Digital marketing ROI - An introduction to attribution modelling

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Digital Marketing ROI:
An introduction to
attribution modelling
@bloomworldwide
www.bloomworldwide.com
hello@bloomworldwide.com

Introduction 5
Chapter 1: What is attribution modelling? 6
Why bother? 7
Return on investment – Not ROI 7
What is it? 7
The different models 8
Chapter 2: Before we begin 9
In a hurry? 10
Illustrating the different models 10
‘Finding Your Attribution Solution’ – visual guide 11
What we will cover 12
What is a conversion? 12
Chapter 3: Last Click – everyone’s first model 13
What is it? 14
Pros 14
Cons 14
When to use it 15
Chapter 4: First Click – Last Click’s mirror image 16
What is it? 17
Pros 17
Cons 17
When to use it 18
Chapter 5: Last Non-Direct Click and similar models 19
Notes on this chapter 20
What is it? 20
Pros 21
Cons 21
When to use it 22
Chapter 6: Linear – equal credit to each touchpoint 23
What is it? 24
Pros 24
Cons 24
When to use it 25
Content

Chapter 7: Positional – location, location, location 26
What is it? 27
Pros 27
Cons 27
When to use it 28
Chapter 8: Time Decay – not as ominous as it sounds 29
What is it? 30
Pros 30
Cons 30
When to use it 31
Chapter 9: Customisation – the better model 32
Time 33
Channel 33
Position 33
Weighting 33
Any combination of the above... and many more 34
Chapter 10: A very social media 35
Demonstrating the value of social media 36
Social media and attribution modelling 36
Conclusion 37
Pictures 37
Where next? 37
Get in touch 38

In the late 1800’s, Sir Arthur Conan Doyle penned the above
line for the world’s pre-eminent detective. 125 years later the
majority of people still don’t adhere to its message.
It is a capital mistake
to theorise before one
has data. Insensibly one
begins to twist facts to
suit theories, instead of
theories to suit facts.
Sherlock Holmes
‘A Scandal in Bohemia’

5
Introduction
Attribution modelling is the method used to measure the monetary impact a piece of communication has on real business goals, for example: sales, customer retention, revenue and profit.
It is a ‘catch-all’ term for changes to reporting structure and communication that allow us to move away from the familiar reporting language of digital marketing (time on site, engagement rate, click through rate, etc.), and translate these metrics into the valuable language of business reporting (volume of sales, net profit, percentage of returning customers).
And critically how to do this efficiently, accurately and exhaustively.
This paper will explore what attribution modelling is, how to implement it, and review common attribution models, which will help define the best starting point for your business.

Chapter 1:
What is attribution
modelling?

Return on investment – Not ROI
This is what attribution modelling does; it attributes a monetary value to every piece of communication, allowing you to
accurately measure the value of your efforts in terms of return on investment, the amount of money you get versus the amount of
money you spend.
Too often, ROI is discussed in non-financial terms. People forget what ROI means – return-on-investment – and talk about, for
example, the extra conversation you generated, or the improvement you delivered in your search page ranking. These could be
seen as digital ROI, but they are not real, financial ROI. Only this measurement allows you to accurately make informed decisions
on budget allocation and which marketing activities will deliver on business goals.
This is the primary objective of attribution modelling; to provide holistic, accurate information about the financial return activities
are delivering so you can refine them, adjust what you’re doing, and use the same budget to deliver more value to your business
and your customers.
Why bother?
There is a good chance that you’re already doing some form of attribution modelling and, when you look at its meaning, you’ll see
why. Attribution modelling means defining a system of analysis that can be used to measure marketing metrics against business
results. This means reporting on the impact of communication activity using metrics like:
• Turnover
• Profit
• Customer retention
• Volume of sales
Instead of metrics like>
• Share of voice
• Web visits
• Click through rate (CTR)
• Impressions
There’s a big difference between these two lists. The second list contains important metrics, but businesses, in theory, could
survive without ever increasing them. However, the business metrics in the first list are essential for all companies that want to
survive and thrive. Without them a business won’t grow, and it won’t last.
Understanding the impact on business metrics is – rightly – more important to senior executives.
What is it?
As mentioned, there is a good chance that you’re already undertaking some system of reporting on business metrics, and
therefore are – at least in its purest form – already modelling attribution.
However, the term ‘attribution modelling’ has come to mean the discipline of doing this in a scalable, representative and holistic
way.
If you work for a business that sells online you will have seen - and maybe produced - reports that showed X% of website sales
were driven by PPC, and therefore PPC delivers £X thousand at a cost of £Y thousand.

