1. Awareness:
TV Ad
Online Ad
Awareness:
TV Ad
Online Ad
Interest:
AdWords
Interest:
AdWords
Consideration:
Search/Paid Content
Consideration:
Search/Paid Content
Purchase:
Online Shop
Purchase:
Online Shop
White PaperCustomerJourney&Attribution
CUSTOMER JOURNEY, OR THE RUN-UP TO THE GOAL: DEFINITION AND PHASES
What does “customer journey” mean? And what is the
difference between a “customer journey” and a “user
journey”? Both focus on observing or analysing the path
that a customer takes, from the initial contact with a
product through to a possible decision to purchase the
product. However, with the customer journey, the focus
is only on the purchases actually made, while with the
user journey, the journeys taken by all other users are
examined. Whether online or offline, the phases that the
user passes through are identical: awareness (recognising
a lack or a need); interest (looking for information or
alternative products); consideration (assessing the
advantages and disadvantages of each option); and
purchase (making the purchase on the basis of an
impulse, e.g. a good-value offer on the desired product).
On average, 40% of the turnover generated by an online
retailer comes from both regular customers and repeat
customers.1
Consequently, it is a good idea to think
about the period that follows the purchase of a product:
retention (seeking confirmation that the right purchase
was made) and, finally, brand advocacy (identifying
with the brand and actively communicating one’s own
decision to the outside world).
1
Source: http://success.adobe.com/assets/de/downloads/whitepaper/13926.digital_index_loyal_shoppers_report_de.pdf, Annex
“Ausgewählte Daten und Fußnoten” [“Selected Data and Footnotes”], Table 2: Umsatz in % nach Besuchersegment, Zahlen für
Deutschland: 12% Wiederkäufer, 26% Stammkäufer, 62% Erstkäufer. [Revenue in % by visitor segment, figures for Germany: 12%
repeat purchasers, 26% regular purchasers, and 62% first-time purchasers.]
What do world-class football and efficient online marketing have in common? If your
answer is “Nothing,” you’d be wrong! The fact is that both are team sports in which
success is dependent on every position in the team being filled by the perfect person
for the role. Every single person needs to know their function in the team as well
as the game plan inside out, and be able to put the latter into practice. Whether
you’ve got your eye on the client or on the ball, in both worlds, the focus is on finding
the most direct way forward and clinching success. In this white paper, we’ll show
you how to identify both the good and bad players in your team, how to avoid bad
passing, and how to boost your completion rate.
Assemble the right team to achieve success:
How you and your team can turn prospects into customers
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PR
Radio
TV
Print
Word of Mouth
Online Ads
Email
PPC
Social Ads
Reviews
Blog
Media
Website
Community
Forum
Newsletter
Social Networks
Blog
Email
E-commerce
Store FAQ
Knowledge Base Promotions
AWARENESS INTEREST CONSIDERATION PURCHASE RETENTION ADVOCACY
A TYPICAL MULTI-CHANNEL CUSTOMER JOURNEY THROUGH ALL SIX PHASES COULD
LOOK LIKE THIS:
A customer’s potential touchpoints along their customer journey—from initial contact through to brand advocacy.
Generally, customers engage with 3–4 touchpoints before making a purchase.
Here’s a real-life example to illustrate this: John saw
some new football boots in a TV ad, and is interested
in buying them (phase: awareness). He types “football
boots” and the name of the brand into a search engine,
and clicks on one of the many adverts that come up. He
looks at his new favourite shoes on various websites,
but does not buy them (phase: interest / finding a
supplier). Later on, when John’s chatting on Facebook,
and then browsing through Amazon, adverts for
those same shoes keep popping up (phase: interest /
collecting information). As a result, he starts to make
detailed comparisons to find the cheapest supplier
(phase: consideration). He finally decides on a shop and
places his order (phase: purchase). After he’s paid, John
has a look through the brand’s website to make sure
that his decision was the right one (phase: retention).
When the football boots are delivered, he’s absolutely
thrilled. He is proud to tell the other players on his team
how comfortable the shoes are, and raves about the
innovative materials that they are made from (phase:
brand advocacy).
