, how do customers work
with their partners then, to understand that we have
to do this legwork first to really understand how much
budget can then go there. ‘Cause I just feel like what I’m saying, the money usually comes first, then the project comes. – Got it, so if I understand the question, I just want to make sure. – [Audience Member] How do
we basically, all this work, how do we then translate
that to actual media budget? How do we then translate that better into how much we can actually
spend against these users? – Absolutely, what I would
do is I’d have a little calculator, and I’d say, I’d back into it and I’d say so look, if
I get a million dollars, and that’s targeting this many customers. You’re gonna target with
that million dollars. You’re gonna say hey, I want
to reach ten thousand people, or I want to reach a
hundred thousand cookies, or whatever it is. You’re gonna need to understand
you’re only going to be able to get 15 to 20% of that, and we hear that all the time. I’ve got my budget, I
want to spend more money, how come you can’t execute? And so you’re gonna take your little list, those four things, and
you’re gonna stack them up into a waterfall and you’re
gonna compare them across vendors, and you’re gonna say okay, well if I really want to
spend that million dollars and I really want to hit this many people, I’m gonna have to start
higher than I thought I did. Does that answer your question? Not quite? Okay. We do, I would recommend, and
I’m really trying super hard not to do a sales pitch. But I would tell you that if you go with, let me put it this way. If you go with the people
based deterministic, those things that I talked about, your falloff is gonna be so much less than in some of those
alternatives out there today. So companies like Google,
Facebook, Conversant and Epsilon, if you do
that, you’re only gonna get a 20% haircut, and you’re
probably gonna spend the majority of your money. Time for one, maybe two more
if we have any questions? Oh, couple on here, great thank you. I’m not used to this. Beyond asking the questions is proposed, how do we hold vendors accountable for ensuring accuracy, scale and persistency. This is down to the numbers, and I think you let up any more
than you can the performance that you’re hearing,
that you’re getting back. Oh, we had this many conversions,
we had this many clicks, we had this many whatever, right? What is our match rate this month? What was it last month? Is it growing, is it shrinking. What is the accuracy rate? This has to be a key metric for you, because it drives everything
else in your business. You could put the
prettiest software on top of poor identity, and you’re
not gonna have an effective marketing program. And so to me, what I’d love to see, and I’m closer to it than most people, this becomes a core KPI
for your business identity, and so good question. How does identity work
in the world of GDPR and other privacy laws. You have to be privacy first. You can’t get this wrong. It’s not going away,
what’s happening in Europe, what’s happening in
California, it’s the beginning, not the end. So you have to treat people’s privacy, their rights to what you collect, what you don’t collect. You have to treat that very seriously, and that has to be part
of the conversation with any of the clients
you’re working with. We have a privacy first. We try to be ahead of
the market on privacy and consumer privacy. We were the first company in
Europe to launch a GDPR tool, to allow consumers to
manage their consent, and we’re gonna plan to
stay ahead of that market. That should be what you’re
hearing from whoever you’re working with. If you’re don’t using these
identifiers available, cookies, emails, et cetera,
how can we identify our leads to target them with relevant ads? We’re not using these identifiers. So whoever asked the question, what kind of identifiers are you using? Maybe that came in from offline. I apologize, I’m not sure
I understand the question. Other identifiers, you
can use traditional ones. Direct mail, TV is becoming addressable. The world would become addressable. There will be ways to
target and personalize across any channel at some point, even if it’s not there yet today 100%. Again, those four questions
are what you need to focus on. – On the different identifiers, the note on the different identifiers, I know that they are
traditional identifiers, keys that were built on top
of traditional databases, those types of things. As Dave mentioned, we’re
working, and the industry’s working to bridge those together, such that offline and
online are tied together. May not be in one key,
but with crossover between that allows you to link them overtime, so. That is an involving. – Yeah, and with that, I’m gonna kind of switch gears. We’re gonna switch over to Carl and he’s gonna talk about data. When we originally set this up, they told us to be four sessions, people might be coming in and out. Looks like we’re good. We’re probably just go one long thing and if you need a two minute
break, you let us know. Alright, go ahead. – Fantastic yes, as Dave mentioned, we’ve got these four main topics. Identity, we’re going
to go into data next. And data for us, you don’t
need to see our ugly mugs there again. Data for us, this is again,
the idea here is that this is a session about
introducing you to the concepts in this industry or bringing you back in. So we’ve taken this down a level. We talk about data today, this
is gonna be the 101 version. This is the way I try and
explain it to my parents as they ask what I do and
they still don’t understand after all these years. Data is one of those
very ubiquitous terms. We all talk about data
like we understand it, and data lives everywhere. It’s in the nooks and crannies, it’s in big giant chunks,
it’s in little pieces, and it’s become more and more important. And you know, as we talk about that, it’s important to understand
who we’re talking about in the space we talk about data. So on this slide, we obviously
have Epsilon Conversant, we’re got companies like Nielsen, everybody’s familiar with them. They’re there to help track
some of the traditional television ratings, those types of things. IRi works very much in
the CPG and retail spaces. Dun and Bradstreet, for those
of you who aren’t familiar, has a compilation of
data around businesses. So when you’re trying to
solve a business to business problem, understanding
who you’re marketing to, who you’re advertising to, data
like that is very critical. And then ComScore is obviously
there for other types of digital measurement
and those types of things. So when we talk about data,
why is more data critical? More is always better, but
there’s risks in working with more data. The issue that we run into
now is that the world’s of ad tech and mar tech
are coming together. And both of those worlds used
data a little bit differently. And as evidence by Dave and
I being up here on stage together, Epsilon and Conversant
came from two different worlds. Epsilon was a traditional,
and we struggle with these delineators because the lines blur, but we are traditional offline marketers. One to one marketers with
email and direct mail and driving personalization that way. And Conversant was much more
in the advertising space, the media space. And they looked at data
a different way as well, but there’s overlap. And that overlap is
starting to extend itself into this industry, and
it’s something we found when we came together three years ago, was that there was certain terminology, there was certain
concepts around this data that sounded like we were
talking about the same thing, but we were just enough out of phase that caused conflict, it caused confusion. And so what we’re going to
do today is we’re gonna talk a little bit about some of
these at a very basic level, and we’ll let you through your questions take us as deep as you
need to, going forward. So when we talk about data,
it’s important to understand what moves the needle. There is a lot of data, and we’re gonna talk
about these three types. We’re gonna spend a few
minutes on this slide and talk about the
different types of data. First and foremost is attitudinal
versus behavioral data. So when you look at this, attitudinal data is the very squishy
but very relevant stuff that lets you know how a consumer feels. So this is data you might
collect through surveys, through preference centers
where they tell you what subscriptions they want to hear about or what channels they opt in to. You get it through your call center data, you might get it through social networking where you start to see that linkage of who their friends are,
who their influencers. Those types of things. So that’s the attitudinal
data that lets you know how somebody feels. It’s very good data, it’s
very rich, relevant data, but you actually, if you’re
managing it yourself, you have to have an
