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The Complete Guide to Marketing Attribution A MARKETER’S GUIDE TO REBUILDING MARKETING ATTRIBUTION www.queryclick.com
Contents 04 Marketing attribution: the basics
09 Marketing attribution models explained
15
The problem with current attribution
solutions
18
Why your data is the foundation for your
attribution success
26
Marketing attribution: a guide to your
choices
33
A new approach to attribution: visit-level
attribution
38
The powerful data views you need to
accurately drive marketing ROI
40 Closing thoughts
ATTRIBUTION PLAYBOOK 3
Overview The purpose of attribution is deceptively In this guide, we are going to take a close
simple: to most fairly share the value of a goal look at some of the key aspects of attribution
conversion across all touchpoints that may including:
have influenced that conversion. However, in
the real world the complexity of touchpoints • What marketing attribution actually is
and media opportunities in marketing create • Why it matters more now than ever
an attribution challenge, even in a perfect
• An introduction to the main attribution
world of data availability.
models including some of their limitations
The customer journey is now very often • Why data is key to all of this – and some of
a highly complex one. And being able to the challenges and opportunities around
attribute the impact of specific marketing collecting data across websites, offline
touchpoints is crucial, as pressure from media, social platforms and CRM/CDP/ERP
internal stakeholders - including finance and
• How techniques like Machine Learning and
the boardroom - to link marketing to revenue,
Deterministic and Probabilistic matching
and prove ROI intensifies. Unravelling the
can help overcome limitations in current
impact of specific touchpoints on conversion
attribution approaches
is priority number 1 for marketers.
• How all of this provides effective attribution
In fact, its relative importance was summed at a Channel, Campaign and Impression
up when Google’s marketing evangelist, level to better focus and optimise your
Avinash Kaushik, described solving attribution marketing efforts so that they are more
to identify incremental value across all media closely aligned to the customer journey
channels as the biggest problem facing itself
analytics in 2020. So, attribution matters.
Marketing attribution:
the basics
ATTRIBUTION PLAYBOOK 5
Marketing So, what is marketing
attribution?
attribution: the
basics
Display
Put in its simplest context, marketing Paid Search
attribution is the process of determining Email
Social
which marketing touchpoints and activities
are contributing to conversions – which
could come in a variety of shapes and sizes
??% ??% ??% ??%
from sign-ups, to downloads, to purchases
of a product or service – or some other An attribution model, or combination of
meaningful conversion types. models, is a defined set of rules that helps
you determine how conversion credit is given
In a complex marketing environment where to these different marketing touchpoints
marketers are using an ever-increasing mix of across the customer journey. They come in all
channels simultaneously it is about unpicking shapes and sizes – but are split broadly into
the impact that channels, campaigns and two types: single-click and multi-click. But
even individual creative executions are having more detail on those later.
across media like Display, Paid Social, Search
(Organic & Paid), email and offline media. What’s important to state here is that, if you
don’t have effective marketing attribution
in place, then it’s a fairly safe bet you don’t
know what is – and just as importantly, what
isn’t – working across your marketing mix. So
accurately assessing and improving “true”
marketing performance isn’t really possible
for you.
ATTRIBUTION PLAYBOOK 6
Why marketing attribution
matters. More than ever before.
So, attribution matters - and it always has. and digital is a growing part of the mix. By
2021 the volume of data in silos involved in
At the most fundamental level the sheer delivering just digital marketing, will have
scale of current investment in marketing increased in complexity 32-fold compared to
demands effective attribution. In the UK alone a decade previously.
marketing spend will top £21.3bn in 2020
The figures are staggering and, as a result,
many businesses are struggling to sustain
Marketing spend Vs Silo complexity (Global, US$ BN.)
growth while maintaining or reducing media
$900 80%
spend budgets. Which – and to echo Avinash
$800 70%
Kaushick - makes solving attribution the
$700
60%
largest opportunity any marketer can engage
$600
50% with today.
$500
40%
$400
But there are also a number of other key
30%
$300
reasons why having an effective marketing
20%
$200
32x increase in silo complexity by volume attribution solution in place is more important
$100 10%
than it has ever been, as it enables you to:
$0 0%
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Traditional Ad Spend Programmatic Ad Spend Facebook Revenue Amazon Revenue
Other Digital Ad Spend Traditional %age of Total Digital %age of Total
Source: eMarketer Global Digital Marketing Report & Statistics.
ATTRIBUTION PLAYBOOK 7
Link marketing spend directly to growth and Prove your value to the business and build
revenue stakeholder trust
Which is non-negotiable now. The pressure Effective attribution builds credibility and
for marketers to spend accurately, efficiently trust with the key stakeholders closest to the
and to prove marketing effectiveness in marketing function including the boardroom,
driving growth is greater than ever. And finance – and, perhaps most importantly,
is only going to intensify, so directly tying sales. Securing the buy-in needed to support
activity to revenue is no longer a “nice-to- your marketing vision and fast-track future
have”. It’s essential. plans.
Keep full control over the nature of marketing Secure and ring-fence budgets based on
spend sound marketing analysis
Not only is there broad evidence that internal Marketing budgets are under the microscope
“Internal stakeholder pressure restricts my option to stakeholder pressure on spend - from outside more now than any time previously. By
employ marketing activity that has a longer payback
period than last-click measures.”
of marketing - is increasing but there are also accurately being able to show how marketing
worrying signs that it is directly influencing is contributing you have the capability to
Strongly agree
24% the nature of spend. In fact, research carried ringfence and even grow your budget.
out by QueryClick points to the fact that
Somewhat agree
43.5% 67.5% of Marketing Directors report that Improve your marketing ROI
internal stakeholder pressure actively restricts
No opinion either way Ultimately, effective attribution helps you
19% the option to employ marketing activity with
drive improved marketing ROI by finding the
longer term payback.
