1) As digital marketing has evolved, attribution models have moved from single-point models like last click, to linear models, and now to non-linear models that better reflect consumer purchase journeys across multiple channels. 2) Case studies demonstrate how attribution modeling reveals touchpoints throughout the purchase funnel, allowing optimization of budgets to increase ROI. Selecting the right attribution model based on industry and regional differences is important. 3) A multi-channel approach to attribution considers touchpoints across email, search, display, and retargeting to better understand their influence on conversions rather than relying solely on last-click attribution.

1. CONVERSION
ATTRIBUTION
Choosing the Right Model for Your Search Marketing Campaigns
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2. Contents
Introduction ............................................................................................................................. 3
Evolution of Attribution Practices in Digital Marketing ..................................................... 4
Common Attribution Models ................................................................................................. 9
Single Point Attribution Models .................................................................................... 9
Linear Attribution Models ............................................................................................. 10
Non-linear and Preference-oriented Models ........................................................... 11
Case Study: Moving off a Single Click Model ................................................................... 12
Case Study: Selecting a Prefer-last Model ........................................................................ 13
Case Study: Considering a Multi-channel Approach ....................................................... 14
Best Practices for Conversion Attribution ......................................................................... 15
Summary ................................................................................................................................. 16
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3. Introduction
Search engine marketing is a practice that’s roughly a decade old and has grown at such a
rapid pace that many concepts that were considered industry hallmarks only a few years ago
have quietly become outdated and obsolete. When the major search engines first began to
offer advertising, they did so with the idea that search engine marketing was a transactional
experience and, thus, a click on an ad would deliver one of two results -- a conversion or no
conversion. Accordingly, the prevailing methodology for marketers became attributing online
conversions to the last click upon which a customer interacts before a purchase.
As the industry evolved, advanced search marketing platforms have given further insight into
how customers behave when interacting with online advertising. Today, we can track activity
across multiple channels in a single reporting system and we can analyze customers based
on sessions or life cycles rather than single interactions. These features have given marketers
visibility into the entire purchase funnel and the marketing activities that impact each phase
within it, rather than giving all credit for conversions to the last ad clicked.
The typical conversion funnel manifests itself in many different ways through marketing, online
marketing, and search engine marketing. The key is being able to understand the interplay and
fluidity between each phase. As seamless as the experience is to the customer, so too must it
be for the marketer who tracks activity and uses data to optimize the experience.
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4. Evolution of Attribution Practices
in Digital Marketing
Disruptive technology has the ability to change the world as we know it. Occasionally, in the pursuit of innovation, developers
move too fast and forget to apply lessons from the past. Search engines appeared on the Internet and changed the way we
discovered information almost overnight. However, in the race to monetize search through keyword-targeted ads, the basic
concept of the conversion funnel was bypassed only to be revived years later in the form of conversion attribution. Let’s examine
the evolution of the digital marketing consumer engagement models.
1. Single Point Credit
The first successful launch of biddable ads in search
marketing was by GoTo.com (later Overture.com). In 1998,
they pioneered the idea of allowing advertisers to bid on
keywords in order to display their ads on search engine
results pages. By the year 2000, Google had launched
a similar service called AdWords, which quickly grew to
dominate the search advertising market. Both services were
focused on the idea of delivering relevant ads and assumed
that each keyword was a transactional single event. Thus,
they credited the conversions and revenues driven from
search ads to the last click -- a single point -- based on
the idea that there was no connection between multiple
searches. See Figure 1 for an example of a conversion with
the last click receiving full credit.
T-Shirt.com
Source: Kenshoo US Conversion Attribution Report | © 2012, Kenshoo
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5. 2. Linear Distribution and Tracking Unification
By 2004, search marketing was no longer a nascent industry. A whole new segment spawned around technology solutions
that allowed advertisers to more effectively reach multiple search engines through a single platform. These new technologies
highlighted a major flaw of the single-point attribution system in that, when a person clicked on ads from two or more search
engines, each engine would claim the conversion value causing instances of conversion duplication. The solution to this
problem was the use of a single tracking system that unified data from all the search engines and online ad programs into
a single location.
The unification of tracking systems allowed search marketers to better understand how people were engaging with ads. The
idea of a “click path” began to emerge as marketers saw the progression of a single consumer from first click to purchase.
The concept of a conversion funnel, a concept common in the offline marketing world, began to manifest itself through
the data. A traditional funnel starts with awareness, making a potential customer aware of an offering, and moving through
consideration and intent before the actual purchase is made. See Figure 2.