The different models
There are a number of technology vendors who can implement tracking, tags, tag containers and similar services that will
accurately gather the data you need. However, this paper focuses on analysis, and what you can do with the information once
you’ve got it. This is what changes dramatically from business to business.
We will look at common attribution models (or sets of rules) and when they should and shouldn’t be used to attribute results to your
marketing activity.
To get the data, you need –
Technology
By implementing the right technology, you can start gathering the
information about where your different digital touchpoints appear
in the customer journeys.
There are several ways to do this and the most common is to
implement tracking tags across all online activity.
Tags allow individual user journeys to be tracked and analysed to
see exactly how users behave when they are exposed to different
marketing activities. This can all be done anonymously so you’re
never violating users’ privacy.
It is worth emphasising that even without the right technology in place, there is a great deal that can be done with the data you
already have. By digging into the data you already have, and looking at how it relates to business performance, you can make great
progress towards developing an attribution model.
However, you know from experience that this is not the full picture. It doesn’t take into account the awareness-generating PR
you conducted, or the affinity you created through your social media activity, or any of the countless touch points a consumer
had with your brand prior to the purchase decision. PPC absolutely had a role to play but it wasn’t responsible for 100% of that
purchase, you just don’t have the data to prove it. The right attribution model will give you this information.
There are 2 steps to putting an attribution model in place>
To understand the data, you need –
Analysis
The attribution model itself is the set of rules that determine the
value of an interaction or touchpoint in a number of different
scenarios.
By applying these rules to different activities, and comparing the
value that each activity delivers against the amount it costs to
deliver, you can identify the return on investment from each activity;
This approach allows you to ‘deep dive’ into the data – for example,
not only can you determine the value of a tweet in general but you
can determine the exact worth you received from a specific tweet.

Chapter 2:
Before we begin

In this example, the customer first encountered the brand through organic search, then through social media, then through an
email and so on. The eighth touchpoint with the brand – a direct visit to the website – was the last and the point at which the
customer made a purchase.
The y-axis details how much of the customer’s spend is attributed to each channel. Here an increasing percentage of spend is
attributed to each channel as the customer moves closer to the point of purchase. This is an example of the ‘Time Decay’ model,
which will be discussed later.
In a hurry?
As with everything, there’s always somewhere you can start. No matter what point you’re on in your attribution journey, there is a
small step you can take to move forward.
Bloom Worldwide has created a visual guide to “Finding Your Attribution Solution”, which provides an overview of the most
popular models. This can be seen on the following page.
For those in a hurry, or who want a reminder, it’s worth keeping this infographic to hand.
Download from:
http://www.bloomworldwide.com/focus-on/attribution-roi/
Time Decay
% Revenue Credited
100%
%0
25%
50%
75%
1st:
Organic
Search
2nd:
Social
Media
4th:
Display
7th:
PPC
5th:
Organic
Search
6th:
Direct
8th:
Direct
3rd:
Email
Touchpoint
Illustrating the different models
To illustrate the different attribution models we will look at a number of examples using graphs like the one below. Each will show
a customer journey from the first touch point to a purchase, and include all the points where the customer encounters the brand.

