Retargeting is one of the most commonly used marketing tactics as it is able to bring back the 98% of site visitors who leave without converting. Retargeting works by keeping track of people who visit your site and displaying your retargeting ads to them as they visit other sites online. The beauty to this technology is that it is only serving ads to people who have shown at least some amount of engagement in your brand. This makes retargeting a smarter spend than most other display ad campaigns as it focuses on your brand’s engaged user. However, retargeting alone often end up a smaller size of target audience pool, meaning less traffic and sales opportunities. In this real case study, we will demonstrate how the use of the different data together with the targeting techniques can assist the different stages along a business’ customer buying cycle.

1. Going Beyond Retargeting
A 3-Step Targeting Strategy To Identify Customers Along The Buying Life Cycle As
Demonstrated By A Real Case Study

2. Introduction
Retargeting is one of the most commonly used marketing tactics as it is able to bring back the 98% of site visitors who leave without
converting. Retargeting works by keeping track of people who visit your site and displaying your retargeting ads to them as they visit
other sites online. The beauty to this technology is that it is only serving ads to people who have shown at least some amount of
engagement in your brand. This makes retargeting a smarter spend than most other display ad campaigns as it focuses on your brand’s
engaged user. However, retargeting alone often end up a smaller size of target audience pool, meaning less traffic and sales
opportunities.
Thanks to the advancement of technology in recent years, marketers are now having more options to extract greater value from
retargeting by leveraging big data and other targeting techniques. In this paper, we will discuss how various techniques can work
together along the sales funnel to eventually lift campaign ROI through a real case study.
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3. The Basis of Targeting is Data
Online targeting works at its best only when it is supported by
sufficient and relevant data. The data-pyramid divides the
Onsite
Customer Data
commonly available data into 4 layers. The bottom of the
pyramid is the placement data which refers to the information
about a specific ad placement and its ad performance over
time, e.g. size and location of placement, average CTR from
historical data, viewable impression, etc.
Moving up the data-pyramid is the audience segment data.
Search Intent Data
CTR and
Conversion Rate
Audience Segment Data
There are now data providers who are able to collect
comprehensive information about users’ interests and intents
through their online and offline behaviors and activities. These
non-personally identifiable information is clustered into
segments, creating exact audience groups that can help
marketers to reach anywhere online. The use of audience
Placement Data
Area = Volume of Data Available
segment data offers a higher level of targeting precision than merely relying on placement data since it provides insight on the
audience’s behavior and interest.
Up one level is the search intent data. Search retargeting is a prospecting tool which helps to identify people who are likely to convert
because they have already searched for a term that matters to your business. Valuable data concerning the shopping intent behind the
searchers can be extracted for marketers to re-target users with higher shopping intent, i.e. close to the bottom of the conversion funnel.
At the top of the data-pyramid is the onsite customer data which refers to all audience data collected via the activities the users
conducted in the marketer’s sites, such as the pages the user has visited, the information downloaded and items added to the shopping
cart. Onsite users have already checked out the brand and have some familiarity with the company.
Data from each of the 4 layers has their own values to help an effective targeting strategy. For instance, placement data is usually
what every marketer will first look into when planning for a campaign since they are the most readily available in most ad planning
tools and ad exchanges. But marketers will also look for data beyond contextual/placement level that can show audience behavior and
intent for more precise targeting. Therefore, the 4 layers of data complement with each other, and in fact, they should be employed
together to form a comprehensive strategy to strengthen the different stages of the customer buying funnel.
The following is a real case study to demonstrate how the use of the different data together with the targeting techniques can assist
the different stages along a business’ customer buying cycle.
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4. Case Study
Background: Our client, one of the China’s most popular shopping sites, aims to lift ROI and the number of online purchase.
Challenge:
The client has used onsite retargeting and generated a satisfactory ROI. However, the number of conversions from
retargeting is diminishing over time.
Stage 1: Onsite Retargeting
Many marketers share the same experience of seeing the
Site Traffic
number of conversions brought by onsite retargeting as
often being very limited . In fact it could be due to improper
tagging of the web pages.
Usually,
those visitors
marketers select for retargeting are the ones who have
Front Page
visited the page towards the end of the conversion funnel,
for example the shopping cart page. In fact, to get the
most out of onsite retargeting, marketers should establish
Marketers who set up retargeting
tag only towards the end of the
customer funnel will limit the
number of relevant audience to
get exposed to the retargeting ad.
Product Page
a better visitor segmentation based not only on the page
the visitor has visited, but to also consider the behavior of
Shopping
Cart
the visitors.