Somewhat disagree parts of your program that are working - and
11.5%
those that are not. This the allows you to
Strongly disagree redirect spend to where it is going to have the
2% Base: all respondents, n=200 biggest revenue impact.
ATTRIBUTION PLAYBOOK 8
Reduce reliance on perceived short-term Take control of marketing complexity
media ‘fixes’
Finally, better attribution enables you to
For many businesses, media like Pay-Per Click take control of what is ultimately a more
is the main channel. In fact, everyone loves it challenging and complex media environment.
because it provides immediate results. Switch
it on and revenue flows when you spend. But Where customer journeys are taken on and
it is no longer the panacea it once was. High offline, and research can be done across a
spending competitors are not only eroding multitude of devices including smartphones,
market share but driving up CPAs. So, ROI is tablets, work and home PCs, and mobile,
being driven downwards. Effective attribution effective attribution enables you to bring
opens the door to wider media opportunities, together the data silos across this complex
with longer payback cycles – because you environment to define a single, data-driven
can pinpoint the data you need to make the view of the customer journey.
business case for it more effectively.
And confidently build your marketing
approach around it.
Marketing attribution models explained
ATTRIBUTION PLAYBOOK 10
Marketing In this section we are going to take a closer
look at attribution models.
Single-channel attribution
attribution One of the key things to understand upfront
These models are relatively simple, and work
on the basis of allocating 100% of the credit
models about attribution models is that there
isn’t a definitive, one-size-fits-all answer
for a revenue conversion or other customer
explained
behaviour to a single touchpoint with the
for everyone. And, in practice, identifying
customer, either at the beginning or the end
which model is right for your brand or your
of the customer journey. All other touchpoints
business is going to be impacted by a range
in between are ignored for attribution
of different factors from the stage, scale and
purposes.
maturity of your marketing activity to how
much time, budget and effort you are able to Works well Where you have a simple funnel
apply to it. environment with very few marketing activities. And
businesses who are scaling from a low base may well
employ them early in their growth.
Below we take a whistle-stop tour of each in
turn. Including where they might work well Possible limitations However, they oversimplify
and where they also have limitations. and ignore the impact of multiple touchpoints on
complex, longer customer journeys.
In broad terms they split into two broad types
There are two types of single channel models
– single-channel attribution and multi-touch
– First and Last-touch attribution.
attribution. Let’s look at these in turn.ATTRIBUTION PLAYBOOK 11
First-touch attribution Last-touch attribution
100% 0% 0% 0% 0% 0% 0% 100%
Built around the premise that the first Similar to First-touch attribution, Last-touch
touchpoint with the customer is the crucial is predicated on allocating 100% of the credit
determinant in a customer’s decision to do for conversion to a single touchpoint.
business with you.
However, in this case it allocates credit on the
So, 100% of the credit for revenue is directly basis of the last touchpoint prior to purchase
attributed to the first interaction in the or revenue. And similarly, it gives zero credit
customer journey. For example, if a customer to other interactions along the way.
comes in on an eBook download it gets
Works well Where you have a heavy focus on
all the credit – regardless of all the other
optimising conversion around the bottom end of the
touchpoints in between that and the sale. funnel.
Works well Where you have a simple, early stage Possible limitations But again, it’s going to be
marketing model that is heavily focused on growth and much less appropriate for analysing longer and more
quickly scaling your business. And where there is a complex journeys accurately.
strong focus on growing top of funnel.
So, taken together, Single Channel attribution
Possible limitations However, it is going to be
models do have their benefits – and you may
much less appropriate where you have a lot going
on in your marketing funnel and a more complex and well start your journey using them in early
extended buyer journey (of 90 days plus). growth stage scenarios in smaller businessesATTRIBUTION PLAYBOOK 12
– but it is also highly likely that even an initial Linear
spurt of growth will see you outgrowing their
usefulness. As your marketing evolves and
matures, your need for more sophisticated
attribution is likely to follow suit.
25% 25% 25% 25%
Multi-touch attribution Very often you don’t want to give single
models channels or campaigns all the credit for
conversion and a Linear model moves the
These models take a broader view of principle on by applying equal weighting and
attribution and attempt to place a relative credit to all contributing touchpoints.
value on the impact of all touchpoints across
Works well Where you are looking to measure a
the customer journey, although these are campaign holistically and get a feel for the customer
weighted differently depending on the model journey, including seeing which channels contribute
you select. across it
Possible limitations Where you already
If you are operating with a more complex understand the customer journey and have a more
model, longer cycles and a multitude of detailed need to dig into the data on individual channels
to understand which ones are critical so you can focus
channels it’s likely that this is where you find
your effort on them
yourself gravitating to. Let’s take a quick look
at each of the commonly used multi-touch
models in turn.ATTRIBUTION PLAYBOOK 13
U-shaped Time decay
40% 10% 10% 40% 10% 15% 20% 25% 30%
Takes view that some touchpoints have more Also weights each touchpoint differently and
influence on the way to conversion than is driven by the assumption that touchpoints
others and scores them separately. Here closer to sale should get more credit. The
first touch and lead conversion matter most assumption being they have a heavier impact
and are both heavily weighted with 40% of on conversion as the buyer moves down the
the credit being applied to each. While the funnel.
remaining touchpoints are given an equal
Works well With a short sales cycle less than
share of the remaining 20%.