First Click Positional Last Non-Direct Click Linear Time Decay Last Click
These attribution model recommendations
are not based on data and indicative only.
Bloom would like to reiterate that there is
no one size fits all attribution solution.
Please get in touch to discuss your business
needs further so we can discuss the best
model solution for you.
Custom models may be recommended.
YES
NO YES
YES
YES
CONSIDERED
IMPULSE
START
NO
NO
YES NO
NO
What is it? This model gives all
the credit to the channel of the
first touchpoint your
consumer has with your brand.
Why use it? If you’re only
advertising in one channel,
most of your business can
probably be traced back to
that channel.
What is it? The position in
which the channel appears in
your customers’ user journey
(first touchpoint, second, third
etc.) determines how much
revenue they are credited
with.
Why use it? Users need time
to consider you over multiple
interactions, but you can
prioritise those that begin and
end that process.
What is it? The channel of the
last touchpoint your consumer
had with your brand that
wasn’t a direct website visit
gets all the credit.
Why use it? An advertised
cheap price or other catalyst
could have driven the sale, or
made the user decide to return
later.
What is it? Every touchpoint
that your consumer has with
your brand is considered
equally, receiving equal
“credit”.
Why use it? A long,
research-filled user journey
with multiple touchpoints
working together means each
channel should be weighted
equally.
What is it? Channels are
credited based on the time it
takes for the consumer to be
exposed to your brand again
after the touchpoint.
Why use it? For impulse
purchases, less time for a user
to find your brand again can
demonstrate more impact.
What is it? The channel that
drove the user to the visit
when they purchased gets all
the credit.
Why use it? Your price or
offering is likely the revenue
driver rather than the channel,
so it’s all about how easy it is
for a consumer to get to you.
Are you an impulse buy
or a considered purchase?
Is your comms activity
multi-channel?
Find out more at www.bloomworldwide.com
The insight driven digital agency
specialising in search, social and content.
Follow us on twitter @bloomworldwide
Say hello to us hello@bloomworldwide.com

‘Finding Your Attribution Solution’ - visual guide

What is a conversion?
Throughout this paper we refer to a conversion as a sale,
however, we know that not every online customer journey
ends in a sale. If you are a brand where the purchase happens
either offline, or in a multi or omni-channel environment,
there is still huge value in putting attribution modelling in
place to measure your digital marketing efforts.
The only difference is what you consider as a conversion.
Think about your online customer journey and identify
the points that demonstrably move the customer closer to
purchase. For example, booking a test drive, downloading a
coupon, requesting more information, asking to speak to a
sales representative and so on. Understanding the impact
of different touchpoints on these conversions provides
valuable business information that will help improve your
digital marketing.
What we will cover
While there are many different ‘off the shelf’ attribution
models, this paper concentrates on six of the most common:
• Last Click
• First Click
• Last Non-Direct Click
• Linear
• Positional
• Time Decay
We also look at how you can customise these models to
create bespoke approaches for different brands. Finally,
we’ll touch on what attribution means for social media, and
provide some useful additional pointers.

Chapter 3:
Last Click – everyone’s
first model

What is it?
Last Click is both the most commonly used model and one of the most inaccurate. The Last Click model assigns 100% of revenue
generated to the last customer touchpoint before a purchase, as in the example below.
Last Click
% Revenue Credited
1st:
Organic
Search
2nd:
Social
Media
7th:
PPC
5th:
Organic
Search
6th:
Direct
8th:
Direct
3rd:
Email
4th:
Display
Touchpoint
Sound familiar? It probably is. This is the model that
most people use as standard when they start reporting
(often unintentionally), because it’s one where all the
data is instantly available in the format needed. It is
useful because it is simple to set up.
For example, if you’re an eCommerce site using Google
Analytics, all that’s required is to set up a purchase
Event or Goal at the ‘Thank you for your purchase’
page, and then look at the channel acquisition of all
the visitors who got through to those conversions. The
number of users who came from those channels and
went through to purchase is exactly the number of
consumers that channel drove according to Last Click
attribution.
It’s easy to track offline sales too. If you’ve got any sort
of dynamic voucher code generation or call tracking
software, you can generate a code or phone number for
consumers to get touch with you.
However, the model is not without its considerable
drawbacks.
Let’s go back to the explanation of the Last Click
model: 100% of revenue generated is attributed to
the last customer touch point before a purchase.
When you think about this, it is both counterintuitive
and inaccurate. It doesn’t take into account the brand
advertising you conducted to drive awareness, any of
the social media activity you undertook to generate
affinity, or any other activity prior to the point of
sale. That is why the Last Click model often paints an
inaccurate picture; there are a host of other factors
that inform a purchase decision ignored in favour of
simplicity.
Simplicity, in this case, does not equate to accuracy.
check Pros close Cons
100%
%0
25%
50%
75%

When to use it
Last Click is simple to execute but often does not provide an accurate picture... so when can it be used?
One scenario could be if you’re conducting a flash sale on a relatively unheard-of product with a large media spend. In this case
Last Click will probably give you some useful, immediately actionable insights. Or if every single piece of marketing you undertake
is aimed directly at driving a sale, Last Click will again give quick and useful (if not wholly accurate) insights.
However, for most attribution challenges Last Click is too ‘all or nothing’ to deliver valuable business information. In these
situations you need to move on to a slightly more sophisticated model.
By tracking which consumers use which numbers or
codes through your CRM, you can get a quick idea of
which channel drove each visit.

