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Blu Homes: Online Advertisement Campaign Report
Yunyun Song
Arie Rapaport
Diego Munoz
MS&E 239: Introduction to Computational Advertising
Yunyun Song, Arie Rapaport, Diego Munoz
General Ad Campaign Strategy
The following steps were performed to develop and analyze the results for Blu Homes‟ advertisement
campaign:
1. Research BluHomes‟ products, potential customers and the trends associated to house buying in
recent years.
2. Evaluate the potential market segments, their sizes and relative importance for BluHomes‟
business. For each segment, define alternatives for key words, creatives and messages.
3. Define which advertisement options and channels will be used.
4. Measure metrics with which to compare the results in each market/channel/key word/message
combination.
5. Reiterate upon previous steps to adapt the segmentation, channels, key words and messages
according to the on-going results.
6. Develop recommendations on which segments our client should target, along with specific
strategic guidelines supported by our campaign‟s final results and analysis.
We will discuss each of these points in detail, along with the results obtained and the analysis performed,
in the following subsections.
Products, Potential Customers and Market Research
To assist us in formulating a general strategy and objectives for our advertisement campaign, we did
some preliminary research regarding the real estate market in California and the Bay Area. All the data
presented in this section was obtained from the National Association of Realtors, corresponding to two
series of reports: 2008 Profile of Home Buyers and Sellers and the Study of Housing Affordability in
San Francisco (both available in association‟s official website).
Relevant results to the formulation of our strategy include the following key points:
Forty-one percent of recent home buyers were first-time buyers.
The typical first-time home buyers was 30 years old, while the typical repeat buyer was 47 years
old.
Twenty percent of recent home buyers were single females, and 10 percent were single males.
This implies that thirty percent of recent buyers are single.
For one-third of home buyers, the first step in the home-buying process was looking online for
home properties.
MS&E 239: Introduction to Computational Advertising
Yunyun Song, Arie Rapaport, Diego Munoz
Eighty-seven percent of all home buyers and 94 percent of buyers age 25 to 44 years used the
Internet to search for housing alternatives.
The typical home buyer searched for 10 weeks before making a purchase.
Forty-six percent of buyers who used real estate agents found theirs through a referral from a
friend or family member.
Given this information, we can be certain that online advertising for Blu Homes will most likely be very
relevant, since most future home buyers spend considerable time searching for home features and
alternatives online. Since our client does not rely on the use of real estate services, social network
referrals might not be an effective approach to advertise Blu Homes‟ products.
Market Segmentation
Our ad campaign will mainly be focused on targeting middle-aged individuals who are interested in
buying a first or second home. For the specific case of second homes, we will launch some ads that
incorporate the idea of modern vacation or recreation homes. Furthermore, we will segment this market
according to the following groups: people seeking “economical homes”, people seeking “prefabricated
homes”, people seeking “green homes” and people seeking “eco-friendly homes”. Ads will be tested
independently for each of these 4 groups.
According to our market research, a considerable percentage of home buyers are described as “non-
white”, which implies that many potential clients might browse the Internet in other languages
additional to English. For this reason, we destined a small amount of our budget ($5-10 dollars) to
running an exact copy of some instances of our ad campaign in both Chinese and Spanish (which,
incidentally are two of the most commonly spoken languages in California).
Advertisement Options and Channels
Our advertisement campaign will focus exclusively on using sponsored search on two different search
engine providers: Google and Yahoo/Bing. Although content match, social network advertising and
display ads were also possibilities considered, we did not pursue them for a number of reasons.
In the case of content match, it is well known that click-through rates are rather low and the ad may be
lost in the content of the page. Alternatively, in the case of display ads, the costs of these seemed rather
high and likely to be misused given there are very few suitable channels to attract potential customers
(contrary to, for example, male products which are rather successfully advertised in sports webpages).