engaging brand to gather that type of data. Very few people, and not
to pick on any people that work in the CPG
space, but very few people give a lot of feedback on paper goods. And so that’s not gonna
get as engaging a brand as say, Axe Body Spray. A lot of people have
their opinions about that, one way or the other. So you’re engagement
level as a brand dictates the level at which you can
collect that attitudinal data. The other thing about that data is it’s, it’s very hard to quantify. So when you take that data,
it’s great for measuring. It’s great for when you look
at something, like say a brand awareness campaign, did it
work, did it drive awareness, did it drive a certain
feeling about a brand. Those are the types of things
that you can sort of register a yes or no, but any depth,
any quantifiable detail level gets much harder to do. So it’s great data, attitudinal data. But if you kind of take
it to an example of who maybe had this type of data, that leads us to a
transitional and behavior, is Netflix. So Netflix is a place that,
when they started out, they asked me a lot of questions about what I liked to watch. So I might of said that
I was very interested in period dramas or thought
provoking documentaries, but that’s not really true. Which takes us to the behavioral data. Behavioral data is not what I feel, but what I actually do. And so Netflix, as you all noted, stopped giving me the
recommendations based on the fact that I said I liked period dramas and thought provoking documentaries, and gave me stuff that was based
on what I actually watched, which is just Adam Sandler
movies and Avenger movies. So once they have that information, that is, it’s very
concrete solid information. Behavioral data is the
stuff that you gather from point of sale systems,
or the systems that collect and manage the dispositions
of emails or ad servers, or those types of things. It’s concrete, it’s transactional data. So you’re actually getting
the data that says hey, this is something that has happened. This is something that
somebody chose to do. It was actually the behavior
that we are trying to draw or something that we can,
a very concretely measure. So very powerful data. The downside with that
behavioral data is if you look at it in it’s own silo, is
that it can be limiting. And it can grow stale, and
it can be somewhat myopic in it’s focus. And so as we talk abut
the Netflix example, that is where that holy
grail of putting them on an axis. How I feel and what I
do is the type of data that works well in
conjunction with each other. So, attitudinal versus behavioral data. Understanding that data
as you collect and gather and prioritize it in your organization. It’s important to understand how it fits in your organization. Do you have an engaging
brand, do you have the ability to collect this, those are the types of
things to think about. Now we move on to talking
about first, second, and third party data. This is one of those terms
that I hear a lot of people talk about like they know what they mean, and then when you kind of question them, they don’t really get it. And it’s really a very simple concept. First party data is the
data that you, as a brand, own and manage. So this is your data. This is data you collected
about your consumers, be it attitudinal,
behavioral, PII, non PII. This is stuff you collected
about the experiences and the engagement you
have with your consumers. And this is very very powerful data. This is the space that Epsilon
has worked for years in. It’s where I personally grew up, in managing that consumer data. You heard of building 360
degree view databases. This was the start of that. The limiting factor in this
type of data is really twofold. It’s really hard to collect. There’s a lot of it. It builds, it evolves, it grows. And second part, is that
it’s actually limiting in the sense that it
is only data about you and your consumer. The experience you have, the
channels you interact with, the transactions they
have with you as a brand. Which is very powerful
as you craft and deliver your messaging, but it
can be somewhat limiting. It is limiting to where
are they in their overall life cycle, where are they
in their buying decisions and those types of things. So first party data, very
powerful but can be limiting. Second party data isn’t
really used very much, but it’s the idea of an exchange
of data between partners. So this is done in a lot
of comarketing efforts, a lot of efforts where
you’re maybe partnering with a brand to say hey,
if we work together, we’ll share this data or
we’ll take data from my brand, what I know about them,
and give this to you in exchange for this or some
sort of monetary exchange. Problem with second party data, the reason it’s not used
very heavily is twofold. One, not a lot of people
know how to monetize and put a value on the data that they have about their consumers. So it’s very hard to put
a concrete number on it, so it’s very hard to make
a transaction around that. Second reason is, it gets into
that somewhat creepy area, that fine line between being
a solid boyfriend who listens and that creepy stalker who
knows way more about you then he should. So that second party data
exchange, those fine lines are the reason second party
data is not used as often, but it is still a market we
think will evolve over time. Takes us to third party data. Third party data is where you’re going out and actually transacting. Buying data from typically a broker, compiled data sources. Data that is aligned to
solve specific problems, and in many cases it’s already been proven
to solve that case. So a lot of pros in third party data and bringing that together. But just like the attitudinal
versus behavioral, they all have their
strengths, their weaknesses, and triangulating across
those is critical. The third area we’re
gonna talk about is PII versus non PII, and I use
this phrase like everybody knows what it means, and inevitably, somebody walks up to
me afterwards and says, what does that mean, so I’ll
go ahead and explain it. PII means personably
identifiable information. So this is all the
information around the name and the address, the contact
information that Dave referenced earlier, that made
building ID’s in the early days very easy. Non PII information is
everything that we know about an individual, but
that there are boundaries in place that you’ve got
to keep that distance, that you can turn it into some
sort of anonymized profile, that you can do your analysis on, that you can do certain
decisionings behind walls, but that you can’t activity
use it in that decision making process. So PII and non PII, very critical parts, critical elements in
bringing data together about your consumer, helping
you know who they are. But different challenges
in bringing them together throughout the organization. So where do you find this data? This data obviously
lives all over the place. There’s volumes of it everywhere. The three main sources
we’ll talk about today, your own backyard. So you’ve got this data in
the interactions you have with your consumers today, and so this is where
through either your own IT department or through
maybe systems integrators, you gain access and collect this data about your consumers. That is fundamentally where
that first party, PII centric data comes in. Attitudinal behavior data comes in. So that is the first place to get that. Rich data, obviously that’s great. The challenges, as
we’ve mentioned earlier, it can be difficult to collect
and difficult to manage over time. Getting it into a database is easy, taking care of it, making
sure it’s evergreen is a challenge and has
costs associated with it. Second source is compiled sources. Data brokers, agencies,
I’m sorry, data brokers, list providers, those types, that have build compiled data sources and deliver that to you for a cost. Again, as I mentioned earlier, this is data that is usually
proven to have a good track record of it’s success in solving a very specific business problem, and can just you know,
it’s just got to balance out against the budget
you’re working against. And the third place is agencies. Agencies typically are sort
of the best of both worlds. They take the data that you have in house, they take data from brokers, they apply a lot of analytic focus to it. And prove out the value of that data set, and did it work, does it
apply to your business need and bringing that together. So between your internal,
your own backyard agencies, and data providers is
where you’ll find that data throughout the ecosystem. So, let’s talk about some
of the challenges in making the most of your data. There’s really three
fundamental challenges that we talk about in this. First and foremost is
that data lives in silos. It’s inherently, it’s an
idea that as a database administrator growing up,
I envisioned this world where all data lived in this utopian, giant database that I could