90 days and if you are looking to identify what drives
conversion in the lower funnel
Works well Where you are looking to identify
what drives channel acquisition or have heavy focus on Possible limitations Not so appropriate for longer
nurture B2B customer journeys where it ignores the fact that
customers can make important decisions earlier in the
Possible limitations If you are using Google
process
Analytics but have a buying cycle that extends over 90
days you can potentially lose the data view you needATTRIBUTION PLAYBOOK 14
W-shaped Custom model
This is where you take the view that you need
a very personalised view for your marketing
needs. And the discussion here opens up
into a wider consideration of more complex
30% 5% 30% 5% 30%
attribution models that include Statistical
An extension of the U-shaped model with or Data Driven Attribution, Media Mix/
the addition of weighting for the opportunity Econometrics Attribution and Probabilistic
stage. Here first touch, lead conversion and modelling. Which we will return to in more
opportunity creation share 30% credit each, detail later on.
with 10% being shared out between the other
Works well Where you have a very direct need to
touchpoints.
tie things back to your end goals and need the flexibility
to create tailored weightings to create a highly refined
Works well Where you are looking to identify
and accurate view of things
touchpoints at the 3 critical points that help you build
audience, create leads and then drive to conversion Possible limitations Requires an adequate
allocation of time, thought and resource to make it
Possible limitations If you are using Google
effective. So not to be undertaken quickly or lightly
Analytics but have a buying cycle that extends over 90
days you can potentially lose the data view you need
For now, it’s probably just important to know
that, in practice, this is going to be a blend of
elements from the models above. Customised
in a way that works for your own marketing
scenario.The problem with
current attribution
solutionsATTRIBUTION PLAYBOOK 16
The problem The section above provides a good overview attribution model for Google Analytics, and
of marketing attribution models, each with is a complete oversimplification where 100%
with current their benefits and limitations. However, the
reality is that existing flawed attribution
of the value of the conversion is given to the
last marketing engagement. In essence, this
attribution models are part of the problem marketers
face.
ignores all the previous points in the buyer
journey: almost the same as applying no
solutions For a couple of key reasons:
attribution at all.
Lookback windows are too short: Even
Historical and oversimplified views: At tools like Google 360 have limitations due
the most fundamental level almost all current to their short lookback window and only
attribution models are historical in nature partial attribution in relation to what is very
and all the evidence is that they are woefully often a long and complex customer journey –
inadequate in terms of doing what they where the customer is influenced by a broad
should. For example, Last-click is the default spectrum of media.
“Attribution is broken and
it’s screwing up your entire
approach to digital marketing”ATTRIBUTION PLAYBOOK 17
So, rebuilding attribution – in a way that
reflects the complexity of the customer
journey – has to be a key goal for all
marketers. Unfortunately, “in the wild” the
vast majority of marketers - in fact, according
to research by QueryClick, over 90% of those
responsible for improving marketing ROI in
the UK - are unable to get to sophisticated
attribution models such as these. Or even
simple rules-based models such as First-click,
Position Based or Time-Decay Weighted. In an
effort to provide better outcomes than a basic
Last-click view.
But flawed attribution models are only one
part of the story. It’s also highly likely that
80% of your core analytics data is wrong too.
So, the quality of “data in” to your attribution
model has a huge bearing on the quality of
your analysis. Which is why we are going to
DOWNLOAD
take closer look at this in the next section.Why your data is the foundation for your attribution success
ATTRIBUTION PLAYBOOK 19
Why your Our extensive work with clients around data So, the seeds of success in effective
enrichment consistently tells us that poor marketing attribution are sown firmly in the
data is the models are only one part of the issue with
attribution.
way that you collect the data to feed into your
model.
foundation for Just as important is the quality of the
your attribution underlying data that you are feeding into your Challenges and
success
attribution model – and in purely layman’s
terms if you put rubbish in, you will simply get
opportunities in data
rubbish out. Let’s take the practical example collection
of Google’s approach to collecting the vast
At this stage it is worth considering some of
majority of digital marketing data which is
the challenges and opportunities around data
based mainly on a pixel plus cookie-based
collection across a number of channels and
approach. The problem is that measurement
techniques including:
in this model is rudimentary and based on
tracking a device accessing a specific web • Websites
property. Not around identifying an individual
• Offline media
on the complex, multi-device journey they are
• Mobile and social
on.
• Fingerprinting & 1st Party Enrichment
• CRM, CDP & ERPATTRIBUTION PLAYBOOK 20
Limitations in the cookie/pixel And this is the point at which the world’s
approach most commonly used analytics packages
start to break down and generate incorrect
The vast majority of digital marketing data or incomplete data. The cause? What is
is what is called ‘deterministic data’ and is being measured is a device accessing a web
collected using a pixel working in tandem property. Not the person themselves. Who
with a cookie. With the exception of log-file will use multiple devices in the course of even
analytics, which uses data collected directly simple transactions.
by the server hosting the web property.
Cookies are supposed to enable ‘joining’
HTTP Request
of multiple sessions generated by devices
Third Party
Server
interacting with a web property but, in
reality, they do a poor job, as is shown in the
First Party
Server
following chart:
Assigned ID
Requested Content
Daily attributable data (i.e. Repaired broken
Tracking Cookie sessions & enhanced cross device)
Corvidae
A variety of data including:
• measurement of dwell time
• previous site visited Google 360
• any ad campaign that may have been
encountered by the visitor
• any repeat visit made by that same person K £2m £4m £6m £8m £10m £12m £14m
is all captured in this way.