Chapter 4:
First Click – Last Click’s
mirror image

What is it?
In layman’s terms, First Click attribution is the polar opposite of Last Click, it attributes 100% of revenue to the first consumer
touchpoint.
For example, if a customer first comes across your brand by clicking on an organic search listing, and then later – from a different
visit – spends £100 on your website, organic search is said to have driven £100 of revenue. As demonstrated in the graph below,
this model disregards any other touchpoints the customer experiences in the run-up to purchase.
Much like Last Click, First Click derives the majority
of its strength from simplicity. It works on the basic
premise that if a user has never heard of you, they can
never buy from you. So however they first heard of you
is the ultimate cause of their spend.
The biggest con of First Click is similar to the biggest
con of Last Click: it deems one interaction – this time
at the beginning rather than the end – to be wholly
responsible for the spend. We know that the customer
journey is more complex than this, and users often
interact with brands many times before purchasing. So
in this respect, First Click’s picture is too inaccurate to
be of business use.
First Click
% Revenue Credited
1st:
Organic
Search
2nd:
Social
Media
7th:
PPC
5th:
Organic
Search
6th:
Direct
8th:
Direct
3rd:
Email
4th:
Display
Touchpoint
100%
%0
25%
50%
75%

When to use it
First Click comes into play when you have a single channel strategy, i.e. you’re only using one marketing channel to promote your
brand.
In this instance, First Click would be a better place to start than Last Click; imagine launching a brand on Twitter, and a consumer
visiting you directly from that network. A week later, they remember you and return directly to your website to buy a product. In
this case, the First Click model will attribute the sale to Twitter – a fair assumption given that is where the consumer first came
across you. Last Click would attribute this sale to a direct visit, which wouldn’t really be fair, as the direct visit didn’t really do
anything to cause the sale; it was just the medium through which the sale was achieved. The First Click is therefore ‘a more worthy’
method to attribute the revenue.

Chapter 5:
Last Non-Direct Click
and similar models

Notes on this chapter
This chapter focuses on the Last Non-Direct Click model – and at the same time we will allude to other models that are similar in
principle but different in activation.
What is it?
You will recognise that this model is similar to Last Click, except for cases when the Last Click is a direct visit. In such cases, this
model finds the latest click that isn’t a direct visit and attributes 100% of the revenue to that channel instead. For example:
If the 7th touchpoint was also a direct visit, the model would look at the 6th, and then the 5th etc. until it ‘found’ a touchpoint that
wasn’t a direct visit, which would then receive all the value.
The rationale behind this model is the idea that once a visitor comes directly to your website they have already made the decision
to buy from you, so the cause of that purchase is not the direct visit itself, but the one that pre-empted that direct visit.
There are other, not dissimilar models that work in the same manner, i.e. they suggest that if a certain channel appears at a certain
point in the customer journey, they are either entirely responsible for the conversion, or in no way responsible for the conversion.
Google Analytics has a model called Last AdWords Click (for PPC), which takes whatever the most recent interaction a user had
with AdWords as wholly responsible for the sale. It doesn’t matter if they’d interacted with AdWords prior to the most recent
click, or if they interacted with some, dozens or hundreds of other channels after the most recent AdWords click – the most
recent AdWords click always receives 100% of the credit.
If we slightly adapt our customer journey example to demonstrate this, it would look like this:
Last
Non-Direct
Click
% Revenue Credited
1st:
Organic
Search
2nd:
Social
Media
7th:
PPC
5th:
Organic
Search
6th:
Direct
8th:
Direct
3rd:
Email
4th:
Display
Touchpoint
100%
%0
25%
50%
75%