access through, you know, sequal or some sort of neural implant, and that’s not a reality. And as much as I’d love it to be, it’s not going to be a
reality anytime soon. Mainly because this data is voluminous. It live everywhere. It grows very quickly. Trying to transfer it, bring
it all together for no purpose is a big challenge. That will take us to our
second challenge in a second. And the other reason is,
it’s very utilitarian, it’s very effective, where it lives. So trying to move it, and migrate it, adds overhead to the
management of that data that makes it very difficult. What we recommend to get
across that is something that Dave talked about earlier,
is that identity layer. So how can you link the
data across those silos, and have that decoder if you will, that lets you know that
the data about this person in this silo is tied to
this person in this silo through identities, through
keys, those types of things, is a much more effective way to do it then to try and do what
is our second challenge, which is boil the ocean. Trying to bring all of your data together in one ecosystem. I have been part of these projects, I have led and somewhat
successfully gotten them off the ground, but this
idea of I’m going to build an enterprise data lake, or
an enterprise data warehouse, that will be the end all be
all monument to technology that we’ll never have to replace because we’ve done it
so well this first time, and those are really really
hard to get off the ground. Because you’re trying
to fight a lot of fires. You’re trying to build for a
lot of different challenges, some of which you can see,
most of which you can’t, and at the same time, the
technology is evolving and leapfrogging past
what you can do today. So what I’ve seen a lot of
cases in this data management mindset is I’m going to
stop down our business for nine to 18 months to build something, that by the time I get there, may not even solve my business challenges. Or worse yet, is I’ve seen
companies get locked down in that sort of analysis
paralysis of trying to decide what am I gonna solve first. So boiling the ocean in this
case, deadly, dangerous. And then the third challenge,
and this mainly applies to the do it yourselfer,
is the regulatory concerns. This is an evolving space,
this is something that as people become aware
of the fact that when systems like Facebook are
free, you’re not really the consumer as much as you are the product. And so rules are evolving. Privacy rules are evolving, and they’re evolving in
different regions and locations. So what we have found is
trying to do this yourself, micromanaging this, is an ongoing battle. Recommendation is obviously
to work with someone who has resources and
expertise in this space. So we’ll wrap this section
up with a case study about Discovery Communications
that came to us, several years ago. And this is a case study
that brings together all of the data we talked about. So Discovery had a challenge. They were trying to
evolve their audiences. They were trying to reach
the right audiences, as tune in for their
TV shows was dropping, they were looking for ways
to spend media dollars effectively, and reach the right consumers where it would be resonant and effective. So they came to us with, in one example, a panel of say, ten thousand households of people who actually watched their show. We took that, along with
our compiled data source, and extended that out. We found lookalikes, people
who had the same demographic and behavioral and attitudinal data, that expanded that audience out to say, a million. We then took that million and took it into the digital
ecosystem with Conversant to say, what are their online behaviors, and how do those look
like the same consumers, and expanded that audience even more, and found audiences that we could reach that the ads for these
shows would be effective based on that information. And what we found was that
in our first year that we launched this, they had ratings 30% over prior season premiers, and the other thing we were
able to do is deliver this in a much shorter
timeframe then they used to with a much smaller set of data. So this used to be a 12
week measurement turnaround. We turned this around for
them in about four weeks. And again, part of that
was all of that data lived in different silos, but
we had the ability to link it together, tie it together,
for purpose driven solutions to solve problems specifically for them. So, when you are tasked
with managing the data within your organization, a few things you should think about. First and foremost, prioritize
what you’re focusing on. You can’t get stuck boiling the ocean. Decide what problems
you’re trying to solve for, and understand what data
works in your industry and doesn’t. This is one of those cases
where I’d love to be able to tell you oh yeah, focus on behavioral, focus on attitudinal,
focus on second party, third party, but each
industry and each business has their own specific needs, and knowing that will help you prioritize where to focus. Second, centralize only what you need to to drive that effectiveness. Don’t boil the ocean, don’t
get stuck trying to build a monument technology. Third, it’s always important to measure, and this is a nice transition
to Dave’s conversation here in a second. Measure not just your
programs or your offers, or the content, but measure the data. Because every piece of data you manage costs money to take care of. When we first started building
databases for our clients, 20 years ago, a marketing
database maybe had 50 attributes. We built one for a client recently, had over 3,000 attributes
on any given consumer. And I know that even today,
no more than 50 to 100 of those attributes are
relevant at any given time, but every one of those
attributes has a cost associated with it from managing
the meta data around it, managing the interface
agreements, the exchanges, the understanding of what that data is, all has a very high cost. So measure what works, not
just in terms of programs, but in terms of the data
and the effectiveness, and finally, be prepared to evolve. This is not a one stop shop. There’s not one universal
project that will solve this problem for you, the data problem. It will constantly grow,
it will constantly evolve. And our recommendations
are to treat it that way, and be prepared for this
to be a lifelong pursuit, to understand your consumers
through better data. I’ll stop there to see if
there are any questions on data. Alright, we have one here. I have heard the term data
leakage used a lot recently. What is it and how do you prevent it? I have actually not heard
the term data leakage in this regard. Does anybody here in
the room that can, yes? Come on. (inaudible speech) – There’s nothing more
powerful to your business than data about your customers. So you got to be
protective about that data. You got to make sure, I mean, there’s a big, anybody want
to name one of the biggest companies out there that’s
responsible for data leakage right now. You all got it, right? Everything you do, every
campaign you run on that company, is sold. In fact, there was an article,
I couldn’t believe it. I read two days ago, or on
Thursday, two business days, about the fact that
your backup phone number that you’re using, in Facebook,
do you know contact me if anything goes wrong,
they’re selling that and monetizing that. And so data leakage is about understanding when you’re partnering with somebody, when you’re allowing
your data to be accessed, your valuable data as
brands and as customers. What happens to that data
and where does it go? ‘Cause there’s a lot of people
who want to take that data and want to leverage it for you. That’s a little bit about data leakage. – The other thing I’d say
about that is kind of what we talked about earlier. Just because you can
collect data and manage it doesn’t always mean that’s valuable data. To your example there, having that data, sometimes that data gets misused, so it’s another point to
centralizing only what’s important, especially from a marketing
and ad perspective. Great question. Any others? In room, all I can see is
bright shiny lights, so. Alright. Awesome, fantastic. We’ll jump ahead to measurement. – Measurement, this is a big one. Actually it’s called attribution, and so I’m definitely gonna
spend some time on attribution. But I’m also, I’ll open
it up to measurement. We can take test and control questions, we can take how do you measure search, and so I want it to be,
it’s a really tough space. Anybody feel like they’re doing
perfect measurement today? In the market, anybody doing that? Alright, we’re not, so
I’d be surprised if, most people I run into this is one of their biggest problems. How do I measure what’s
working and what’s not working? So that’s gonna be the
gist of our presentation. Couple of other level setting questions. How many people measure on clicks today? Are there still some click