Non-Attributable Data Attributable DataATTRIBUTION PLAYBOOK 21
This chart shows that using deterministic journeys involve a plethora of media and
analytics data only (i.e. pixel and cookie it’s worth now considering some of the
data) to create a picture of the individual’s challenges – and opportunities – in data
behaviour behind their multiple devices. Even collection across mobile applications, CRM/
where advanced session joining algorithms ERP and offline, to build a clearer view of
TECH FOCUS
are employed - as with Google 360 - attribution across the customer journey.
Random forest modelling generates data that is around 80% incorrect.
Random forest is a regression technique that
uses multiple decision trees and merges their The alternative approach being used by Difficulties in integrating offline
predictions together to get a more accurate and
Corvidae in this example, is a Machine data
stable prediction rather than relying on individual
decision trees. Learning model approach, which takes the
For most businesses, marketing activity
core deterministic data returned into the
tree1 tree2 treen is a blend of offline and online activity.
clickstream, and rebuilds it using random
Offline increasingly offers rich data as
forest probabilistic modelling (see Tech focus)
measurement moves beyond traditional
and regression analysis.
panel-based approaches to incorporate
k1 k2 kn
This approach is not applied by existing Web Beacon technology, opt-in Wi-Fi Tracking
Analytics suppliers such as Google, IBM and and RFID data. These technologies, deployed
In a Marketing Attribution context – and when
Adobe, as compute intensity is high and initial compliantly, enable good data mapping to
combined with the Machine Learning capability
of tools like QueryClick’s Corvidae platform model development has until very recently digital marketing data.
- it offers the possibility of taking deterministic
involved manual ‘tuning’.
data gathered from the clickstream and applies
The largest marketing reach touchpoints
Machine Learning to rebuild it in conjunction
with techniques like device characteristics to This analysis highlights why even core 1st outside of store are linear TV and Radio
significantly improve attribution and identify
party web analytics data for web properties activity. Which typically offer either panel-
individual customers journeys. As well as
providing the predictive data insight needed to fails to provide good data to base marketing led, econometrics data. Or increasingly
target incremental growth by using propensity to decisions on. But more complex customer purchased, direct from manufacturer data -
convert models to drive revenue.ATTRIBUTION PLAYBOOK 22
for example from Smart TVs - for inclusion in Challenges and opportunities in
marketing mix analysis. mobile application & social data
collection
There are challenges with econometrics
data including the inability to look beyond
channel level impact as part of A/B channel
or messaging strategy adjustment, a lack
of immediacy in terms of delivering data
outcomes and the fact that they operate in
a silo to all other data collection strategies.
Which creates an increased issue around
cannibalisation. In addition to being
expensive and less appropriate for smaller
budget media spends.
However, if they are applied to enrich
Data collection in-app or by a social
an already unified marketing strategy,
platform is more tightly associated with an
econometrics can be a valuable tool to unlock
individual who is required to login. Which
customer behaviour, environmental influence,
creates a much more robust measurement
competitive intelligence and meaningful
environment.
signals for targeting.
However, the challenge today with activating
mobile app data is building good quality
data to connect it to. For example - if purely
deterministic data collection is being used
in the wider web, then the individual’s appATTRIBUTION PLAYBOOK 23
based activity which it is being “joined onto” APIs - such as the Facebook one - and ingest
will remain 80% incorrect for the reasons we their data into a wider analytics view.
highlighted in the section above.
In addition, practical issues around how Fingerprinting & 1st Party
social platforms utilise “share” functionality Enrichment of Data
across web properties. Combined with the
One associated data collection technique -
fact that significant volumes of retargeting
which is highly relevant to social profiling - is
TECH FOCUS activity occurs against social platform
fingerprinting. Essentially a way to combine
“Walled Gardens” on Social pixels - essentially short-cutting the need to
certain attributes of a device to uniquely
Media platforms understand the customer journey and simply
identify it.
targeting them based on their demographic
The broader definition of a Walled Garden is a
closed Ecosystem in which all operations are or sociographic profile and proximity to a
There are two common forms of
controlled by the ecosystem operator. conversion event - means individuals are
fingerprinting: device and canvas.
already some way down the conversion path.
In media terms, this is very much the approach
that highly successful social platforms like
Device-led fingerprinting
Facebook, Twitter, YouTube etc. are taking in Social platforms also typically take a ‘walled
marketing terms. By keeping their information
garden’ approach to data availability for
and data analytics firmly closed for privacy but - This approach uses “hit-level” data from a
analysis outside of their own analytics
just as importantly, commercial value reasons. In person’s device to determine its uniqueness,
practice, this means reporting only aggregated (see Tech Focus). Which is geared towards
data with no identifiers to aid the process of
and then match it to subsequent visits. The
enabling native advertising to their users.
identifying individual customer journeys. level of data available is very high and this
is a very accurate way of joining multiple
It is only by using the power of Machine Learning However, these platforms offer a potentially
in Attribution platforms like QueryClick’s Corvidae sessions together when included in the type
rich data view of customers and it is now
that you can begin to use sophisticated datasets of probabilistic models we outlined earlier.
from social – using data on page views, location possible, using sophisticated Machine
details, device details, on-site behaviour etc. - to Learning techniques to ‘unbundle’ data from
derive specific user journeys.ATTRIBUTION PLAYBOOK 24
Canvas fingerprinting All of this means that 3rd party data collection
- essentially data collection from domains
A single device offers a wide range of data that are not the web property domain - will
when preparing to render a web property and become ineffective, unless they switch to 1st
when combined with canvas fingerprinting party data collection processes.
it can become entirely unique. Although it
does have potential data compliance issues 1st Party Data 2nd Party Data 3rd Party Data
to address.