THE WHOLE SQUAD HAS TO GET ON: TEAMWORK AND THE ROLES OF VARIOUS
MARKETING PARTNERS
As you can see, the customer looks for, or needs, the
right information at each phase of the decision-making
process so that they can take a step towards making a
purchase. This is where marketing partners come in: the
striker can only put the ball in the back of the net, or
the customer can only make a purchase, when all the
marketing partners work together like a football team,
passing the customer from one touchpoint to the next.
Sloppy passes lose the ball—or, in this case, the customer
leaves their own conversion funnel and is instead lured
away by a competitor. You have to tackle this from all
positions and with all the resources at your disposal to
win back the customer.
But all that’s easier said than done; unfortunately, this
bears little relation to reality! The compensation models
currently used, known as “attribution”, are to blame:
so far, only the players that actually led the customer
to make a purchase have been compensated. The
connection to sport is particularly noticeable here: the
person who scores the goal goes down in history, and
everyone else sinks into oblivion.
The second section of this white paper will reveal why
this is utterly counterproductive in terms of online
marketing, and why the goalkeeper and defenders
shouldn’t try to score all the goals themselves.
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The customer journey needs to be tracked, or made
visible, so that the influence exerted on the purchase
decision by the various players at each touchpoint can be
understood and measured. Unlike in offline commerce,
this can be achieved seamlessly online thanks to tags
and cookies—and in real time.
This means that you can react directly to customer
behaviour, for example, by showing the customer
adverts that are appropriate to their stage in the
purchase process, and thereby “leading” the customer
to make the purchase. This makes it possible to shorten
the purchase process, reduce CPA and, of course,
improve conversions. A “cookie switch” is used to
allocate conversions. In this process, the rules previously
determined by the attribution model, or the marketing
team (customised attribution), will be used on the order
confirmation page to distribute out “winning tags” that
mark the players who are to receive compensation.
These rules can be formulated so that there isn’t just
one winning tag in play: instead, several different
tags could be up for grabs. Modern enterprise tag
management and attribution solutions can calculate the
amount of compensation dynamically, depending on the
attribution model used, and distribute this proportionally
to the channels involved in the success. To date, cookie
switches have primarily been used to determine which
marketing partner triggered the final impetus before the
purchase was made. Even today, this partner will receive
the entire amount of compensation, while the other
channels involved in the conversion get nothing.
The fact of the matter is this: only people who are
rewarded appropriately for good performance will
continue to demonstrate commitment and do all that
they can to fulfil their role in the team to the best of
their ability. For example, a display advertising campaign
primarily designed to raise awareness does not have to
generate great sales figures. Even if this is “just” the start
of the customer journey, the campaign is still successful
if it has met its objective: namely, to have a long-term,
positive impact on brand awareness, or to generate
awareness of the product. The same is true for players
in different positions who provide the customer with
information but who do not generate direct sales: they
too have an important role to play in the conversion
funnel, and deserve to be rewarded—to the same extent
as the final marketing partner who clinches the sale.
But that is precisely what is lacking at the moment, and
often with grave consequences: sales are sometimes
compensated more than once because multiple “winning
pixels” are in play. New customers are forwarded by
affiliates onto discount pages that were originally
intended for retargeting, and all marketing partners
focus on the last phase of the customer journey because
that is where the money is to be earned. It’s high time
that attribution strategies were rethought—and with
them, compensation models for online marketing.
WHO WINS THE TROPHY: WHY HAVING THE RIGHT ATTRIBUTION MODEL CAN WIN
YOU THE GAME
There is yet another obstacle in the way of determining
conversions. So far, we’ve only talked about clicks;
however, the number of “clickers” is actually relatively
low when compared with the number of users who see
an advert, and then later head to the seller’s website to
make a purchase.
It is estimated that only 10–15% of conversions are
“post click”, i.e. a direct click through, while 85–90% of
conversions are “post view”, i.e. when a customer saw
an advert but did not click on it.2
It is possible to assess
post-view rates because a cookie is stored on a user’s
computer even when an advert is displayed. However,
it is impossible to say with any certainty whether the
user actually noticed the advert: some affiliates place
adverts at the bottom of the page, or use small banners
featuring a multitude of brands so that they can receive
compensation for as many conversions as possible. This
is why many online marketing experts rate post-click
cookies higher than post-view cookies, even if the post-
click cookie was stored at an earlier point in time and did
not lead to the purchase.