Last
AdWords
Click
% Revenue Credited
1st:
Organic
Search
2nd:
Social
Media
7th:
PPC
(Adwords)
6th:
Direct
9th:
Email
8th:
Social
Media
10th:
Direct
5th:
Organic
Search
3rd:
Email
4th:
PCC
(AdWords)
Touchpoint
Again, there are many models that can be set up in a similar way in order to give all (or no) credit to certain channels at certain
points of the user journey. But they all stem from similar principles, accountability.
By accountability, we mean the ability to judge each
channel according to what it should be doing. If you
know that PPC is the one channel you are using to drive
sales then you can use Last AdWords Click to measure
this. If you know that there is nothing on your website
that is going to increase a user’s inclination to buy, then
Last Non-Direct Click reflects this.
If, for example, you implemented a social media
campaign that encouraged users to buy within a
specific date range, then you could set up a model that
gave 100% credit of any sales in that date range to
any social media touchpoints that occurred during the
campaign. The ability to customise this model to exactly
what activity and goals your business is conducting is its
greatest strength...
...and its greatest weakness. With accountability comes
inherent bias; if you only deem a channel responsible
under certain criteria then you are neglecting the
possibility of it impacting another stage of the
customer journey, or any halo effect it may have. You
are also discounting the impact of other touchpoints in
the customer journey.
In both examples above, and any other model of this
form (all or nothing under certain criteria) there is an
unavoidable bias. In Last Non-Direct Click there is
a natural bias against direct visits, as they can never
receive any credit for the sale, and the last non- direct
touchpoint will always receive all the credit.
Similarly in the Last AdWords Click model there is a
bias towards AdWords. As soon as a user clicks on
an AdWords listing, no other channel has a chance of
receiving the credit.
100%
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25%
50%
75%

When to use it
To state the obvious, the best time to use the Last Non-Direct Click or similar models is simply when the pros outweigh the cons,
and when other options aren’t suitable.
The level of control you are permitted in setting up this type of model, to measure exactly what you want to measure, is of
real benefit. The time to use this model is, therefore, when this benefit outweighs the inherent bias that comes with weighting
everything on an all or nothing model.

Chapter 6:
Linear – equal credit to
each touchpoint

What is it?
The Linear model is based on a different principle to the First and Last variants. Where those models deem that one part of the
customer journey is solely responsible for the sale, the Linear model states that every step of the customer journey is equally
responsible. It is the democratic attribution model; every touchpoint gets credit for an equal portion of the revenue a customer
spends.
Therefore, in a customer journey where the consumer had five interactions with the brand, each interaction will be credited with
20% of the revenue from that customer. In a journey with eight interactions, as below, each will be credited with 12.5%.
Linear
% Revenue Credited
1st:
Organic
Search
2nd:
Social
Media
7th:
PPC
5th:
Organic
Search
3rd:
Email
4th:
Display
6th:
Direct
8th:
Direct
Touchpoint
This highlights another important difference between Linear and First and Last Click models. The Linear model, because it
attributes value to different stages of the customer journey, presents the possibility that one channel can be ‘attributed’ twice.
If you look at the graph above, Organic Search receives 12.5% of the revenue when it appears in both the 1st and 5th touch point.
Likewise, Direct receives credit at both the 6th and 8th position.
Therefore, while each touchpoint receives the same amount of credit for the conversion, different channels can receive very
different amounts of credit in total. In this case, Social Media, Email, Display and PPC each get 12.5% of the credit, while Organic
Search and Direct each get 25%.
Linear recognises that every step of the journey is
important in a customer’s conversion. Linear attribution
Linear attribution does not recognise what a channel,
campaign or activity is trying to achieve. For example,
100%
%0
25%
50%
75%

means that more than one channel can receive credit
from every purchase and so gives a clearer picture as
to which channels are working best and which are not
working at all.
Linear is also useful because it is a more sophisticated
model but one that is still relatively simple to
understand.
if a campaign receives an extra push of budget, which
could potentially diminish the overall ROI, this isn’t
reflected in the model. Similarly, if a campaign is
intended to drive sales (i.e. the impact should be felt at
the end of the customer journey) but instead generates
awareness (the impact appears at the beginning), Linear
would not recognise this crucial difference and
therefore not demonstrate that the campaign had not
achieved its objective.
When to use it
Due to its drawbacks, Linear is perfect when no ‘hero’ or push campaigns in progress. When you are delivering a standard
marketing plan so you can compare how each channel performs on an ‘equal’ playing field. It is a good model to use in many cases
if you are looking to progress away from Last Click, but perhaps not yet ready for a custom model. Because of its simplicity and
fairness, it’s great as a first venture into more sophisticated attribution modelling.