based folks out there? Wow this is an advanced group. This shouldn’t be called the 101. We should of upped our game. There’s still a lot of folks by the way that are doing click based measurement, a lot of folks out there. When it was the only currency
out there it was fine, but there’s a lot better
ways to do it today. Throw up a few companies. Attribution vendors, ’cause
the gist is attribution. Google is now a big attribution vendor. Facebook has it’s own
attribution solution. Bizible, Bright Funnel,
Conversant, Epsilon, Visual IQ. Who uses a third party
attribution solution here? Can you raise your hands? Anybody use a third party? Anybody use one of these
companies, anybody? Okay, I’m gonna pick on somebody. How are you doing measurement today. You, yeah. You don’t do measurement. Anybody, anybody tell me how
they’re doing measurement? Yeah, go ahead. (inaudible speech) Yeah, just taking the
data and do it yourself. Hire a media mix company,
something like that, maybe a third party
analytics company to do it, great okay. So there’s a good reason why we don’t have a lot of folks using these measurements. Nobody’s happy with the
measurement providers out there. And I’ll get to that in a little bit. So before I start on that, what is, what do I mean when I say attribution? What am I talking about,
and we can debate, you know, this, but I want to level set. It’s as simple as taking
a marketing event, some marketing event. An email was opened, an
impression that was sent to somebody, an affiliate
message that somebody clicked on, an impression that was
sent on a mobile device. Something, some market event. A direct mail piece, right? Some marketing event that
was sent to somebody, and a conversion of it. And that conversion event
doesn’t have to be at sale. It could be an online sale,
it could be an offline sale, it could be a website visit, it could be sign up for an email or sign up for my loyalty program. So attribution is about
connecting a marketing event to some kind of conversion
event that I want to take place. Easy as that, pretty simple. So what’s the problem? Well one of the problems is
that most of the industry still works on what’s called
last touch attribution. And so I say well, I
did an email campaign, I sent a display ad to
a PC and I sent it to the mobile provider, but
I was running an affiliate campaign and they clicked
on the affiliate campaign and they converted, and
therefore the purchase goes to the affiliate provider, and that’s called last touch attribution. The next one is, multi touch attribution. More complicated and you can
see why people have trouble. Especially now you’re
starting to see why identity might matter in this solution, why persistency might matter
in keeping things together. Because multi touch
attribution tries to say how does different activities combine to drive a sale? You put a weighted score
on the different channels, you look at the consumers
path to conversion, so that you know where to invest, so you know what’s working and know what’s not working. And so somebody opened an email and then they saw an impression
on their mobile device with media partner A, and then
they looked on their computer and got an impression and then a click, and then they visited
the site and either did an online or offline purchase. So that is called multi
touch attribution, alright? But in order to do that you
need to see the full cycle of the person. And then there was a question
earlier in the audience about lookback windows, and in fact, I’m gonna stop after I do this next slide and we’ll go back and see if
this answers your question or if you had a different question on it. Lookback windows. So a lookback window is
how far do I look back, because I might have been
messaging that person for five years. I’m not gonna take into
account every message and every channel and every
contact I had with them for five years. It’s part of the purchase cycle. I’m gonna say what is
the reasonable timeframe I should look back at all
my marketing activities? And so if I’m Walgreens or
CVS, I might only look back seven days because the purchase cycle of convenience pharmacy is really quick. If I’m a retail clothing manufacturer, I might look back 30 days,
’cause that’s kind of the decision cycle that I go through. If I’m selling cars, I want
to go back months in time, three four five months. So it really depends on
what the purchase cycle is of your industry, in
order to decide what your lookback window could be, and
that’s a really flexible thing and something that people
should play with and figure out. Now did I answer your
question about attribution, or did you have a different,
about lookback windows? (inaudible speech) They want more credit for the sale. So one of the key things,
and I’m gonna say this at the end because it’s
gonna takeaway flow, but I want to come back to it. I would encourage you,
because this is mostly about attribution. I would encourage you all where you can to use, there’s a classic
thing that’s been around since direct mail, we kind
of forgot all about it in the digital world, it’s
called test end control. It’s called creating a group
of people that I’m gonna send a email to, and a
group of email that I’m not gonna send an email to, and
looking at the difference in sales between the two. It’s about taking a group
of people I’m sending a display ad and not sending a display ad, and looking at the
difference in the sales. And creating a hold out group,
because you’re exactly right. Because for any of you in
this room who have added up all of the conversions
that your email company tells you they got, that
your display company, if you got multiple display companies, you end up with five times
the sales you actually have sitting in your conversion,
in your business. And so a lot of people are claiming sales, and that’s why you want to centralize, you want to use test and control, and you want to bring that all together to an individual level. And so we can unpack that a little bit. That’s a little, not too much, it’s a little more sophisticated
so we’ll save that one if we want to go deeper
in that in the end. So it can vary by type, it can vary by channel, how
far you want to look back for attribution. Why do I care about attribution? The number one thing
is I want to understand what media’s working,
drive budget allocation. Do I spend, is this digital media provider working better for me than that one? Is this website working better for me than that one? How about my channel. Should I invest more in email? Should I invest more in mobile? Should I invest more in direct mail? Should I cutback in direct mail? How do I invest my marketing in general? How do I get the most
leverage possible out of my marketing dollar? The good news is it’s more measurable than it ever has been. Especially in digital today. Bad news is it’s super complicated because of some things we talked about, and we’re gonna talk
about that in a minute. Understand the customer value, oh sorry, customer journey path. What path do people take to purchase? What steps do they take along the way, again, so I know where to invest. And understanding the
customer value of the customer by channel or by conversion path. So maybe the people who are on mobile are more or less valuable than the people who are only answering emails. Maybe it’s the opposite, right? And so how do I understand
the value of the customer with those channels. The thing is attribution
is really just math. It’s not that hard. You come up with a lookback window, it’s seven days, its 30 days. You come up with a fractional attribution. I’m gonna give 10% to
email, I’m gonna give 30% to display ads, I’m gonna
weight that a little bit depending on how long each of those works, but it’s just a math formula
at the end of the day. So why is it so hard. Why is the net promoter
score for attribution vendors negative 29%? People, the net promoter
score is don’t work with this company, and I don’t
care which of those companies it was up there, nobody’s
happy with attribution vendors. Nobody’s happy with their attribution. It’s messy. It’s messy because
there’s a lack of trust. It’s messy because there’s
a lack of transparency. It’s messy because data
management is really really hard, I’m gonna talk about each of
these in a little more detail. So we learned earlier today, it’s messy because identity
is super super hard. I’m just gonna go into that
in a little bit more detail. Trust. I put up a bunch of names
up there and there’s only one of them that’s still
branded as a same company. That was Visual IQ. There was Visual IQ, this
was only two years ago, Convertro, Adometry, Datasong, these were independent
attribution companies that said hey look, I know
you’re working with Google, I know you’re working with Facebook, I know you’re working with Conversant, I know you’re working with