MS&E 239: Introduction to Computational Advertising
Yunyun Song, Arie Rapaport, Diego Munoz
On the other hand, we did not pursue social network advertising due to the nature of the products Blu
Homes sales. Although social network advertising might be greatly successful with frequent
consumption products (such as clothing, movies and handbags), the nature of the decision associated to
buying a house is quite different, frequently made after considerable thought and research of both
features and alternatives
Instead of diversifying our advertising channel options, our campaign will try to figure out which
combination of keywords, creative and messages attractive a higher rate of potential customers
(measured as a proxy of click through rates) while maintaining reasonable costs.
Performance Measurements
We primarily look at “clicks”, “average CPC” and “CTR” as the key measurements for ads performance.
We consider the ads with high clicks and low average CPC is worth investing for the company since it
does not cost much but still drive attention and traffic. We believe the best performing ads are the ones
with both high clicks and CTR, and reasonable average CPC, these ads are valuable in providing
insights on user behavior and search habits on future advertisement.
Google Adwords Campaign Summary
The lion‟s share of budget on advertisement for the client goes to the Google Adwords, given its
popularity and greater market share among the available search engines. The campaign started on Oct.
24 and ended on Nov. 17. Table 1 summarizes the results for the Google campaign, while Table 2
presents a more stratified vision of these statistics by ad group segment.
Table 1: Google Adwords Campaign Overall Results
MS&E 239: Introduction to Computational Advertising
Yunyun Song, Arie Rapaport, Diego Munoz
Table 2: Google Adwords Campaign Results by Ad group segment.
A summary of the timeline of changes made over this campaign may be appreciated in figure 1. While
this campaign shares many of the characteristics of the Yahoo/Bing campaign (particularly in the initial
setup), to contrast and obtain more reliable data, many of the changes performed were exclusive to this
campaign (in particular those related to the budget).
Fig 1. Google Adwords Campaign Timeline of Changes
MS&E 239: Introduction to Computational Advertising
Yunyun Song, Arie Rapaport, Diego Munoz
Initial Setup and Adding Keywords
Initially, we only put three ads on the campaign with limited keywords (see Fig 2) to test the user
response and segmented ads to represent three unique user bases (“Economical”, “Energy Saving” and
“Personalized”) that have different purchase/search preference. However, as the campaign continued to
run, we monitored the performance of existing ads and added more ads and keywords over time. In
particular, we included the ads “Tahoe Homes” to target at potential second home buyers in Tahoe area
as well as ads with more vivid tones to attract potential users (see table-1).
The longest ads run over four weeks while the shortest for only one week.
Fig 2. Google Adwords Campaign initial keywords, titles and creatives.
Soon after our initial assumption on customer segmentation, we found the difference in user search
intention was not obvious by looking at the clicks driven by certain keywords. Which means a majority
of users did not necessarily search by the variety of keywords we set (those that were intended) to
represent users‟ distinct need but rather search on common queries and features. This does not fully rule
out the possibility of users who search with a specific purpose, but rather implies that users might start
with general search queries and end up with clicking the ads (or multiple ads) that best match/reinforce
their preference after seeing the ads pop up.
To add more keywords, we took advance of the „Opportunity‟ Tab on the campaign website, which
offers the use of the “Keyword Tool” to identify attractive and more relevant keywords. We then applied
these keywords on this campaign (or on both, this and Yahoo/Bing‟s) to understand how specific ads
correspond to the user intention. We found that certain keywords responded to the user search much
better and generated more clicks than most of the other keywords; these high performing keywords
include “modular home/s”, “prefabricated home/s”, “prefab home/s”, as well as “green home/s” which
also drove some fair amount of clicks.
MS&E 239: Introduction to Computational Advertising
Yunyun Song, Arie Rapaport, Diego Munoz
In Table 3 we present a summary of the keywords used for this campaign.
Table 3. Google Adwords Campaign keyword comparison.
Also keywords that had impressions but no clicks included “low cost house”, “build green home”,
“energy saving home”, “dream green home” etc.