PUSHING UP THE PITCH WITHOUT TOUCHING THE BALL? POST VIEW VS. POST CLICK
2
Source: https://www.comscore.com/Insights/Press-Releases/2009/10/comScore-and-Starcom-USA-Release-Updated-Natural-
Born-Clickers-Study-Showing-50-Percent-Drop-in-Number-of-U.S.-Internet-Users-Who-Click-on-Display-Ads, 2nd paragraph:
“Today, marketers who attempt to optimize their advertising campaigns solely around the click are assigning no value to the 84
percent of Internet users who don’t click on an ad.”
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However, a part of this display advert (estimates put this
at around 20%) had an influence on the purchase and
this should be taken into account when determining the
“winning” touchpoint, provided that the purchase was
made within the fixed lookback window following this
kind of advert.3
Ad viewability solutions let you determine—with a high
level of accuracy—whether, and for how long, adverts
were displayed in the visible area of websites, mobile
apps, or in videos. This means that it is essential for
solutions of this nature to be integrated into customer
journey tracking, particularly for clients whose
advertising focus is on banner ads or videos.
Broad conclusions can also be drawn from an analysis
of the performance of the various attribution models
(post click and post view). These are very important for
evaluating the success of display advert campaigns, for
example. Even the origins of the views play a role; for
example, views from display advertising or retargeting
should not be given as much weight as those that come
from videos and other viral media.
“TRIED AND TESTED” ATTRIBUTION MODELS: LAST CLICK WINS, FIRST CLICK WINS,
BATHTUB MODEL, LINEAR DISTRIBUTION
We will now take a quick look at the four static attribution models. “Last
click wins” is certainly the most frequently occurring variant. Its success is
firstly due to its ease of use, and secondly due to the assumption that it can
increase conversions and, by extension, turnover in the most sustainable way
because it rewards (or seems to reward) direct involvement in the sale. No
other touchpoints along the customer journey are taken into account—only
the person who scores the goal is acknowledged. This means that, at the end
of the day, all the players on the pitch—and not just the strikers—are only
interested in scoring goals.
The “first click wins” model rewards the customer’s initial touchpoint, or the
marketing partner who guides the customer into the conversion funnel in the
first place. This touchpoint definitely plays an important role, because without
it, the customer would have probably ended up at a competitor company.
However, this model is just as unsuited to fairly compensating all the players
involved as the last-click model, and for the same reason: all the other players,
and consequently all the other important functions along the customer journey,
go home empty-handed: all that matters is getting the ball.
Real-world example 1: A marketing manager is
optimising on the basis of earnings contribution, and
opts for the last-click model. After a while, he realises
that retargeting led to the last click prior to purchase in
80% of cases. As a result, the marketing manager focuses
exclusively on retargeting. After 3 months, traffic to the
site plummets—as does revenue. No new channels have
been targeted to bring new potential customers to the
website and, as a result of new seasonal products, his
retargeting campaign has run itself into the ground.
3
Source: http://www.goldbachgroup.com/de-ch/news/ohne-klick-zum-kauf-chancen-und-risiken-bei-der-messung -von-post-
view-conversions, summary at the end of the article: “It is currently impossible to give an accurate measurement of the number of
post-view conversions for online performance. At the moment, the sector has to make do with estimations. Post-view conversion
values of around 20% are currently realistic.”
Real-world example 2: A board of directors has given
its approval for an expensive advertising campaign, and
would like to see the results of this. As a result of the
typically low click rate for display adverts, the traffic to
the site has dropped, and retargeting partners have come
away empty-handed. The visibility of the page has been
reduced because of the low traffic volume, which in turn
has a negative effect on ongoing AdWords campaigns.
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Real-world example 3: The person responsible for
e-commerce at a fashion retailer uses a linear attribution
model along with cookie switches. When comparing
analyses with the last-click model, he notices that
compensation payments are more than three times as
high, because all the performance channels that lead to
a purchase are being rewarded.
Real-world example 4: The online manager of an
insurance company has decided to switch from a last-
click model to a bathtub model. Under this model, the
first and last channels leading to an application form
for an insurance policy being successfully filled out will
receive 35% of the compensation. The remaining 30%
of the compensation will be equally divided between
the intermediate channels along the customer journey.