Canvas fingerprinting data can also be
collected via the delivery of ad inventory in
the wider digital ecosystem. However the
bulk of canvas render requests today are for
ad visibility verification purposes. Facebook
also has tracker pixels performing the same
fingerprinting across 30% of the top 1,000 Leveraging the wealth of data in
websites by visits globally. Giving their ad your CRM, CDP & ERP
targeting data deep richness.
Similar to mobile application data collection,
However, Pixels that are set from outside a Customer Relationship Managers (CRM),
web property’s domain are already blocked Customer Data Platforms (CDP), and
by default for Safari users. And so, by default Enterprise Resource Planning (ERP) in some
also on the vast majority of iOS devices. In senses make data collection and achieving
addition, the world’s most popular browser, data accuracy simpler. As customer
Chrome, which is owned by Google, will be identification or sign in is usually a pre-
moving to the same default block position requisite.
through the course of 2021.ATTRIBUTION PLAYBOOK 25
However, these systems’ attempts at ‘joining’ increase the volume or frequency of
or crossing over customer IDs into wider purchases
web analytics, is also affected by the 80%
As well as enabling a deeper understanding
inaccuracy issue highlighted in an earlier
of marketing ROI. So, exploring what is
section.
possible here is well worthwhile.
However, there is a wealth of potential data
available to build into your analysis including: Finally, be careful with data
sampling and vendor selection
CRM & CDP data
Across email, SMS, targeted ads, phone calls Before signing off on this section on the
or direct mail challenges and opportunities of data
collection, it is worth making a small note
ERP data on the impact of sampling and vendor
Across clubcard schemes, Point of Service
selection. There can be issues with “sampling
(POS) transaction data, inventory and stock
Inventory Service in” analytics data which differ from vendor
availability data, returns and fulfilment cost
to vendor. So, vendor selection when
data
Product considering all of your data collection needs
Sales
is a key part of getting attribution working for
That can be complemented by 3rd party data
you.
Financials ERP that can be associated with customer profile
HR data to help drive:
• high quality customer segmentation and
MRP
Purchasing
cohort analysis
CRM
• marketing automation to improve
conversion and engagement and toMarketing attribution: a guide to your choices
ATTRIBUTION PLAYBOOK 27
Marketing Once you have identified and gathered your Without rebuilding the clickstream,
data, how you apply attribution to it is equally however, they suffer from increasing levels
attribution: a important. Marketers today have 4 options
to get attributed data on their marketing
of inaccuracy as we can see in this side by
side comparison using the same dataset as
guide to your activities. in the chart below – where attributable data
identified by Google Analytics 360 was a
choices
• Single/multi-touch or rules-based
fraction of that uncovered by QueryClick’s
attribution
Corvidae platform.
• Statistical or data-driven attribution
• Media mix/econometrics attribution Daily attributable data (i.e. Repaired broken sessions &
enhanced cross device)
• Probabilistic modelling GA360: Time Decay Weighted
Let’s take a quick look at each in turn.
GA360: Last Click Non Direct
Single/multi-touch or rules-based
attribution
Corvidae Attribution
K £5M £10M £15M
We looked at these models earlier on and
they include a range of options including Attributable Unattributable
First-touch, Last-touch, Linear, Time Decay
When used in combination with
and W/U shaped models.
segmentation, channel attribution can still
These approaches are segmenting data from be powerful and enables budget reallocation
core clickstream data into scoring for each across channels for paid marketing activity
channel touchpoint and therefore typically that will affect overall marketing impact.
offer only channel attribution outcomes. However, it offers no insight for individuals,ATTRIBUTION PLAYBOOK 28
preventing the use of this attribution type for available in digital ad tech like Google Ads
automated customer acquisition or insight and is based on using vendor specific tags
on individual impressions. Or the impact of – which have the very real limitation of not
specific pieces of marketing content on that being aware of data collected elsewhere of
individual and their subsequent behaviour. course.
Conversion credit model = Data-driven Path length > 2 touchpoints
Statistical or data-driven Channel
attribution 1 Organic search 100% Direct x2 0%
2 Organic search 50% Email 50%
Increasingly marketers have access to ‘data 3 Direct x2 100%
driven’ models which essentially apply 4 Organic search 100% Direct x2 0%
channel attribution rules that are determined 5 Referral 100% Direct x4 0%
by the data available to them. Instead of 6 Referral 100% Direct x5 0%
rules-based assumptions about channel mix, 7 Paid social 100% Direct 0%
data-driven models determine the model’s 8 Organic social 100% Direct 0%
weighting based on existing data. 9 Paid Social x2 100%
10 Email 100% Direct 0%
Conversion Beginning of path to conversion
Paid Search 31% 12% 8% — 171,650.00
Display 32% 16% 5% 6% 55,505.25
Channel attribution using data-driven approaches allow for
Referral — — 24% 47% 13,824.46 path grouping like this in reports.
Organic Search 24% 24% 8% 29% 2,647.27
Direct — — — 48% 23.02
Social Network — 9% — — 9.50
Data Driven channel attribution in GA and
N/A 0% 10% 20% 30% 40% 50%+ Adobe suffers from the same data quality
challenge as rules-based options described
Even the free version of Google Analytics now
previously – the modelling it relies on can
offers this functionality, and it is increasingly
only work with the supplied data. Which asATTRIBUTION PLAYBOOK 29
we have seen is inaccurately representing the But, again, should not be used to assess
individuals behind the clickstream. And so, revenue outcomes.
the data-driven models are showing only part
Orders Assisted Orders
of the real picture.