whatever media company, I’m gonna look across all those and I’m gonna help you with this. I got some great math,
and I’m gonna help you understand what’s working
for you and what isn’t. As a standalone company,
none of those companies exist today. The only branded company that
exists today is Visual IQ, but it’s not independent. These companies are owned
by, Visual IQ’s owned by Nielsen, who’s in the media business. Convertro’s owned by Oath,
who’s in the media business. Adometry’s owned by Google,
who’s in the media business, and Datasong is now owned by Neustar, who’s in the media business. So little bit of fox
guarding the henhouse. And so how do you trust the providers? How do you find an independent provider? Well the truth is, these
marketing platforms are really complicated and
they take a lot to run, and that’s why some of these
company’s have been adsorbed. So you can have trust if
some of these companies are doing it, if you have transparency. And so this would be the number one, my number one takeaway
from this whole session is not how you do the
math, ’cause you can argue about that, and it’s not who to trust, because trust is earned. It’s that trust comes
through transparency. And I’ll use an example
of as I was taking the cab from the airport today, or last night, I was thinking about the
old days when I used to take the cab to New York from the airport, and I was sure that person
was driving me in circles all the way around. I didn’t know where I was
going, the meter was going up, it might be right, it might be wrong. I didn’t have trust, and I
didn’t have transparency. So what came along on the ride this time, the guy was a great guy,
so I didn’t have any reason to double check him, but
let’s say I was nervous. I could of pulled out my Google Maps, I could of seen the right path to go, and I could of directionally
had transparency that I was going in the right direction. So I actually think
attribution is hard enough that you do want to
work with somebody else, whether it be one of these partners, whether it be a small boutique
shop who takes in data and analyzes it for you, but if you’re a large organization, you can certainly have the analytics people to do it yourself. But the key, the key to it
is ask for transparency. And that means you’re
saying, give me the data. Give me all of the conversions you saw, give me all of the impressions you saw, across all of the channels,
and let me validate that myself. I might get you to do
this on a regular basis, but occasionally I’m gonna test you. I’m gonna spot check this. I’m gonna have another party
take a look at this data, because without the transparency, then you’re just taking their word for it, that the results are why they are. And there is a real lack of transparency in this industry, and I
think that’s the number one thing we all need to drive for, because I’m not saying
anyone’s out to cheat anybody, but they’re also out to make
their solutions look good. And you validating that
is an important part of your business. These are two of the least
transparent organizations, and they are two of the biggest
organizations out there, and we all should be doing social media. It’s a great channel, it’s
where our customers are. And yet even with them, you need to drive for transparency, because
what you’re going to find at some of these
companies, and you probably know if you’re using them, they’re gonna get aggregated results. You’ll get nice reports,
you’ll get some performance goals and stuff like that,
but you’re not gonna get the raw data. My big question would be why? What’s anybody afraid of? It’s working, it’s
working, just show me the individual level results
of those programs. And that would be the question, and it’s gonna take a lot of time, it’s an industry problem, it’s
gonna take the big players pushing and pushing and pushing, but we got to keep doing that. Data management. This sounds, about as dry
as, this is about as dry as it’s gonna get. The data management is
how do I collect that data in the first place. Stacy is the data expert
at Epsilon Conversant, and runs our whole data business, so she’s the one to go to
for questions after the show. Data management. How do I collect data? Seems easy right? Just send me over a file
and I know how many people I direct mailed, I know
how many people I emailed. What about mobile apps? How many of you are
doing a really good job at connecting which individuals
visited your mobile apps? What about tagging all the
different media companies that you’re working with, and having those tags on
the internet flow into one individual place across
all the different vendors? What about your Facebook
and your Google, right? How easy is it for you
to do data management. According to Forester, it
was the number one criteria of importance when people were evaluating attribution vendors. Can they even collect the
data in the first place? And so that’s a super super
important question to ask. How do you collect data,
can you collect it on all the channels? Can you collect the search
data, the paid search data, that you’re running against? There are ways to do that, I
don’t know if you guys know but through adwords, you
can actually identify who the individual was who hit your site through your paid search thing. So ask your vendor, can they do that, can they capture that? And we can talk about
that later if you want. Identity, again, most
of you here were in the first session, so not gonna
bore you with that again, but number one thing. So I grab the data, I pull it all in, I have my math. But none of it matters if
I don’t have the accuracy, persistency, and scale to identify this at an individual level. Because all it says is,
I put 25 cookies out and I got a conversion. Well did those 25 cookies
go to 25 different people? Or those 25 cookies
all to the same person? You got to come up with
a common denominator around identity. So client success story. This retail brand in the clothing space, 30 days, as we talked
about, attribution window. And so what they found was
they were running attribution and their mobile conversions
were less than one percent, and the division head
of the mobile unit said I got a real problem. They’re devesting in my business, they’re not funding us,
they don’t think mobile does anything. I’m telling you, I know of it out there, I talked to, it was a
18 to 25 year old brand. The kids are all on mobile,
I don’t get what’s going on. And the truth was, he couldn’t track it. And so what happened was
as he migrated to a system that could track identity,
could track persistency, could track all across these channels, he found out that prior to a conversion, he might only be getting
one percent of the sales on their mobile device,
but 42% of the time, people were seeing mobile ads and consuming the media and
getting the brand consideration on their mobile device. Huge change within the organization. Huge shift in understanding
about how mobile affects the sales process. That’s just one little story. You’ll all have your own stories, but it’s super super important
and shows you the difference between where you might
invest your money or not. So, you must have checklist, it’s just math at the end of the day. So if it’s just math, then demand, trust, and transparency. Make sure you’re getting
the best data management and you’re able to capture the
data from all those channels. Another thing I didn’t hit was, you get some people in
the space who are good at online data capture, and
some people good at offline data capture. It’s a very rare combination
to find a company that was designed to do both. I would keep looking for those companies. Epsilon Conversant, that’s
one of the unique powerful things of Epsilon and Conversant, so shameless plug there. We’ve been doing offline
forever and we’ve been doing online forever, and now we’ve
kind of put them together. And then make sure identity is correct. And again, would point you to that blog, more questions. The questions are aligned
around measurement, attribution, identity, data, questions to ask at that blog
that we talked about earlier. And with that, if there’s any questions, I will take them now. Yes sir? Yeah. – [Audience Member] How
do you get to the right combination, mostly when
you have non transparent channels like Facebook for example and Google, mostly Facebook, that you’ll not even get
the order ID to be able to max the data. – If I can solve the
Facebook attribution problem, I probably wouldn’t be
standing here right now. It’s the biggest problem. Social is so important to us,
but it’s such a black hole, and you know, I wish I had
a magic answer for you. It’s the one channel we can’t. The only thing I can say is look, remember the days when AOL was
the only thing in the world, or Yahoo was the only thing in the world? Well, more pressure’s gonna
come, more player’s are going to come. Amazon, we saw upstairs,