Initially, the manager is faced with some challenges
because compensation-driven channels want to
guarantee 100% of the compensation going forward.
By analysing customer journey data and calculating the
additional income resulting from the fairer spread of
compensation under the model, the manager can prove
that the new model is working for her performance
partners, and then adjust her contracts accordingly.
Lastly, the “bathtub” or “u-shaped” model is a combination of the three previous
attribution models: the first and last touchpoints are given a heavier weighting,
while those in the middle are given a less heavy weighting. This is a good
approach because the two important touchpoints at the start and end of the
customer journey are taken into account, and nobody goes home with empty
pockets. However, there is still no examination of the actual influence that each
player exerts on the customer’s decision to make a purchase. Winning the ball
and scoring a goal are rewarded more than successful passes in the midfield.
“Customised attribution”: Developing your own attribution model is a solution
that requires a more precise knowledge of the customer journey, along with
experience and a light touch. The aim: mapping out the involvement of every
single marketing partner as accurately as possible, and rewarding them based on
the results. This is no easy task thanks to the sheer quantities of data involved,
which need to be analysed! Without professional attribution management,
this is nearly impossible to do with precise, highly granular data. This is how
a world-class team should play: adapting to each individual situation, while
sticking to a previously determined strategy.
“Linear attribution” aims to provide some assistance here by equally rewarding
all the partners involved in a successful customer journey. In principle, this
approach is as simple as it is understandable, or even sensible. If only it didn’t
ignore the matter of the efficiency of each player. All you need to do to get a
share of the compensation is put just one pixel into play at some point along the
customer journey. The role played by the individual partners, and whether they
are doing their job well, is not questioned. Good players, bad players—the main
thing is that you’re on the team.
Real-world example 5: The person in charge of
campaigns at a travel portal would like to take into
account a range of factors, such as the intensity of
interaction (click vs. view), different cookie run-times per
channel, and temporal proximity to conversion so that
the contribution that each channel makes to success can
be represented as precisely as possible. This leads to a
model that uses a mathematical formula to distribute
compensation variably to each individual channel, which
is then rolled out across all channels.
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Maximum efficiency, minimum transparency: dynamic
attribution. In this model, algorithms take over the
game play completely, and determine how much
each touchpoint contributed to success. Advantages:
optimum effect because the dynamic attribution model
“learns as it goes along” and compensates the best
touchpoints in each case; additionally, clusters aren’t
required because every customer journey is viewed
individually and can be influenced in real time in line
with previously determined rules. Disadvantages: the
marketer can hardly understand why conversions
are going up or down because there is no longer any
insight into the system; they have to rely blindly on
the attribution software. In addition, introducing
dynamic attribution requires a radical rethink in terms of
organisational structures and responsibilities.
In order to introduce a dynamic attribution model and
handle the compensation of individual performance
channels in a similarly dynamic way, a company needs to
set aside the silo mentality, and subordinate everything to
one overriding objective: usually, maximum conversions.
Everyone involved has to work together, cross-channel:
the people who were previously “responsible” for each
channel have to give up some of their responsibilities
and become part of a large team striving towards a
common goal. The introduction of a dynamic attribution
model can only be successful if the organisational
structure and corporate culture permit such significant
restructuring. For example, compensation for existing
partners needs to be renegotiated, and the shortfall in
contractually assured compensation for the term of the
existing contracts needs to be made up.
If you do not meet these conditions, you should stick
with rule-based, individually managed attribution models
(customised attribution); these already offer a huge jump
in efficiency compared to simple models.
Summary: Even if the right path seems to be clearly
marked out, many challenges need to be overcome
before you can set off on your journey. Individual
players who previously focused solely on their
own performance need to come together to form a
functioning team striving towards a common goal. It
is doubtful that this will work in every situation with
the existing players. Under certain circumstances, roles
will have to be redistributed, and tasks and objectives
redefined. Teamwork and the “goal-to-shot ratio”, or
conversion rate, can only be optimised and improved if
everyone accepts the previously defined strategies and
subordinates themselves to the common goal. There
is no room on the team for lone rangers: everybody
needs to perform as well as they can in their position,
and be rewarded accordingly.
Real-world example 6: The product manager at a
comparison portal is carrying out a data analysis and
realises that a different attribution model suits each
individual portal category (e.g. travel, insurance,
time deposits, electricity, etc.) and each time period.