Last Touch | Visitor
Marketing Channel
2,009 2,009
Feb 1 Feb 28 Feb 1 Feb 28
1. Email 624 31.1% 480 23.9%
There is still value and insight to be gained 2. Referring Domains 542 27.0% 355 17.7%
3. Social Networks 217 10.8% 141 7.0%
from these models, however. As they can 4. Paid Search 190 9.5% 136 6.8%
give an understanding of the contribution
5. SEO 179 8.9% 103 5.1%
6. Display 174 8.7% 134 6.7%
of channels that are typically poorly served 7. Direct 42 2.1% 42 2.1%
8. None 41 2.0% 41 2.0%
by Last-click - Paid Social in particular - in
your marketing mix. As an overall measure of
volume of interactions. Media Mix Modelling (MMM) /
Econometrics
This means that relying on data-driven
models in web analytics today is not advised This approach applies statistical techniques
for assessing revenue outcomes. Without to attempt to find incrementality in large data
additional processing from the likes of sets using mathematical techniques. Often
QueryClick’s Corvidae platform. relying on integrating panel data for offline
activity.
Other types of statistical attribution are
available in digital marketing, for example In all cases, the object of MMM or
assisted conversions reports like the one Econometrics is to isolate a test case that is
shown here from Adobe. That can be usefully to be measured when it is applied in two or
applied to get a better understanding of the more variations. And the outcome is often
volume impact of impressions on conversion expressed as a measure of ‘incrementality’ to
paths. aspects of the media mix:ATTRIBUTION PLAYBOOK 30
For example, a business may ask what the Profit
Revenue Profit Expense Net
Factor
cost per acquisition is for an offline catalogue.
Catalog + Email $12.00 30% $3.60 $1.01 $2.59
In creating a test to answer that question,
Catalogs Only $10.00 30% $3.00 $1.00 $2.00
different mixes of media channels will be
Email Only $8.50 30% $2.55 $0.01 $2.54
used for cohorts of customers and the overall
No Catalogs
$5.00 30% $1.50 $0.00 $1.50
acquisition outcomes measured. This will No Email
involve mix variations including:
A simple experiment example of test variations and measured
• the use of catalogue only metrics from an A/B or Media Mix test. This example shows a
2% incremental benefit from running offline catalogues with an
• catalogue plus some marketing channel email campaign. Source: kaushik.net
• and the same channels with no catalogue
In this way we can understand that media mix
modelling is not just a case of running A/B
By comparing the results of these variations,
or multivariate testing - it is a science with a
an overall value can be determined for the
great volume of subtleties that can be used to
incremental acquisitions made thanks to
answer a particular question for a particular
the catalogue itself. And that result will be
period of time.
enriched with data about:
• the cost of delivery Due to the complexity and volume of data
required to create balanced tests and models
• operational cost
that are statistically sound, media mix and
• marketing costs and so on. econometrics studies done correctly are an
This gives a good and rich level of detail expensive, ongoing proposition. That should
to the insight gained from the media mix only ever be run in parallel to attribution
modelling. Ultimately offering an actionable activity that is intended to allow tactical
outcome that is statistically true for those optimisation day to day.
tested cohorts for the period of time the test
was run.ATTRIBUTION PLAYBOOK 31
TECH FOCUS
Markov Chain modelling In short: A/B Testing and Econometrics A very broad brush explanation of a typical
Markov Chain is an example of a sophisticated, studies should be used infrequently to inform Shapley approach is to find an average
probabilistic modelling approach that looks at strategy. While attribution should be used to incremental value for a particular channel
individual events in the customer journey and
inform day to day decision-making in service touchpoint by looking at all conversion
identify patterns of user behaviour to build
journey profiles. of optimisation. Which leads us to our last paths that exist, their conversion path rates
option: probabilistic modelling. and their mix of channels. Comparing the
By evaluating the probability to move from one
state to another in the customer journey.
different mixes and conversion values brings
you an average incremental value for each
0.2
Probabilistic modelling channel for each conversion path. Which is
SEO
then aggregated into the reporting you see in
Recent advances in Machine Learning and
the analytics interface.
cheap deployment in SAAS architectures
0.2 0.6
have allowed probabilistic data processing to
0.2 0.1 Another common model is a Markov Chain
be applied to data challenges in marketing
0.7 model. Markov Chains are named after
day-to-day.
0.1 PPC Social 0.6 the mathematician Andrey Markov, and in
∑ C(S), C(S) ≥ 0
0.3
essence, enable modelling a sequence of
v(A) = events in which the probability of each event
For example, if a website visitor is identified as
coming in on Organic Search, the Markov Chain S○A . depends only on the state attained in the
would then understand and look at the other previous event.
channels the visitor could have come in on
and the likelihood they would come back from For example, Google Analytics uses Shapley
Modelling for their data-driven channel And practical application of the Markov Model
Organic. It would also look to see if they would
come from Paid or if they would never come back models. This is based on Game Theory, and can be powerful. Particularly when you are
or if they would transact. approaches marketing as a game, where interested in channel level attribution.
each channel is a player in the game, and
This type of technique can be used to predict
the set of all players/channels can be seen as Let’s look a practical example. In the case
behaviours that lead to conversion, and the data
though working together in order to drive the
can be fed back into the marketing model to below, a Markov Chain based approach
optimise marketing performance. conversions.ATTRIBUTION PLAYBOOK 32
was used in conjunction with the Corvidae For example, using Markov chains for
platform to assess and more accurately marketing conversion paths will find lots of
100% probability value chains due to the very
attribute revenue contribution by channel for
long tail of customer conversions that exist for
a well-known Online Fitness retailer. all businesses - and are limited in their ability
to look at more granular value answers than
As you can see below, compared to Last simply channel value.