Chapter 7:
Positional – location,
location, location

What is it?
The Positional model recognises two important factors in a strategy that the other models we have looked at so far do not:
1. The shape of a customer journey
2. The different values of the stages within it
As we know, every part of a customer’s journey to purchase is important. While the familiar path of Awareness Consideration
Conversion has become more sophisticated in recent years, the fact that there is a journey, which starts with a potential customer
finding out about a brand, is undeniable.
The Positional model acknowledges and represents this by combining aspects of First Click, Last Click and Linear. Essentially
it says that the first touchpoint and the last touchpoint are worth X% each, and all the other touchpoints in between have the
remaining % divided up evenly among them.
A common set-up of the Positional model is to have the first and the last touchpoint worth 40% each, and the remaining 20%
divided equally amongst the touchpoints in between. For example:
Positional
% Revenue Credited
1st:
Organic
Search
2nd:
Social
Media
7th:
PPC
5th:
Organic
Search
3rd:
Email
4th:
Display
6th:
Direct
8th:
Direct
Touchpoint
We have already touched on the benefits of the
Positional model, which recognises that different
stages in a journey should receive credit for different %
of revenue generated. The model gives prominence to
the first and the last stages, those when the customer is
The first issue is the weighting of the percentages. 40%
to the first and last stage of the journey may seem like
a large portion, especially when you consider how little
it leaves for the other stages (particularly when you look
at journeys with many more touchpoints). However, this is
100%
%0
25%
50%
75%

When to use it
So when do you use this model? A good example is when conducting large scale, global brand campaigns and smaller, local-product
focused campaigns. The intention of these working in tandem is often to generate brand awareness from the large-scale brand
campaign, and product consideration and conversation from the local campaigns. In these situations, Positional attribution is a
great way to measure whether what you think you’re achieving, or what you’re trying to achieve, is what you’re actually getting
from your combined global and national efforts.
This makes it clear that the Organic search touchpoint wasn’t as valuable as we thought. While this was indeed when the
customer first encountered our brand, it was sometime before they came back to us again on social media, and then a long time
before they decided to purchase. In this example it starts to look – at least initially – more like the real catalyst was PPC.
first exposed to the brand and when they converted.
It balances this by recognising that every step in the
customer journey has a role to play, and therefore every
touchpoint receives some credit for the conversion.
So, for the balance of weighting the (potentially) more
important first and last steps of the conversion path
while acknowledging that every step has a part to play,
the Positional model is worthy of consideration.
Positional
% Revenue Credited
1st: 2nd: 3rd: 4th: 5th: 6th: 7th: 8th:
Touchpoint
Organic Search
Social Media
Email
Display
Organic Search
Direct
PPC
Direct
a minor concern as the percentages can be customised,
which we’ll look at in more depth later.
A bigger concern when considering a Positional
attribution model is that, while it takes the shape of
the user journey into account, it does not consider
timescale, at least not in its core design. If you
restructure our example to take into account not just
the different touchpoints on the x-axis, but also the
time between them, then a truer picture would look
something like this (see graph below):
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Chapter 8:
Time Decay – not as
ominous as it sounds

What is it?
Let’s clarify one thing: Time Decay is not the best model. Whilst Time Decay is one of the more sophisticated, in both
implementation and understanding, this alone does not make it the best or the one that everyone should use.
In the Time Decay model the principle is that the closer a touchpoint is to the conversion, the more influence that touchpoint had
on the customer decision.
It is important to stress that ‘closeness’ is based on time, not position in the user journey. This is particularly important when
looking at the example below because the shape of the graph could be very different depending on the time frame over which it
occurs.
To give context to the example, let us say that 24 hours passed between the first touchpoint and the second, 12 hours between
the second and third, and 6 hours between the third and fourth. Then the time keeps halving until only 22 minutes and 30 seconds
passes between the seventh and eighth touch point, which leads to the purchase.
Time Decay
% Revenue Credited
1st:
Organic
Search
2nd:
Social
Media
7th:
PPC
8th:
Direct
6th:
Direct
5th:
Organic
Search
3rd:
Email
4th:
Display
Touchpoint
The Time Decay model attempts to represent the
intent of any piece of comms activity to increase a
customer’s willingness to buy and accelerate their
decision to do so. It is a good way to understand which
activities are causing the biggest step-change in your
marketing activity; it gives credit to the touchpoints
Because Time Decay focuses on the timeline, rather
than the shape of a customer journey it can, if not
planned correctly, over-favour the touchpoints most
recent to the purchase. For example, research is an
important stage in many customer journeys. This
research could happen at one time, when the customer
100%
%0
25%
50%
75%