Target’s got a big ad initiative. The more pressure the more
competition that comes to the market, the more
those companies are gonna be transparent with their data, the more we’re gonna
see and force everybody to be transparent with their data. And I would say, you know,
you got to put your money where your mouth is. Yeah, you put some dollars
in, but maybe don’t put as many dollars in. And I will tell you, the
biggest clients we have are making some dents, are
getting more than the average person is starting to
lead the way in putting pressure on those companies. Yes. – [Audience Member]
Campaign managers has said they are not going to release event level, or yeah, event level data
that’s required for attribution. – Is that Facebook campaign manager? – [Audience Member] Google. – Google campaign manager. So you are, you’re just at the
mercy of their attribution. If you’re going to use them, you’re going to have to
not have individual level insight to who is being measured or not. You’re gonna have to wonder
if you are here in the identity session, if there’s
clustering going on or not, and you’re gonna have to
take their word for it. That’s the only answer I could have on Google and Facebook. Yes. – [Audience Member] When
you talk about transparency with some of the big
players besides Facebook and Google. – Sure, yeah. – [Audience Member] And you
say allow for spot checks, I mean just, the hurdle
in getting everything, when you talked about data management, getting the tag, getting
everything flowing, how do you do the spot check? – Oh that’s a great question. So what I would do if I
was a mid sized company, and I had mid sized resources, I would say look, I really
trust who I went through my checklist, you passed
most of the things, at least you’re better
than most people out there. I want you to kind of do
my attribution for you. I don’t have a ton of resources, but I want you to do is, I want
you to give me a data feed. And I want you to send me
that data feed every week. And it’s gonna have the impressions in it, serve to cross all media, whether that be my media
or somebody else’s media. It’s gonna, I’m gonna
send you my direct mail and email files, and you’re
gonna send those back through the impression feed. It’s gonna have all the
conversions across any channel in it, and you’re gonna
send me that feed each week. Now, you might not do
anything with that feed for week one, week two, week
three, week four, week 20. And then one day, you hire a third party, a nice little analytics firm. You say hey, here’s this data feed. Could you validate what we’re
hearing from this company and just double check
that everything’s okay. And one of two thing’s is gonna happen. You’re gonna have a big discrepancy, and you’re gonna need to
get everyone in a room and start talking about that, or things are gonna look
directionally correct. It’s okay if they don’t look perfect. They’re gonna look directionally correct, and you’re gonna go okay,
I think this is working. I think this is fair. I’ve double checked that
person with somebody else, and I’m gonna move on now
for another five months or six months. And I think that’s the way I would do it. Yes, in the back? – [Audience Member] If you’re
working with an advertiser who’s ran the marketing mix, so I’ll give you an actual real example. Trying to break into Nielsen’s black box. And you know, I’m getting
asking some of these questions, but understanding what are
those questions I’m not asking? Their panel doesn’t even match the data and the targeting we’re selling, so we don’t even know what that is. Where our users are mobile first, so you’ve already
calculated a 70% drop off of the measured media. Do I ask for a data feed as well? But how do I recheck the actual sales that I know are modeled on modeled data. – Right. Sounds like you’re asking
some of the right questions, but you’re not necessarily
breaking through. And one of the suggestions
I would have is find a third party to help be your advocate. So find a firm who’s doing this and on a regular basis, and
it doesn’t have to be us, right? If you want to ask after,
we can give you a few names of places, or it can be us. We do this all the time
and we help companies with the right questions, and like I said, there’s a list on the blog, but there’s a list of bigger questions. And we can put a little pressure on those. What I can’t promise is I can’t promise we’re always gonna get the magic answer. I can promise we’ll get
to the right questions and we’ll get some more data probably than you do, but again,
they’re called walled gardens for a reason, right? Nielsen, maybe not so bad. Nielsen, I think we know a
lot about Nielsen and IRI where we can help you a
little bit more on that. Any other questions? Alright, thanks so much, and at the end, I’ll be around
if you want to talk any more. – Great, well we are in the home stretch. Moving along quickly here. So our last section is platforms. Jump ahead here and give a few people, if you want to switch
sessions you’re good. We’re gonna talk platforms now. Platforms is sort of the
final of these pillars, and it’s an important one. What we found is over
the last, in my 20 years in this industry, there’s
a pendulum that swings back and forth between the
we need to own this ourselves internally and manage this data, this orchestration, this content, and switching then the
other way which is well, we need to just outsource this. This is too expensive, too costly, we can’t keep the resources in house so we need to outsource this. Then the problem becomes, well I’ve outsourced this to some agency that I now can’t see what’s going on. Now I need to pull it back in house. So that pendulum in my 20
years has swung back and forth. And a couple of things have
happened over the last five years that have really led to
the advent of the platform. So obviously, the cost
of storage and housing and horse power and cloud
computing has been universal in helping all of these, but there’s a couple of big changes. Am I cutting in and out there? First and foremost, the big
software providers years ago, started acquisitions. They started buying in pieces of parts and stitching them together
into a larger ecosystem. The second thing that
happened is that these big social media networks went from
Farmville to Madison Avenue. Sort of realized that
they had this audience, they had this group of
engaged individuals, and they found a way to
sell advertising on that. And then the third thing is that an advent of venture capital has
started to apply platform technology, the problems
that have been around forever but have always been solved
in a very manual focus. So those three initiatives
have led to this advent of the age of the platform. And the problem with the
platform is that there are a lot of players. So in this case, we’re
just listing a handful. The SalesForce’s, the
Adobe’s the Oracle’s. There’s obviously the IBM’s, the Facebook, all the social media providers. Epsilon and Conversant is
sort of a different breed in that we kind of come from the, we were a services organization, an agency if you will, and
we are pushing to be more platform orientated. To take our intellectual property and our experience and
package it in such a way that we sell it as platforms. So you have everybody
converging towards this software as a service model, and
it becomes a problem. It becomes a struggle because
they all have an overlap. We’re all sort of playing
in the same space. In fact, if you take all of our story and you turn it into the elevator pitch, it’s we help you understand
and know your consumers and reach them across every channel. That’s the story of everybody
in the platform page, whether it’s in detail or not, it’s the same story at the highest level. So how do you get through that? How do you solve for this,
how do you pick the right platform? And I’m just gonna roll really fast ahead. There’s not one platform
that solves it all. I mean I would love to
tell you it’s us, it’s not. There’s not a single
platform that does it all, and is the single easy choice to make that will get you promoted to BCMO. There is a lot of things
that have to go into the decision around your platform. First and foremost, can you articulate what you’re trying to do? Each of these platforms that we discuss has a different strength. Has a different focus,
came from a different set of DNA that means it solves problems better than others, or maybe it started at a point where it got to
leap frog certain problems. So understanding what you’re trying to do, what you’re trying to
achieve, is important, because the other problem
you get is you will get bombarded with the big stack
of it can do everything and anything. If you buy one tool you’ll
get four more thrown in, and that can lead to a lot
more confusion down the road. So, can you articulate