Click Non Direct (the rules based model used
by GA 360 and Adobe by default), channel
Most importantly however, both are limited
attribution has changed by £39.9m.
in that they must be re-modelled to provide
Top 6 Channel attribution comparison by channel more granular outcomes than just channel
£50m attribution. Which is computationally very
expensive - and which also causes these
£40m
£30m
£20m models to run into data volume limits which
£10m
prevent their practical functioning. Small
K
Affiliate Search PLA Search Text Direct Email Organic
Paid campaigns would not model with either
First Touch Last Touch Last Touch Non Direct Corvidae
(GA360/Adobe) (Markov App)
approach for example. And understanding
Given total turnover is only £136.7m across the engagement an individual visit has - and
the measured channels, this radically alters that individual’s probability to convert at
the ROI implications of media spend, each individual stage of their own, unique
particularly in Paid Search and PLA (Paid conversion path - is not being modelled.
Search Shopping Ads).
But what if it could be?
Fundamentally both approaches, Shapley
and Markov, are predictive models and have
much in common, however they both have
drawbacks.A new approach to
attribution: visit-level
attributionATTRIBUTION PLAYBOOK 34
A new approach It is now possible to take an approach
that learns from econometric modelling
Stage 1 – Rebuild
to attribution: techniques but is focused on providing near-
live data for tactical marketing insight using
Using previously developed session stitching
Machine Learning processes, data from
visit-level bleeding edge data science. And which
leverages Random Forest Machine learning
almost any marketing channel may be added
to the Corvidae system for inclusion. Because
attribution techniques and session-stitching capability the system first creates a customer journey
plus the capacity to join any offered data from the ground up using raw clickstream
Advances in technology have source to fix the incoming clickstream data data, it is then possible to further ‘stitch’
being generated by analytics platforms. And in marketing content that overlaps with
made this a reality.
join data to any marketing channel online or an individual’s journey. Where supplied
off. data overlaps in time, geolocation or other
fingerprinted patterns match.
This - visit-level attribution which is used by
Corvidae – is the creation of a fully attributed This scores each engagement a visit has -
map of a customer journey. With a scored and the probability of its next engagement
value against every piece of marketing - based on existing data. This completely
collateral that individual has engaged with. rebuilds core marketing data from the
ground up around a true picture of customer
And has 3 clear stages as follows: behaviour.
• Stage 1 – Rebuild Providing the basis for building a single view
• Stage 2 – Unify of each individual’s journey to conversion.
• Stage 3 – Attribute
Let’s take a closer look at each in turn.ATTRIBUTION PLAYBOOK 35
Stage 2 – Unify where geo and time data – from a range of
offline media sources - overlaps with the
This probabilistic approach then enables 500 measured markers held against each
joining of data from offline activity, store customer across a two year lookback window.
activity and even enables the joining of third
party ‘walled garden’ digital data such as data Where predicted customer conversion
from Facebook, Google Ads or YouTube. behaviour is measured to have changed for a
customer, and they have likely been exposed
So, how does this work in practice? to an offline marketing event - a TV ad, or
instore activity - then that activity is ‘stitched’
Let’s take an example – where rebuilt data in into their conversion journey.
a platform like Corvidae can be accurately
mapped very precisely to offline events When attribution modelling is subsequently
applied that offline event is allocated value
Uplift by Station (Most impact at least 50 slots)
just as a digital channel would. Which opens
up the possibility to quantify offline activity
8 Station
film4
dave
7
effectiveness, and allows digital and offline
channel 5
discovery science
itv hd
6
to be held to the same, unified, account for
itv
Mean uplift (absolute)
sky 1
more4
5
performance optimisation.
ch4
ch4+1
4
3
And the impact is significant in terms of the
2
insight it can provide.
1
0 1 2 3 4 5 6 7
Minutes past ad display As a result of joining offline data to online,
QueryClick were able to identify significant
If supplied offline activity data is sufficiently granular, platforms
like Corvidae can be used to apply further performance opportunities for spend reallocation in future
segmentation.ATTRIBUTION PLAYBOOK 36
TV and Radio media buying campaigns for Stage 3 - Attribute
a major life insurance customer. In fact, as
the table below shows, 39% of TV spend was While the customer engagement paths
identified as ineffective and available for generated by the Rebuild and Unify
allocation to other channels for no negative phases are greatly improved data, they still
revenue impact. represent a vast and complex relationship
which must ultimately be scored to give
TV Spend Effectiveness: Reallocation Opportunities
Ineffective Spend
a single attributed revenue outcome for
Total Yearly TV Spend £2,264,000 each engagement point. Considering the
Savings from Time adjustment £430,160
incrementality or otherwise of a piece
Savings from Station adjustment £311,753
Savings from Day adjustment £152,209
of content on one particular prospective
Total £3,158,122 customer journey.
More starkly, no correlation between radio And that is a complex task.
activity and conversion impact enabled
£2.42m of radio media spend to be
For example. Consider the challenge
reallocated with no negative revenue impact. of valuing the incrementality of a single
impression for a single piece of content -
The rebuilding & joining phases above create which may have many millions of impressions
the most complete possible journey for every - on a single prospective customer. Who may
well behave differently both before, and after,
identified individual, including an accurate
that impression than every other prospective
attribution score for each and every content
customer. And will almost certainly be in a
interaction for each and every individual. different conversion phase of their conversion
path - or non-conversion path - from every
other prospective customer again!ATTRIBUTION PLAYBOOK 37
But it is possible, by applying advanced AUROC Graph - Retargeting Model
Machine Learning – and neural network based 100
– techniques which are predictive in nature. 9
80
Like the ones we have outlined to this point, 8
7
and that are used in sophisticated attribution
Engagement Level
Conversions (%)
60 6
software like QueryClick’s Corvidae platform.