From our observation of keywords clicks, we identified that the word “house” didn‟t resonate well with
the users as the word “home”; as well as the word “energy saving/efficient” to the word “green”. Also
we found that the plural of the keywords (e.g. “prefab homes” conceivably drove as much clicks as the
singular form “prefab home”). Further, simple and shorter expression such as “modular home” and
“prefab home” are more favored by users compared to longer expression such as “custom modular home”
or “modular prefab home”, etc.
All the keywords were set to be broad match in order to capture the most clicks, however, under such
mechanism there is possibility that some of the clicks could be unwantedly driven (by users looking for
information that is less or not relevant to the ads). This issue was mitigated in our campaign by setting
“negative keywords” (keywords that upon search by unintended users will not prompt the ads). The
primary intention behind the negative keywords was to minimize the unjustified campaign cost and
avoid confusion and dissatisfaction of users looking at irrelevant ads. These negative keywords include
“solar home”, “solar prefab home”, “solar home design”, “green home products”, “green home guide”
etc.
MS&E 239: Introduction to Computational Advertising
Yunyun Song, Arie Rapaport, Diego Munoz
Auto biding
We set all the keywords to bid on an automatic basis (with a bidding limit of initially $1.5 and finally
$2), which is monitored by Google itself to increase the clicks with minimum CPC. Given such a
bidding strategy, there is no option of scheduling for the ads to show up at a predetermined time and
date.
This is because we have very little idea about the user search behaviors in the beginning and the tight
time-frame for running the ads doesn‟t allow us to gain much insight within a very short time; therefore
we decided to let Google navigate through all the dynamics for the optimized strategy.
We did try manual bidding (Nov.6-Nov.7) for different keywords, however, the results weren‟t very
encouraging with a slightly increased number of clicks but higher average CPC compared to auto
bidding (see Fig 3). We changed back to auto bidding afterwards.
Fig 3. Google Adwords Campaign clicks by day vs Average CPC.
Change of Title and Creatives
Table 2 summarizes the initial ads we designed for the campaign, however the performance of these ads
in the first three days was not quite satisfactory given an average total clicks of 17 and a low average
position at 7-8. We then modified the ads content iteratively to make the ads appear more appealing and
relevant.
MS&E 239: Introduction to Computational Advertising
Yunyun Song, Arie Rapaport, Diego Munoz
Table 4. Google Adwords Campaign Final Ads groups.
We uniformly started the creatives with “We build” which contains a more proactive tone and clearly
shows the advertiser‟s role as a house builder (instead of green home advisor or energy saving experts
trying to persuade people to adopt energy efficient ways of living in a house, etc.) Given the limited
space for creative, we also try to capture the value of service our client provides in housing building
with separate adjective words such as “modern”, “affordable”, “durable”, “spacious” etc.
Furthermore, we changed the title to combine both general information such as “prefab” and “home
builder” and specific information such as “economical” and “green”. Table 4 shows a final version of
all running ads.
Effect of Daily Budget
Clicks were observably much higher when allowed with a higher daily budget to the campaign. Due to
the budget constraint, the ads campaign might lose significant potential clicks (therefore, conversion of
potential customers) to our client. We played very conservative in the first few weeks with a daily
budget set at first $10, then $15 after two days and $20 after five days. The average clicks per day
stabilized around 32 and average CTR at 0.79% as of Nov. 8, two weeks before the campaign was
paused. By then, the total budget used by Google Adwords stood around $200 and $90 for Yahoo.
MS&E 239: Introduction to Computational Advertising
Yunyun Song, Arie Rapaport, Diego Munoz
Fig 4. Google Adwords estimaton on potential loss of clicks based on budget plan.
We were concerned of the possibility of underusing our budget; therefore we tried two major moves to
increase daily clicks. The first initiative was to raise the daily budget from $20 to $40, and the second
initiative was to introduce ads with more vivid and aggressive tone and we set a budget separate for
these ads at $20. The increase in clicks was quite evident in the following week while the average CPC
kept at a much lower level around $0.43.