what your problems are, what you’re trying to solve for. Second is, can you prioritize that? Because the reality is that
we talked about earlier, these projects take a
while to get stood up. Even the platforms that are
multichannel marketing hubs that have it all put together, are still built out of acquisition, and some of these are built
out of 10 to 12 acquisitions that have been stitched together or are being stitched together
after you close the deal and stitched together behind the scenes by companies like ours to
make them work together. So being able to prioritize
what’s important, three months, six months, 12 months, 18 months down the road, is important as you make these decisions. Because as we talked about earlier, technology is constantly
leapfrogging and evolving. And a player in a certain
space may acquire somebody that makes that solution
something they could do themselves, and yet you’ve
already purchased something that now has to be stitched together. Prioritizing is key. And the third, and this is
really the most important as you look internally,
is are you building around an organization, or are you
going to shape your organization around a new vision? So a lot of tools and a lot
of people where I’ve seen go wrong in the platform decision is they buy a platform
and they try and make that platform fit a broken model. And in many cases, I even
see this broken model shape how the decision
for platforms are made and cause other problems. So you have the VP of email marketing making the decision on the platform they’re gonna use for email, and the VP of measurement and analytics is gonna chose their partner, and somebody is gonna
choose one for web analytics and media, and now you’re
dealing with different pieces and parts from
the different platform. So ar you trying to work
around that as you get in to the level deeper. The orchestration of those
audiences is now gonna become more complicated. Who approves this audience
being used in this channel? Those are things that technology by itself and these platforms
will not solve for you. You as an organization have
to be ready to understand that there are gonna be
changes made around that to fix for that. The other big challenge in
these platform decisions are what’s in place that
works for you today? Either because it works really well, or because it would be
difficult to replace, and that shouldn’t be underrated. Or third, because you’ve
already got some sort of long term engagements,
some sort of licensing deal already in place with a
provider that can provide that functionality and you or
somebody in your organization is incented to get the most
and leverage that platform. Those deals are signed,
and sometimes they sit idle for two, three years on the back end of those long term deals, and those platform decisions, you know, your best bet is to try and
make the most out of those while you got that licensing in place. So, a lot of decisions that
you’ve got to consider. It then takes you to the, you know, which of the platforms, what
does the landscape look like? We talked a lot in this
about the marketing clouds, the big software giants we talked about. What I would say in this space is that this is why it goes back
to understanding what it is you’re trying to do and
what you’re prioritizing. Each of the clouds has a
strength, a position they may have started from or an acquisition that was foundational. Adobe is really solid in
the web analytics space and managing content. IBM is obviously fantastic at
their analytic capabilities and some of their orchestration. You’ve got Sales Force
which is great taking from the call center and
understanding those consumers. So each of them has a strength, and each of them has tried
to bolt on certain components to give them the complete stack. And sometimes it’s a
game of musical chairs, and some were left without
an eCommerce platform or others are finding
strategic partnerships. One thing I would note is
an announcement came out last week, and I think you’re
gonna see more of this. Adobe announced a strategic
partnership across, I think it was SAP and Microsoft, an open API set that allows
for the exchange of data and information between
these three platforms. I think you’re gonna see more of this as each of these software
giants sort of realizes that no one is buying their
big black box of technology and landing it in a data center, and dealing with that
as their sole tool set. Everybody is working around
the different tool sets. The other thing that’s important to note when you’re buying, when you’re
looking at marketing clouds. The one thing that they
don’t tell you about is that for every dollar in licensing, they look to spend between
four and eight dollars to either integrate or
manage on an ongoing basis, those platforms. So a lot of times the
salesperson for those platforms is in there telling the
story about hey look, this tool is gonna solve this problem, and it’s very compelling,
but the problem is there’s another four to eight times that spend in managing that. So that’s something that
you got to understand and prepare for. Moving on to walled gardens. We’ve talked quite a bit about them. I think we’ve kind of kicked
them around a little bit, and the reality is there are strengths, and we see a lot of clients, depending on the problems
they’re looking to solve, maybe just find working in this space and saying look, I’m gonna
take my consumer base and I’m gonna go fish where the fish are. I know where they’re at, and
I’m gonna market to them, and I’m gonna trust that that’s effective. And for several clients,
that’s all they need and that’s where they
spend, and that’s where they spend a bulk of their spend
and that works for them. But for everybody else,
and as we’ve heard today and I’ve heard it in this room, very few people are looking
to put all their eggs in that one basket. So looking to exchange,
looking to work across that is where most people are
looking to take their platform investments, so it’s
something to keep in mind as you consider that. The final frontier, and this
is sort of that new area that I talked about in the introduction. This is that idea of
customer data management, and this is an evolving space. This is really my sweet spot,
this is where I grew up. And for Epsilon, we built
these as one off custom databases. We used to start working with a client and a blank whiteboard and we’d say, what do you want to do? Great, what data do you have? And we’d evaluate that and
we’d put that together. More and more companies are looking for, the other thing about that engagement was every time we go into a client, even in a particular
vertical, we would say look, is there somebody else
you would want to model your business after? Somebody else you want
to model your marketing? And we were told nope, we’re
unique, we’re different, we do things completely
different than anybody else, this has to be a custom solution. Nowadays over the last two
years, CMO’s and CEO’s are saying if you can give me 80%
of the functionality out of the box and
prevent me having to build and manage my own monument technology, I would rather do that. So this is where about
three or four years ago, this idea of managing it
yourself with customer data platforms, customer
data management platforms, and there’s a huge list of different types that you see up there,
that are all up there competing in the same space, and they’re all saying the
same thing in a much smaller sphere around understanding
your consumers. And this is a report from
Gartner that came out earlier this year, to help people
understand what CDP’s are. A year and a half ago, no
one knew what a CDP was, now everybody believes
they have to have a CDP. And the reality is when you
look at what platforms do, and this is really important. I’m not gonna go through
everything on the left side there, but I think it’s important to talk about the categories on the top. Across the top you’ve got
the idea of data collection. So this is the idea of
bringing data together, putting the identity
together to that keying it, bringing it together, fleshing that out with profile unification,
which is a deeper level of that identity resolution. Taking your online and your offline, matching it together,
that takes you into that segmentation layer, so you now
know who your consumers are. How do you bucket them into audiences and groups that you want
to drive behaviors in? How do you then activate on those? And this is a really important one. Activation versus native
marketing execution. What you find a lot of is these platforms will talk about the ability to activate, and the reality is, you
can activate an audience by pulling a list and
handing it off to any number of providers. So that’s activation,
that’s the definition. How can you orchestrate that? And there’s a level of complexity in that that gets very very deep