Different factors such as the amount of money invested
in the customers, scarcity of the products, decision-
making periods etc. all play an important role. Click
prices and success rates vary wildly for each channel.
A flexible, dynamic model is the right way to make the
most of the marketing budget and campaigns. The raw
customer journey data is exported to a big data solution,
enabling the manager to determine correlations time
and again, and to construct new clusters and models.
A real-time interface and a combination of BI and the
attribution management and tag management solution
can enable dynamic algorithms to be used to control
attribution models.
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Determine your objectives and start off with simple models: What do you
want to achieve with attribution? Are you focusing on budget allocation,
reassessment of partners/campaigns, or options for automation (keyword: bid
management)? Learn how to understand data, correlations, and processes within
your company—and within your advertising partners and agencies. Classify your
campaigns (branding, performance, upselling, lead generation, etc.) and channels.
Complex and dynamic models can quickly turn into a black box full of pitfalls, and
should only be used when you have a firm grasp of other topics. The initial focus
should be on the objectives that you want to achieve with the help of attribution,
such as budget allocation.
Carry out a descriptive analysis of the customer journey and determine what
data is available, and of what quality: Are all campaigns being tracked cleanly?
Analyse the data that you have available: how great are the discrepancies
between online and offline data in terms of products sold and revenue? You
should take cancellations and returns into account when doing this. Before you
can manage an attribution model, you need clean, cross-channel data tracked
as a unit that combines off-page, on-page, and CRM views. Which channels
typically play the role of “introducer”, and which ones play the role of “closer”?
Which other channels between introducer and closer have the most influence
on the customer journey, and in what ways? Can offline conversions (purchases
in brick-and-mortar stores, from a distributor, on the telephone, etc.) also be
taken into account?
Seek out and recognise potential quick wins: How many touchpoints per
journey? How many journeys consist of just one channel? Is there any scope
for optimisation based on reweighting touchpoints? Are there recognisable
differences in terms of revenue when compared to multi-touch journeys? Are
there touchpoint chains that don’t lead to conversions? Take into account
usability aspects, such as funnel analyses. Eliminate errors and usability obstacles
for website users via website optimisation (A/B testing), so that these don’t slam
the brakes on your budget optimisation.
Lay the foundations for more complex models:
a) Organisationally: Launch an integrated marketing team approach (do
away with the silo mentality and stop thinking in terms of channels/
areas of responsibility). For example, this could occur by changing the
marketing targets into cross-team targets and KPIs. Ideally, online
marketing, CRM, analytics, and business intelligence teams should
all work together. Important: avoid creating a black box. Every model
should maintain clarity and only be as complicated as necessary. In
contrast to static models, even simple individualised models bring in
a lot of added value!
b) Contractually: Look through performance marketing contracts.
Discuss and agree on new models of compensation with partners,
if required.
5 TIPS AND TRICKS FOR YOUR PATH TOWARDS ATTRIBUTE-SUPPORTED
OPTIMISATION OF THE CUSTOMER JOURNEY
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3
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c) Technically: Maintain your marketing team’s IT independence and
agility by introducing an enterprise tag management solution with
high data precision and advanced cookie switches. Ideally, combine
a tag management solution with attribution management and data
management solutions, so that you have the option of exporting raw
data and importing offline data.
Propose optimisation hypotheses to start with: Generally, you want to have as
perfect a budget allocation as possible for each channel. The aim of the game is to
boost conversions by improving budget distribution. Analyse current data and start
off by using a simulation to compare the possible impact that the various models
could have on budget allocation and compensation. Then get your performance
marketing partners or agencies and employees on board. Test the new attribution
model on a clearly defined target group. What impact does it have? Is your
hypothesis correct? Finally, roll out the new model to other campaigns and product
categories, and compare to see if your assumptions play out.
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W W W . T A G C O M M A N D E R . C O M
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their marketing performance. TagCommander’s solutions
enable online marketing executives to collect data
across a range of devices and apply this to all channels.
Once collected, the information on target groups can
be analysed, and relevant data can be communicated
to e-marketing solutions. The outcome is precise
segmentation, targeted customer approaches, and
effective personalisation. TagCommander is based in
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