The final week, we changed the budget back to $20 per day to prevent overspending but we lost control
of the overall budget for both Google and Yahoo campaign. Additionally, the newly added ads had
positively driven more clicks in the last week and we failed to be properly cautious on the money spent
over time. By the time we paused the ads, we realized that we overshoot the allocated budget by $352
dollars, most of which was driven by the last week of advertising.
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MS&E 239: Introduction to Computational Advertising
Yunyun Song, Arie Rapaport, Diego Munoz
Fig 5. Google Adwords Campaign clicks after budget increase and introduction of new ads.
Effect of ad position, time of the day and day of the week on ad performance
Scheduling the ads to show more often during the morning and evening period when there are more
traffic and low CPC, also midnight when CTR is the highest (see Fig 6).
Fig 6. Clicks and CPC by Hour.
The campaign drove the most clicks during weekends (see Fig 7). CTR was the highest on Saturday but
also stable during other days of the week. This suggests that budget of campaign could be evenly
allocated to each day of week but a bit higher on Saturday.
Fig 7. Clicks, CTR and CPC by Week.
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MS&E 239: Introduction to Computational Advertising
Yunyun Song, Arie Rapaport, Diego Munoz
Fig 8. Position vs Clicks by Day
On the other hand, position and CTR seems to be rather independent to each other, which means higher
position (smaller number shown in the exhibit) of ads shown on the result page doesn‟t evidently drive
higher clicks or higher CTR.
Yahoo/Bing Ad Campaign
The Yahoo/Bing Campaign ran from 10/25/10 until 11/16/10, and has its overall statistics summarized
in the following table:
Table 5: Yahoo/Bing Campaign Overall Results
In Fig. 9 we present a timeline indicating where each of these types of changes was implemented. Due to
the amount of tests and modifications performed over the duration of this campaign, we will only go
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MS&E 239: Introduction to Computational Advertising
Yunyun Song, Arie Rapaport, Diego Munoz
through the most relevant of these.
Fig 9. Yahoo/Bing Campaign timeline of changes
October 25 (Initial Campaign settings):
We started the campaign with a monthly budget of $100, a daily budget of $10, targeting all hours of the
day and making all bids equal to $1. Furthermore, Fig. 10 show the initial keywords, title and creatives:
MS&E 239: Introduction to Computational Advertising
Yunyun Song, Arie Rapaport, Diego Munoz
Fig 10. Yahoo/Bing Campaign initial keywords, titles and creatives.
Besides similar changes in bidding limit and variety of keywords variety as in the google adwords, the
most relevant and pronounced change in driving better ads performance was in creatives:
Creative Changes 1 :
Group: Prefab Houses
Title: From “mkdesigns” to “Amazing Prefab Homes”
Text: From “Affordable and well-designed {keyword}” to “Customize your own prefab home.
Learn more today!”
Creative Changes 2:
Group: Energy Efficient Houses
Title: From “Energy Efficient Houses” to “Green Homes”
Text: From “Save in Energy Consumption” to “Save Money with our Energy Efficient Houses.
Learn More Today!”
The changes in creatives produced very good results, the number of clicks started to increase and the
CPC started to the decreased. Unfortunately, we didn t́ have more budget, therefore we had to stop the
campaign and we couldn‟t test these new ads more thoroughly.
Ad Distribution
We got some impressions in the content advertisement but we didn t́ get any click. It is probably more
suitable for other types of products, but seems to perform poorly for Blu Homes‟ products.
Fig 11. Yahoo/Bing Campaign impressions vs clicks.
Hours of the Day & Days of the Week
MS&E 239: Introduction to Computational Advertising
Yunyun Song, Arie Rapaport, Diego Munoz
Fig 12. Yahoo/Bing Campaign impressions and clicks by time of day.