and very sophisticated if this is the type of client, the type of audience you’re looking for. How do you manage multi wave touchpoints. That if they saw this banner
ad and then they clicked through the website, do I
then send them a follow up email, and if I sent
them the follow up email and it wasn’t read, what do I do there? That’s that level of activation, and then the native marketing execution is do I have the ability to
actually push that message in my channel. And so this is where
you’ve seen some of these big providers buying up email platforms so that they can drive that
and drive the push messaging and those types of things. All of them getting into that space. The next one there, and this
is a really important one and this is an interesting final frontier for a lot of people. The marketer managed. We worked at Epsilon and
Conversant in the Fortune 500 enterprise client set. The idea of marketer manage
works really really well in a single brand organization, or maybe a 800 pound gorilla
brand within an enterprise type organization. Marketer managed means that
the marketer is in control of ingesting the data,
bringing it together, and then driving through
all the activation and orchestration. And the reality is
stitching that data together has a level of complexity, it’s a very unsexy part of this, but it’s about managing that metadata and the data about the data. How that data’s stitched together? How do you bucket large sets of data? You bucket it in groups of five or do you give the actual identity, or the actual integer
number for each of those. That’s a very unsexy part of it, but if a brand does it one way and another brand does it another, then trying to do that
across an enterprise is very very complicated. So we see that marketer
managed is being something that everybody’s looking for, and it’s definitely
something we’ve pushed for in the segmentation and activation. But the idea of ingesting
and bringing that data together in an enterprise level is something that is best
left to IT organizations or system integrators
or others to drive the least sexy part of this
which is data governance, and how do you manage that across that, so that data is consistent across brands and relative to each other
at the enterprise level. So marketer managed is
a very big buzz word that you’re seeing driven
through the CDP platforms today. But as you see, it’s not really
done in many other places. And then the final one
is real time decisioning. This is obviously critical. This involves both the ability
to ingest data in real time, get it back out, decisioned
with some sort of real time decisioning machine learning that builds and learns on it’s own modeling, and drives those results out. And again, this is where
we think the industry will leapfrog. A lot of the sort of the
orchestration levels, but that’s gonna take time. There is a lot of technology debt. There’s a lot of investment in time and organizations that
manage those work flows. Letting the machine do
that is something that’s technically capable
today, but is something that will take challenges, you know, hurdles to come
organizationally over the next several years. So, all of that, those
are the tough decisions around the platforms if you will, and then the final
question is, who is going to do the work? This is one we talked about earlier, that on the walled gardens in particular and true on the customer
data management as well, there is a licensing cost and
then there is a people cost. And that people cost is not
just in terms of the bodies it takes to do the work,
to push the buttons, to drive the integration,
but it’s also the expertise and the know
how to say hey look, if I do this, is it gonna
drive efficiency downstream? Is it gonna be manageable
in the next six months? How do I take care of that? And there’s really several options here. A lot of agencies do this type of work. A lot of the big consultancy
firms to this work. A lot of what you’re
seeing is very much like in the late 90s, where all
of the accounting software was sort of centralized into one platform, the big consultancies built practices around that. You’re seeing that now with platforms that those consultancies
are building expertise bases behind that to drive
off of these platforms. So the good news on this
front is that there are people out there to do this work. It’s definitely worth considering
as you budget for this, that doing this with
outside help is critical because one of the biggest
challenges that I have found organizations have is
managing the career path for people that integrate or
manage these systems overtime. A lot of companies have
made a big investment in a big cloud provider technology, they’ve hired somebody,
they thought that it could take care of the job. That person comes in, builds their resume, halfway through their project, and leap frogs their career,
and they’re left holding you know, half build,
half integrated technology and try to chase that down. So the decision, not just
around the technology platform, but the availability of
resources is a critical decision as you look at decisions around
what you’re gonna decide. We’re gonna wrap with a case
study around a retailer. It was a multibrand retailer
that had a big challenge. And I would love to tell
you that they picked one platform, it was ours, Mesobase, and it solved all their problems, and that would be a fantastic case story for us, case study for us. The reality is, it’s deeper than that. This was a client of ours that already had big chess pieces on the
board with Oracle and Adobe, and it was up to us to help them make sure they were leveraging those,
not just for the length of the contract they had left, but for the technology debt
they had already paid into it that could go even longer. So we worked with them, not
just to stand up a platform we called Mesobase, but we
worked with them to build out a strategy, and a
technology road map for them that took and helped them focus and articulate very clearly the problems they were trying to solve
with their platform decisions. We helped them build the
road map and prioritize the problems they were
gonna solve at which point, so that everybody had
transparency and visibility into what to expect. And then we helped them
even with some of the organizational decisions
around what does their organization need to look
like today and tomorrow, to support the evolution of this platform decision as they go forward. And then putting in the
governance and the processing around that to help them
get the most out of, not just the platform
we delivered for them, but the systems they already had in place. So, as you get to making
decisions about which platforms to put in place, which
ones to implement into your ecosystem, it’s important
to know what problems you’re trying to solve. It’s important to internalize, and that means the resources, skill sets, those types of things,
only where critical, only where it moves your business. Focus on what your business is, where those people have career paths within your organization. Drive to measurement, excuse me. Drive to measurement. As Dave talked about earlier,
measurement is the only way that you’re able to see the results and articulate them very clearly. And that doesn’t just happen with math, with good data, it
takes a drive and a push and a prioritization of that
within your organization. Everything should be measured, and everything should be
measured from front to back. And then finally, budget. Budget not just for the licensing cost, not just for the infrastructure, not just for the operational cost, but budget for the know
how and the expertise and the skill sets to
stand up and help you drive the most value for the investments that you’ve made and those
that you’re about to make. With that, I will wrap for questions. There’s one question on the thing. We got one on the screen, and then, where would you rank
attribution in terms of, oh that’s from earlier. Okay, we got another one growing? Oh, do we want to come back to, is this one that Dave answered today, or? Okay, so it’s funny
you say the must haves, attribution and identity
are bookends in our opinion. Your attribution with bad identity leads to fragmented or bad attribution. Whether Dave is one person or two, if you see him as two, then you’re gonna get
double counts on what works or half counts where
it did or didn’t work. So for us, identity and
attribution go hand in hand. When you get the attribution right and you have the transparency
and all the data, the attribution is just math. It just adds up and
gives you what you need. So for us, it goes hand in hand. Alright, any other
questions, online, in house? Fantastic. Well thank you all very much,
we appreciate your time, and enjoy the rest of the show. (applause) They’re on the other side, oh thank you. Yes, hey. – [Man] Nice presentation. – [Carl] Thanks, thank you. (inaudible speech)

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