FIGURE 2: Traditional Marketing Funnel
AWARENESS
CONSIDERATION
INTENT
PURCHASE
Source: Kenshoo US Conversion Attribution Report | © 2012, Kenshoo
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6. In search, unified tracking helped marketers recognize that different types of keywords functioned in different parts of the
funnel. Generic keywords like “shoes” or “cell phone” indicated the initial phases of product research and a consumer’s
willingness to be made aware of different offerings and brands available. As the consumer moved closer to purchase,
search queries became more focused on specific products and niches. Finally, when the consumer was ready to buy, he/
she would type in the brand name that surfaced as the best option during the earlier phases as a means to navigate directly
to a website and complete the purchase. See Figure 3.
FIGURE 3: Typical Keyword Search Marketing Funnel
GENERIC KEYWORD
PRODUCT KEYWORD
NICHE KEYWORD
BRAND
KEYWORD
Source: Kenshoo US Conversion Attribution Report | © 2012, Kenshoo
At first, search marketing technologies only delivered attribution for paid search ads but, later, as tracking grew more sophisticated,
the funnel could be applied across multiple channels as well. In the multi-channel funnel, search is put in context with other online
channels to capture the most realistic picture of a customer as they engage with ads and brands across several media. See Figure 4.
FIGURE 4: Example Multi-Channel Online Marketing Funnel
DISPLAY AD
SEARCH - PRODUCT
CATEGORY KEYWORD
SOCIAL MEDIA AD
SEARCH - BRAND
NAME
KEYWORD
Source: Kenshoo US Conversion Attribution Report | © 2012, Kenshoo
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7. 3. Preference-oriented Distribution and Non-linear Attribution
By 2010, search marketing technology had evolved to support attribution models that catered to the specifications of
individual verticals and business types. The first introduction of a non-linear model for the wider market was Kenshoo’s
introduction of the U-shaped attribution model. This model uses an 80-20 rule to emphasize the first and last clicks in the
path, while giving middle clicks in long paths-to-conversion less credit. This policy assumes that the search keyword that
kicked off a converting path and the keyword that secured it should be highly valued, while giving less recognition to interim
searches. See Figure 5 for an example conversion path with U-shaped Attribution.
FIGURE 5: Sample U-shaped Attribution
T-Shirts
All Sizes
T-Shirt.com
40%
Red T-Shirt
Great Prices
T-Shirt.com
10%
Blue T-Shirt
Great Prices
Keyword 3 T-Shirt.com
10%
T-Shirt.com Red XL
Buy Now and Save
T-Shirt.com
Keyword 4
40%
T-Shirt.com
Source: Kenshoo US Conversion Attribution Report | © 2012, Kenshoo
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8. Beyond the U-shaped model, companies with the understanding and desire to create their own bespoke policies were
working with leading search platforms such as Kenshoo to create attribution models based on their unique preferences. One
example is a time decay model that gives more weight to click recency when fast sales cycles are inherent. Another example
is a model that proportions out attribution values differently for different online media channels based on the importance
of certain channels to an advertiser. See Figure 6 for a sample time decay attribution model applied to a conversion path.
FIGURE 6: Sample Time-Decay Attribution
T-Shirts
All Sizes
T-Shirt.com First Click
5%
Red T-Shirt
Great Prices
T-Shirt.com +7 Days
20%
Blue T-Shirt
Great Prices
Keyword 3 T-Shirt.com
+8 Days
35%
T-Shirt.com Red XL
Buy Now and Save +8 Days
T-Shirt.com
Keyword 4 1 hour
40%
T-Shirt.com
Source: Kenshoo US Conversion Attribution Report | © 2012, Kenshoo
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9. Common Attribution Models
Following is a breakdown of the most popular attribution models used by search marketers today.
Single Point Attribution Models
A. First Click Only Attribution
This attribution model credits only the first interaction a customer has with an advertiser. If customers interact with multiple
keywords and/or channels, only the original placement will receive credit. See Figure 7.
FIGURE 7: First Click Only Attribution
Source: Kenshoo US Conversion Attribution Report | © 2012, Kenshoo
B. Last Click Only Attribution
This attribution model credits only the last interaction a customer has with an advertiser. If customers interact with multiple
keywords and/or channels, only the last placement will receive credit. See Figure 8.
FIGURE 8: Last Click Only Attribution
Source: Kenshoo US Conversion Attribution Report | © 2012, Kenshoo
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10. Linear Attribution Models
FIGURE 9: Distribute Evenly Attribution
A. Distribute Evenly Attribution
In this model, all interactions are attributed
with some value depending on the value
of the conversion. If there are more clicks
in the path, each individual click will earn
a smaller piece of the pie. See Figure 9.
Source: Kenshoo US Conversion Attribution Report | © 2012, Kenshoo
B. Linear Ratio Attribution
FIGURE 10: Linear Ratio Attribution
In this model, a decay ratio is set based
on the number of clicks. For example,
3 clicks would be distributed by a 16.7–
33.3–50 ratio and 4 clicks would have a
ratio that looks like 10–20–30–40. See
Figure 10.