When to use it
When you have an holistic, multi-tiered and complimentary communications strategy with each aspect having an intended call
to action that leads to the next step in the journey, Time Decay can give you an accurate picture of whether your strategy is
delivering as intended.
With the right set-up, it can also be a good model for those selling products or services that are long-term considerations as
opposed to impulse buys. The model can show you which of the many different touchpoints are expediting the journey, which
are causing a slow down, and which are causing users to drop off. Here Time Decay can provide a valuable picture of long-term
purchase cycles.
that generate the gradual increases in momentum that
many purchase decisions go through over time and
focuses on touchpoints that are drawing that decision
to a conclusion and converting the sale.
has multiple contacts with the brand. The outcome of
the research could be a decision to purchase, but the
purchase itself could happen several days later. The
time frame between the purchase and the research is
not relevant to the customer. It is an arbitrary period
between deciding to buy and acting on that decision.
Time Decay ‘penalises’ all the important touchpoints
during the research phase simply because of that time
period. In this instance though, the touchpoints most
recent to the purchase are less important that those
during the customer’s research. However, because of
the way Time Decay weights channels, the picture it
presents to senior stakeholders or those not familiar
with the data could be innacurate.

Chapter 9:
Customisation – the
better model

The right model for you
The most important factor when looking at attribution modelling is recognising that off-the-shelf models are not the whole
picture. They are a starting point on the path toward identifying the best attribution model for your brand or organisation.
One of the steps on this path is understanding what can be customised, given the right technology and the right set-up. Below we
provide an overview of the different elements within attribution modelling that can be customised and combined to create the
right model for you.
Time
As touched on in Chapter 8: Time Decay, customising the way a model behaves depending on time can be very important. For
example you could set a rule that, if a customer doesn’t interact with your brand for a week, then that journey is considered over.
If the same customer returns, a new journey is started. Similarly if a customer is exposed to two complimentary touchpoints in
a set space of time, you may want to up-weight the value of one or both because they are shown to be acting in tandem. Setting
different rules based on different time periods is one way to customise an attribution model.
Channel
As described in Chapter 5: Last Non-Direct Click, different channels have different behaviours and different intentions. You
may therefore want to weight them according to the amount of channel spend, the perceived value of the action it is intended to
motivate, or the amount it appears in different customer journeys.
For example, if you want to quickly look at ROI, and are spending a fraction of the amount on social media than you are on PPC,
then you may chose to down-weight PPC interactions and up-weight the value of social media interactions.
Position
In Chapter 5: Last Non-Direct Click, we looked at the importance of different channels appearing in different positions on the
customer journey. Does a direct visit to your website that appears in the first stages of the journey have the same value as a direct
visit that occurs in the final stages? You can customise your model to take into account such questions.
Similarly, in Chapter 7: Positional, we examined how important it is to know which positions are the most important to your
customers’ journeys. It is essential to customise positions so they best represent what is right for your brand.
Weighting
We have looked at models that apply different weightings under different criteria: for example Positional depending on different
positions, or Time Decay based on different timelines.
It is possible to customise the different percentage weightings within these models. Should the first engagement be worth slightly

Any combination of the above... and many more
The end customer action, the campaign you are running, the time of year, which web pages are visited... all of these and more can
be used to tailor an attribution model to your needs and ensure it is as useful as it can be for your business.
All the models we have looked at (and more) are interrelated, and each has its nuances. It is important to have a team who
understand your business, your data, your brand, your needs and your available technology in order to devise the best, pragmatic
solution.
The optimum solution is almost always a combination of everything we have covered. The best attribution projects will consider:
1. The right solution to strive for
2. The best way to get there to continually deliver more ROI on the project as a whole
less in Positional? Should you adjust the credit Time Decay gives to each touchpoint as time from purchase increases? Customising
the weighting you give to each touchpoint in each circumstance is a further opportunity to create an attribution model that makes
sense for your brand.