Instead of retargeting only the visitors who made purchases or those who left the shopping cart without making a purchase, our client
has segmented visitors according to their onsite browsing behavior as follows:
87,835
16,589
visitors who have visited at
least the homepage
visitors
who
have
downloaded coupons
Visitors with brand awareness
= 165,359
unique visitors
Visitors with purchase intent
44,783
43,234
visitors who have visited any
of the product page
visitors who have visited
made a purchase
Visitors interested in products
Repeat Customers
With the new visitor segmentations, the client has significantly expanded the base of audiences for retargeting. In less than 2 weeks,
the effect of retargeting has kicked in:
New Visitors
Old Visitors
Conversions
CPA
Conversions
CPA
Before
324
$71
81
$233
After
465
$63
89
$211
Up 44%
Down 11%
Up 10%
Down 9%
The new approach in visitor segmentation has lifted conversions from old visitors by as much
as 44%
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5. Stage 2: Target Search Audience + Pre-packaged Audience Segment
While retargeting is effective in bringing prior visitors back to the website to increase the chance of repeat purchase, marketers should
also consider broadening the number of visitors in the upper part of the conversion funnel so it could create a continual supply of new
visitors to the website.
In this case, the client has added audience segments who have previously searched keywords related to the products, and also
leveraged pre-packaged audience segments that have shown similar online behavior and browsing patterns to its existing site visitors.
Influx of new site visitors
Stage 2
Increase traffic to create influx of visitors by targeting to
users that show relevant search intent and also leverage
pre-packaged audience segments
Front Page
Stage 1
Onsite Optimization
Product Page
Shopping
Cart
Online Purchase
New Visitors
Old Visitors
Conversions
CPA
$63
89
$211
$48
315
$150
Down 24%
Up 254%
Down 29%
Conversions
CPA
Before
465
After
588
Up 26%
The use of search retargeting and pre-packaged audience segments created a strong
prospecting effect which generates a much higher traffic from new visitors.
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6. Stage 3: Add even more new customers to the top of the buying funnel by Machine Learning
Machine learning is a powerful tool to help marketers identify users who exhibit similar behavioral pattern to your existing online
customers. In the case of this client, XMO’s machine learning takes place by analyzing the patterns of client’s users who have made
purchases online and identified a segment of relevant audience of around 2.3 million online users in 1 week. By reaching the users
identified earlier and also the new users identified via machine learning, the result was:
New Visitors
Old Visitors
Conversions
CPA
Conversions
CPA
Before
588
$48
315
$150
After
624
$42
397
$171
Up 6%
Down 13%
Up 26%
Up 14%
Machine leaning is able to recongnize the behavioral traits of your existing visitors and then
identify new visitors from the vast sea of online inventories who are likely to exhibit the same
pattern, enable you to prospect relevant users in a more targeted approach.
Despite a slight surge in the cost-per-acquisition, it can be seen that machine learning can effectively boost the number of conversions
by bringing more new visitors to the site.
Q
What is Machine Learning?
A
From Wikipedia: Machine learning is a branch of artificial
intelligence concerned with the design and development
of algorithms that take as input empirical data and yield
patterns or predictions thought to be features of the
underlying mechanism that generated the data. A learner
can take advantage of examples (data) to capture
characteristics of interest of their unknown underlying
probability distribution. A major focus of machine learning
research is the design of algorithms that recognize
complex patterns and make intelligent decisions based on
input data.
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7. Key Takeaways
The use of various data can assist marketers to improve
No. of Online Audience
campaign performance in 2 folds. Firstly, it increases the
base of the targeted audience pool allowing marketer to
Onsite data to retarget users who have visited to the site
have a continual supply of new traffic running to the site.
Secondly, it strengthens the different parts along the
customer buying funnel.
Marketers usually adopt
Leverage search data to enhance the reach of relevant users who have shown
interest to the product.
retargeting hoping to bring “shopping cart-abandoners”
and existing customers back. In fact, for the business to
The use of 3rd party pre-packaged audience segments to broaden the reach
further to large base of online audience who have not visited the site before.
grow in a sustainable manner, marketers should also
concern themselves with the split between existing and
Use machine leaming to automatically learn the behavioral traits of your
existing users and identify new users who exhibit the same pattern.
new customers who are generating sales for the business.
The use of data from search intent, prepackaged audience group and machine learning would help to generate more customers at the
top of the funnel. Together with the right creative and messaging strategy in place, it helps to accelerate customers to move along the
conversion funnel and create a huge business opportunity for the business.
The use of data from search intent, pre-packaged
audience segment and machine learning can
help to generate more customers to enter the top
of the funnel.
Awareness
Interest
Decision
Shopping
Cart
Most marketers adopt retargeting to only catch the
customers who have previously reached towards the
end of the buying funnel.
Customers
About iClick
Redefine the digital marketplace and advertising performance with data, insights and
innovations.
iClick is the first online buy-side platform in Asia. With its proprietary cross-marketplace
optimization platform - XMO, iClick helps marketers adapt to the complex advertising
ecosystem by simplifying and automating the online marketing process. This
cutting-edge data technology brings efficiency to campaigns and eventually
maximizes ROI in a sustainable manner.
Visit us at www.i-click.asia or follow our weibo www.weibo.com/iclickasia
Sales contact: sales@i-click.asia
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