Source: Kenshoo US Conversion Attribution Report | © 2012, Kenshoo
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11. Non-linear and Preference-oriented Models
A. U-shaped Attribution
This model uses an 80-20 rule to emphasize the first and last clicks in the path, while giving middle clicks in long path-to-
conversion less credit. This policy assumes that the keyword or ad that kicked off a converting path and the keyword/ad that
secured the action should be highly valued, while giving less recognition to interim interactions. Unlike most other models, with
U-shaped attribution, both “starter” and “closer” keywords/ads will get the same credit regardless of the path length. In other
words, if a keyword/ad starts a path, it will not lose attributed value if the path grows over time to 4 clicks, 6 clicks or any other
length until the conversion takes place. See Figure 11.
FIGURE 11: U-shaped Attribution
Source: Kenshoo US Conversion Attribution Report | © 2012, Kenshoo
B. Time Decay Attribution
In this model, the conversion value ratio among the interactions decays over time so that the more recent clicks receive more
credit. Refer back to Figure 6 for a sample conversion path with Time Decay Attribution.
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12. Case Study:
Moving off a Single Click Model
A television cable provider in Europe was using Kenshoo Enterprise to run several SEM campaigns to drive subscriptions. The
client was using last click attribution and was worried that it was misallocating budgets by putting too much emphasis on the
keywords that drove immediate conversions.
Using Kenshoo’s conversion attribution functionality, the client was able identify every keyword that a customer clicked on
prior to signing up. The client selected a “distribute-evenly” attribution model and realized that 43 percent of the keywords that
assisted in driving conversions were not being considered. In this case, a relatively large number of brand and generic keywords
were being under-represented. Armed with this information, the campaign managers moved budgets away from the long tail to
drive more revenue and increase ROI by 13 percent.
FIGURE 12:
43% of Keywords Influenced a Conversion but Received
No Credit in the Single Click Model
6,029
Keywords Leading
to Conversions
2,612
Unattributed
Keywords
473
Non-Brand
Keywords
Source: Kenshoo US Conversion Attribution Report | © 2012, Kenshoo
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13. Case Study:
Selecting a Prefer-last Model
A national service center chain in the United States was trying to understand differences between regions for their SEM campaign
being run through Kenshoo Enterprise. They looked at their conversion paths and found that, even within the same country, customers
behaved differently in various states. Even though the brand was well known and the campaigns were identical in terms of budget,
landing pages, and keywords, they tracked different responses by region. Here is data from two different states.
CALIFORNIA FLORIDA
• 73 percent of conversions • 50 percent of conversions took
occurred within a day more than a day
• 15 percent of conversions • 15 percent took more than a week
took longer than a day
and less than a week
The company looked for reasons why one region would prompt such quick conversions while another took so much longer on
average. They did a deep keyword analysis and learned that in Florida, their customers spent much more time using broad and
general keyword to search for services because of the high levels of competition in that market. To best optimize for each region,
they allocated budgets accordingly and invested more in Florida to grab customers who were using the broad and general keywords
at the beginning of the sales cycle which were more costly per click. They also changed their attribution model to the prefer-last
model which allocated more conversion value to the end of the conversion path. By using the prefer-last model, they put more weight
on the keywords in the longer conversion paths that helped new customers make the final decision to convert.
As a result of the optimization and better understanding of conversion paths, the service center chain was able to dramatically
increase campaign effectiveness over the next three months. In California campaigns, they achieved a 55.8 percent ROI increase
(see Figure 13), while in Florida they recorded an ROI increase of 44.5 percent (see Figure 14).
FIGURE 13: 55.8% ROI Increase for FIGURE 14: 44.5% ROI Increase for
California Campaign Florida Campaign
Week 1 2 3 4
Week 1 2 3 4
Source: Kenshoo US Conversion Attribution Report | © 2012, Kenshoo
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14. Case Study:
Considering a Multi-channel Approach
A leading pet supply cataloger in the United States wanted to analyse its digital marketing campaigns to determine and understand the
influences upon purchases, as well as to assign the most appropriate credit to the different touchpoints through the conversion funnel
rather than just abiding by last click attribution. To do this, the client worked with Kenshoo to consolidate all of the various channel data,
(including e-mail, CSE, display and Display Retargeting) in one location and perform analysis on the results.
After connecting various media channels via Kenshoo’s Universal Platform, the client was able to gain a more holistic approach to
viewing online marketing and was better able to see how these different channels influenced each other. Using Kenshoo’s Path-to-
Conversion and Attribution Simulator Report functionality, the client could identify what channels played an integral role in conversions,
particularly when critical activities were not rewarded by last click attribution. For example, you’ll see that both the last click and first click
models underrepresent the value of retargeting activity toward conversions (Figure 15). In the linear models, especially Divide Equally,
we see that Retargeting is a valued touch-point for the retailer to reach their customers. Retargeting plays a role after the initial click
to keep customers engaged with the brand and promote continual awareness until a purchase is made and often used thereafter to
promote repeat purchase.