The interesting results here are that between 2pm and 7pm the number of clicks decreases a lot. The
reason for this is probably that people don t́ have a lot of time at this moment and are driven by ads on
first positions or perhaps not intent of research purchasing options for homes.
In line with the google adwords, we get higher CTR on the weekends. This is probably because people
have more time and they click many ads and not only the ones that are in the first positions. It makes
sense that people will be looking into features and purchasing options for homes on their free time,
which is usually on weekends.
Fig 13. Yahoo/Bing Campaign impressions and clicks by day of the week.
Keywords
From all the keywords we used, around 7 of them were the ones that generated most of the clicks. The
CPC was similar between the keywords, but “house plans” was lower than the others. The CTR was also
similar within the keywords but “prefab houses” was much higher.
MS&E 239: Introduction to Computational Advertising
Yunyun Song, Arie Rapaport, Diego Munoz
Table 6. Yahoo/Bing Campaign keyword comparison.
MS&E 239: Introduction to Computational Advertising
Yunyun Song, Arie Rapaport, Diego Munoz
Correlation Matrix
In order to do this analysis, we took each observation as one keyword-day and we have computed the
correlation matrix between these variables.
Fig 14. Correlation matrix between different metrics.
There is a strong negative correlation between the Impression and the CTR. Apparently, it is possible to
obtain a lot of impression with very broad keywords, however, these will not necessary drive to clicks
and will only decrease CTR.
Recommendations
1. Invest in the ads that drive high clicks but have low CPC, which is useful for testing unproven
markets, e.g. Tahoe Homes.
2. We have identified a set of best performing ads (those associated with “prefabricated homes” and
“house plans”), which have attracted high clicks and cost low on average CPC. These ads are
worth investing on and should be tested on more campaigns, for longer periods, to gain more
insights on user needs and ads relevance.
MS&E 239: Introduction to Computational Advertising
Yunyun Song, Arie Rapaport, Diego Munoz
3. Use both formal tone in creatives to gain attention from conservative users; and vivid, persuasive
tone to gain attention from more users.
4. Initially try auto bidding on keywords. Take advantage of the ad company‟s algorithm to
optimize the result (maximize clicks with lower expense) and gain insight from the data over
time. Another benefit of auto bidding is to acquire data of clicks across all day instead of running
out of budget quickly, which prevents us from gaining valuable information on the ads
performance.
5. Niche market defined by language spoken (e.g. Spanish and Chinese) is unattractive in general.
Don‟t too invest in these markets now, but it is still possible to attract a few very cheap clicks
with them.
6. Scheduling the ads to show more often during the morning and evening period when there are
more traffic and low CPC, also midnight when CTR is the highest. Due to the heavy traffic,
weekends are periods were running the ad campaign should be a priority.
7. Budget of campaign could be evenly allocated across each day of week but a higher on Saturday.
Set the budget that ensures the expected CTR.
8. Segmenting the market itself didn‟t help as much as trying to segregate Blu Homes product
features, and finding keywords that drive these features.
9. Balance tradeoff between impressions and clicks. Broad keywords will help make it to the ad list
on each search; but too broad or an unattractive creative will gain no clicks. Keywords such as
“prefab homes” and “house plans” are good for this, but must be accompanied by creatives that
related Blu Homes product features (report contains full list).
10. Users don´t search for “economical” products even though they are looking for non-expensive
products or services. The term “economical”, especially for house selling, this terms ends up
translating to “cheap” and conveys the idea of bad quality.
11. The ad campaign‟s performance can drastically change with slight changes, or even seasonal
trends! It is very wise to monitor the performance on daily basis.
References
Profile of Home Buyers and Sellers 2008. National Association of Realtors.
Priced High on a Hill: A Study of Housing Affordability in San Francisco. National Association
of Realtors.
Profile of Home Buyers and Sellers 2007. National Association of Realtors.