Chapter 10:
A very social media

Demonstrating the value of social media
Before concluding, it is worth taking a look at social media, a prime candidate for a case study on the importance of attribution
modelling.
Many Social Media Managers struggle to demonstrate the business impact of their work. They know it’s there, indicated by
improving engagement rates, increased audience sizes, advocacy and more. However, these are still merely indications of a deeper
business success that hides beneath the surface and is difficult to prove.
Social Media Managers know this and understand that social media is primarily an awareness, affinity and retention-increasing
platform, not a channel that focuses on direct selling. But when budget decisions come to be made they find it difficult to justify
why they should have budget because PPC Managers are reaping part of the reward from the awareness and affinity they
generated, which undoubtedly increased that AdWords click through rate (CTR).
Social media and attribution modelling
Attribution modelling levels this playing field and proves the business impact that social media has on the bottom line. It allows
Social Media Managers to take credit for the work they are doing by assigning a fiscal value to every action, campaign and
network they manage, and therefore to demonstrate real ROI.
It is tough to justify a campaign that will generate 100,000 Facebook fans if not a single fan from the first 10,000 clicks through
to make a purchase. It is easier to do this when you can prove that each of those fans added £1 to the bottom line; it just appeared
further up the conversion funnel than had previously been identified. All of a sudden that Facebook campaign seems to make a lot
more business sense.

Conclusion
Pictures
Reading, understanding and implementing the suggestions in this white paper is a first step. The most useful attribution models,
those that hit the sweet spot of being the most accurate and well used, are bespoke implementations that reflect detailed
knowledge of a brand and its communication strategy.
We hope we have demonstrated the possibilities that implementing an attribution model offers, and why it’s important to ensure
that the model presents an accurate picture of customer behaviour.
The question always arises as to whether these pictures will be perfect, and the honest answer to this question is no. No picture of
communication attribution is perfect, and anyone who claims otherwise is peddling snake oil. There is always the possibility that
customers will swap devices, swap browsers, deny tracking cookies and so on. It is difficult to circumvent one, let alone all of these
and as a result attribution will always present an imperfect picture. However, this picture is still a significantly more accurate and
useful one than traditional reporting allows for.
We have only scratched the surface, there are valuable methods that include offline communication and customer engagements
in your attribution model to give an even fuller picture. This can include TV, print, DM, telesales and more. So the future is bright
for those who invest in attribution modelling now, and use it to optimise their marketing spend in future.
Where next?
Some may focus on the bad news after reading through this paper, to recognise how far away we are from the perfect attribution
model. This would be to deliver false worry, no optimum model is implemented overnight: it is a process of small steps towards a
greater goal. With each step resulting in small learnings that can deliver big wins.
There is always a first step you can take with the information you have available. Just having a data strategist look at the data your
digital marketing activities generate, in light of the business return you have actually delivered, will reap wonders in terms of costs
that can be saved, profits increased, and budgets efficiently allocated.
It is possible to get to the truth about how your digital marketing activity is impacting your bottom line, and deliver valuable and
appreciated reporting as a result. Don’t report your data to suit your strategy, design your strategy in response to data.

Get in touch
To find out more about how Bloom can help define and implement the right attribution model for your business speak to:
John Murphy
Head of Multichannel
+ 44 (0) 1273 732 626
john@bloomworldwide.com
@JohnMur3
About the author
John is our Head of Planning here at Bloom. He lives and breathes digital – from social to search, PoS to Python and everything in
between, John has worked with all types of digital communication and is always up to date with the latest industry changes.
A numbers geek at heart, John also prides himself on making sure everything he and Bloom does is grounded in numbers and leads
to numbers; ensuring work is based on provable insights to maximise success and leads to measurable results to demonstrate
actual business impact.
By combining digital knowledge with data-driven intelligence, John has lead and worked on projects that have won awards
including DMAs, Marketing Week Engage Awards, BiMAs and more.
John’s shamelessly paraphrased motto is ‘head in the clouds, feet on the ground’ – he believes that the best ideas are those which
are over-ambitious but not unachievable, and that all work is only as good as the action or change it drives.

@bloomworldwide
www.bloomworldwide.com
hello@bloomworldwide.com
Thanks for reading

Digital marketing ROI - An introduction to attribution modelling

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