Blu Homes: Online Advertisement Campaign Report

Transcription

1 Blu Homes: Online Advertisement Campaign Report Yunyun Song Arie Rapaport Diego Munoz

2 General Ad Campaign Strategy MS&E 239: Introduction to Computational Advertising 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.

3 MS&E 239: Introduction to Computational Advertising 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 nonwhite, 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).

4 MS&E 239: Introduction to Computational Advertising 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

5 MS&E 239: Introduction to Computational Advertising 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

6 Initial Setup and Adding Keywords MS&E 239: Introduction to Computational Advertising 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.

7 MS&E 239: Introduction to Computational Advertising 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.

8 Auto biding MS&E 239: Introduction to Computational Advertising 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.

9 MS&E 239: Introduction to Computational Advertising 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.

10 11/8/ /9/ /10/ /11/ /12/ /13/ /14/ /15/ /16/ /17/2010 MS&E 239: Introduction to Computational Advertising 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 Clicks Avg. CPC

11 MS&E 239: Introduction to Computational Advertising 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) Hour of the Day Clicks Avg. CPC 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 % 1.20% 1.00% 0.80% 0.60% 0.40% 0.20% 0.00% Clicks CTR Fig 7. Clicks, CTR and CPC by Week.

12 10/25/ /26/ /27/ /28/ /29/ /30/ /31/ /1/ /2/ /3/ /4/ /5/ /6/ /7/ /8/ /9/ /10/ /11/ /12/ /13/ /14/ /15/ /16/ /17/2010 MS&E 239: Introduction to Computational Advertising Clicks Avg. position 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

13 through the most relevant of these. MS&E 239: Introduction to Computational Advertising 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:

14 MS&E 239: Introduction to Computational Advertising 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 t 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 t get any click. It is probably more suitable for other types of products, but seems to perform poorly for Blu Homes products. Hours of the Day & Days of the Week Fig 11. Yahoo/Bing Campaign impressions vs clicks.

15 MS&E 239: Introduction to Computational Advertising 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 t 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.

16 MS&E 239: Introduction to Computational Advertising Table 6. Yahoo/Bing Campaign keyword comparison.

17 Correlation Matrix MS&E 239: Introduction to Computational Advertising 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.

18 MS&E 239: Introduction to Computational Advertising 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 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 National Association of Realtors.