5
4
40
3
So, what does that look like in practice?
2
20 1
0
Let’s take a closer look at the example 0
0 20 40 60 80 100
of accurately assessing conversion in % of Visitors - Ordered by Predicted Conversion Probability
retargeting activity for a major life assurer. In Corvidae’s ability to see the whole conversion picture of each
individual allows very high performance retargeting.
this case, Corvidae made available a Machine
Learning based, predictive view of customer
Prospecting for incremental revenue via
prospect conversion for ingestion into the
programmatic display was also highly
client’s Google’s DV360 platform. In order to
successful, delivering a 3.8:1 ROI, and overall
run an A/B comparison against the in-built
Corvidae’s automated customer acquisition
‘data-driven’ ML targeting offered by the
feeds returned 5.8:1 ROI against media spend
client’s existing platform.
in the highly competitive life insurance sector
in the UK.
And the results were outstanding. Corvidae
was able to deliver 4x more conversions at Programmatic Display Corvidae API Performance
½ the CPA of Google’s model. Delivering a Spend Revenue ROI
Retargeting £4,951 £254,770 50.5 : 1
50.5:1 ROI and clearly demonstrating the
Prospecting £110,000 £529,900 3.8 : 1
value in taking a predictive, neural network Total £114,951 £784,670 5.8 : 1
based approach to attribution.The powerful data views you need to accurately drive marketing ROI
ATTRIBUTION PLAYBOOK 39
The powerful By building powerful visit-level attribution Channel Level Attribution
data you are able to unravel the complexity of Removes 3rd Party Cookie
data views
ONGOING ROI
dependency
the wide variety of touchpoints on individual
Optimisation of media spend
customer journeys and accurately attribute by channel.
you need to the correct level of impact to each.
• Spend reallocation (manual)
• Predictive Paid customer
accurately drive
acquisition (automated)
This enables you to make the direct link
marketing ROI
between marketing spend and revenue,
providing the capability to significantly
Campaign Level Attribution
improve your marketing ROI by making
Optimisation to individual
ONGOING ROI
informed judgements based on sound data campaign level.
analytics. • Granular campaign
performance data (manual)
• Bid adjustment (automated)
It also opens up access to a broader, tiered
level of attribution at a Channel, Campaign
Store and Impression level. As shown to the right.
OOH Organic
Impression Level Attribution
Lookalike (Voice)
(RTB/Prog)
TV/R TV/R
Paid Shopping • Optimisation of social
ONGOING ROI
OOH
Store TV/R (Digital) display and video
Paid Social
Organic
Display
(Brand) • Optimisation of individual
Paid Social
Paid Shopping
Organic
(Generic) creatives
Awareness Consideration Purchase Service Loyalty
(See) (Think) (Do) (Care) Expansion
Paid Social Remarketing Organic
Paid Shopping RTB Programmatic
Corvidae provides the ability to work at both
(Generic) (Paid Search) (Brand)
Organic
Store
strategic and tactical levels to improve your
Direct mail
Earned Paid Search
(Programmatic)
(Content) (Brand)
Email
(Referral Offer)
Remarketing
(RTB/Prog)
Email
(Rate & Review)
marketing programmes from the top down
and bottom up.
Work Mobile 3rd Device In PersonClosing thoughts
ATTRIBUTION PLAYBOOK 41
Some closing A range of factors from increasing media
complexity to the need to directly link
on solving the attribution issue for leading
brands including Tesco, Wiggle and Vitality
thoughts marketing to revenue mean marketing
attribution is the key issue for marketers to
Life.
solve right now. But our data shows marketers Reaching beyond Google 360 and Adobe
lack access to effective attribution and are Analytics, Corvidae is the only attribution
facing a myriad of challenges including: solution which uses sophisticated Machine
Learning techniques to completely rebuilds
• seriously flawed existing attribution models your marketing data to eliminate inaccuracy
that are based on oversimplified and that prevents effective attribution and enables
historical approaches that simply aren’t you to:
suited to the length and complexity of the
customer journey • view the complete customer journey across
on and offline media
• an increasingly long and complex customer
journey that is undertaken across multiple • maximize marketing ROI by eliminating
channels and devices both on and offline wasted spend and optimising budget
allocation
• poor underlying data that is largely
predicated on a Cookie/Pixel based • correctly value content and build top of
approach that generates data which is funnel activity
around 80% incorrect • automate customer acquisition and convert
Founded in 2008, QueryClick is an customers based on probability to convert
independent digital marketing agency trusted • enable dynamic data science analysis and
by brands over 32 markets and focused Machine Learning in actionATTRIBUTION PLAYBOOK 42
Attributed Performance Vs Last Click Goal: Conversion
Talk to us about
#All +Segment
Total Change Direct C
£23,835,328 61% $4,901,41
Corvidae Corvidae is QueryClick’s SaaS marketing
$4,000,000
attribution platform and the only attribution $3,900,000
$3,800,000
solution which completely rebuilds your $3,700,000
$3,600,000
Total Revenue
marketing data, reaching beyond Google 360 $3,500,000
$3,400,000
and Adobe Analytics.
$3,300,000
$3,200,000
$3,100,000
$3,000,000
Feb ‘20 Jan ‘20 Dec ‘19 Nov ‘19 Oct ‘19 Sep ‘19 Aug ‘1
Channel Revenue Canniba
GET STARTED £22,356,228 +32%
$12,356,228 / Last Click $8,356,22
“Corvidae reveals up to 334% more
data for attribution than market-leading
competitors. Providing game changing
accuracy and predictive performance.”You can also read