FIGURE 15: Conversions by Attribution Model for Display - Retargeting
U Shape
Prefer Last
Prefer First
First Only
Last Only
Divide Equally
# of Conversions
Source: Kenshoo US Conversion Attribution Report | © 2012, Kenshoo
In this multi-channel attribution project, the client also observed:
• Approximately 1/3 of the orders had multiple marketing program touchpoints
• Nearly 15 percent of the orders had a touchpoint in more than one channel (i.e., PPC, CSE, e-mail, etc.)
• The average elapsed time between the first online marketing touchpoint and the conversion was just over 1.5 days
With these new insights, the client was not only able to better optimize across channels, they were also able to justify an
additional 10-20 percent increase in advertising spend for relevant retargeting channels, while still hitting ROAS targets.
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15. Best Practices for Conversion
Attribution
Following are four tips for applying conversion attribution strategies to your search marketing campaigns.
1. Leverage an Attribution Model Simulator
The best way to choose an attribution model is too look at your actual data. Like we saw in the case studies above, an attribution
model should be unique to your business goals and needs. Kenshoo’s Attribution Model Simulator shows you how different
models will affect different campaigns in terms of revenue, conversions, or ROI earned.
2. Eliminate Campaign Waste
Use an attribution model simulator to run your data against a few different models, then find the keywords that have accrued clicks
but no conversions in any of the models. These keywords drain your budget and can be eliminated, but make sure that, before you
delete them, the keywords have had enough time or budget to be statistically significant. For example, you don’t want to delete a
keyword that only has a few clicks as it might just be too early. But a keyword with a few hundred clicks will most likely continue to
behave in the same manner and can be eliminated to save costs. One thing to watch out for is keywords that have high seasonality
like “Easter Basket”. This keyword will have very little activity for almost 11 months a year, so make sure to look at a large enough
data set to allow for seasonal fluctuation.
3. Grow Campaigns by Expanding Assisting Keywords
Looking back at the traditional marketing funnel in Figure 2, we can see that the steps in the middle are just as critical to a
conversion as at the first and last. The same holds true for paid search marketing. Look at the keywords that tally assists but no
conversions. These are keywords that your customers are using to help them in the decision-making process. Expand existing
keywords to find new customers and add a high level of relevancy to your current offering.
4. Think Outside the Paid Search Structure
Use categories (in Kenshoo, these are called dimensions) to organize your keywords by real-world business goals for reporting.
Then you can apply attribution model analysis to understand how your different categories or products can influence others. For
example, if you sell camping goods you might notice a trend that people who search for “rain jackets” also look for other waterproof
gear like “waterproof boots” or “rain-proof tent covers”. Armed with this deeper analysis, you can optimize your campaigns to drive
even more conversions. For example if “rain jackets” is the keyword that triggers most of the waterproof-related sales, then you
could optimize your landing pages and ad texts for a broader message to encompass more rain gear-related sales.
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16. Summary
When it comes to conversion attribution, there is no perfect model. As a marketer, the imperative is to find a model that works
with your business goals and allocates conversion values to meet your priorities. Using your existing data, you can compare
several models at once to gain a deep understanding of your customers’ behavior to better optimize your campaigns.
At the end of the day, conversion attribution will structure your data in the way to best reflects your customer interactions. With
that data, you can draw key insights and make the strategic and tactical decisions for your campaigns.
About Kenshoo
Kenshoo is a digital marketing software company that engineers technology solutions for search marketing, social media and
online advertising. Brands, agencies and marketing providers use Kenshoo Enterprise, Kenshoo Local and Kenshoo Social to
direct more than £16 billion in annual client sales revenue. The Kenshoo Universal Platform delivers automation, intelligence,
integration and scale to make better marketing investments. With campaigns running in more than 190 countries, Kenshoo clients
include Accor, Burberry, GroupM, Havas, John Lewis, Omnicom and Tesco. Kenshoo has 16 international locations and is backed
by Sequoia Capital and Arts Alliance. Please visit www.Kenshoo.com for more information.
Kenshoo is a trademark of Kenshoo Ltd. Other company and brand names may be trademarks of their respective owners.
CONTRIBUTORS
Author Ari Rosenstein, Director, Marketing Research
Aaron Goldman, Chief Marketing Officer
Editors Miriam Carey, Director, Content Marketing
Pavitra Kumar, Sr. Manager, Marcom and PR
Michael Seiders, Creative Manager
Designers
Margo Kahnrose, Manager, Web Design & Development
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