Application of Data Mining in Google Online Marketing Challenge

Transcription

1 Application of Data Mining in Google Online Marketing Challenge Jie Chen,Chiungyi Tseng May 1, 2010 Abstract In this project, we utilize Google Adwords (a "Pay-per-Click Model" ad platform) to increase online business for our client, who is just started its business and facing three major marketing challenges: totally offline marketing, local geo-targeting, and small business scale. We present a systematic problem solving path to help our client (a local business owner make markeitng decision by providing him the most profitable keyword portfolio and competiting strategies. Starting from a single targeting keyword "vegetable delivery", we successfully mined 66 most relevant keywords through "Frequent pattern mining" among six industries ( 4800 keywords). Further, by carefully study the competitor strategies and statistics of desecive factors, we develop our static and dynamic model to simulate "total clicks" and by optimization, we finally derived 21 words with proposed prices as our final portfolio. 1

2 1 Introduction Google Adword is a "Pay-per-Click Model", advertises only pay when viewer click on the links. It provides a great platform for small or local business who would only be charged when the links are clicked by the "targeted" group of customers who are specifically interested in the products and services by searching corresponding keywords.this would significantly reduce the total ad cost but still fulfill the business marketing needs. Our client, " is a newly started local fresh vegetable/fruit delivery business owner, who is in great need to promote his service to Chapel Hill (in North Carolina) area through effective Google Adwards. By carefully study their business, we found the business category is really new, there are only 3 competitors around North Carolina. Also there is a higher percentage of people enjoying "organic foods" which would be a great opportunity for our client to grow its business base on the high needs in this area. However, compare to a much wider scope of businesses, it suffers from less recognition, especially online. The traditional competitors such as farmer s markets, grocery stores, or organic stores have been mature and are existing for many years. Even free customized delivery service with low price positioning is a huge attraction to busy professionals and health food seekers, the targeting individual consumer are far from being clustered in this new emerging market. How to grow their business in Chapel Hill area? How to make more people recognize this service? We hope to address below three foundamental problems: How to convert the current "offline" market to "online"? How to focus on "Local customer" in advertizing (Geo-targeting)? How to dealt with limited budget due to their small business scale? 2

3 1.1 Google Adwords Mechanism Google search engine matches highly relevant search results to any search query entered. In addition, Google displays relevant AdWords ads above and alongside search results. These are labelled Sponsored Links as shown in previous section. Google separates search results from ads for user distinction. Thus advertisers design AdWords ads to target the user s search (or wants or needs), matching ads to the user s query. Advertisers first choose keywords that relate to their website or product offerings. Then, when a user enters the same or similar keywords into Google, the advertiser s ads are shown. We are interested in the impression (Ranking) that our ad will potentially be placed. Google adwords search engine determines the position of the ad based on a combination of the ad s relevance and the proposed bid relative to those of advertisers competing on the same keyword. See below Table 1.1. The mechanism used, an adaptation of the generalized second-price auction, ensures that the fee charged per click will be one cent more than the minimum necessary to keep the same position on the page. Edelman et all.(2006) demonstrate the advantages of such auction-based sponsored advertisement, now favored by search engines, over the generalized first-[rice auctionbased structures previously used.they conclude that in such a setting, empirical data anlysis is highly appropriate. Table 1: Terms and Definitions [-2.5ex] Term Average CPC Impressions Position(RANK) Clicks CTR Conversion Converstion Rate Quality Score [0.1ex] Definitions and Formula The actual average cost-per-click charged by Google. The number of times a particular add was shown Theposition in which the ad appeared,position 1 being thetop position. RANK=CPC*Quality Score. The number of times a particular ad was clicked. Clicks= Search*Impressions*CTR Click-through rate. The proportion of impressions that are clicked. CTR=Clicks/Impressions A click that ends in a dsired action (purchase, sign-up, etc.). The proportion of clicks that convert(crate), CRate=Conversions/Clicks Ad score base on overall advisors statistics. Google Adwords provides many useful tools for campaign manager to fetch relavent info, such as Estimated Monthly Clicks, Estimated bidding price, and rate of competition (of certain keyword), see below figure of the interface Google Adwords provided. 3

4 1.2 The Challenges However, detail data especially the bidding information and keywords portfolios are unobservable, which make it hard for us to carry out complete competitor analysis merely through this interface. The specific challenges are: Hard to find out our direct competitors keyword portfolio. Can t access competitors bidding strategy (keywords and price) and campaign period change pattern. Forluma of Quality Score and CTR...etc are invisible. (eg. "Quality Score" is determined by many factors: historical CTR, history of account, the historical CTR of the display URL, the quality of landing page, the relevance of the keyword to the ads in its ad group, the relevance of the keyword and the matched ad to the search query, account s performance in the geographical region where the ad will be shown, other relevance factors.) The search engine will automatically fetch surounding businesses and increase when it can t find similar business in small region, so that we can t control on our search range. To tackle with above difficulties, we proposed a way to bypass the unobservable "Quality Score" and find a easier path to get maximum total "Clicks" as our temperal goal. See detail methods in Section Project Goal Find accurate marketing position (industry and competitors) and target consumers (searching behaviors). 4

5 Mind the most relevant keyword set (keyword portfolio) Build a optimization model to obtain most clicks within budget. Find the most significant ads quality factor which improves our CTR and Clicks. 5

6 2 Models and Solution 2.1 Models Two different types of models were proposed in this project. The first model is a deterministic static model wich provide us the optimal keyword portfolio and the second is a dynamic model which considers more complex time series pattern and more complax correlations between factors Model Assumptions and Simplification Since CTR is the major contributor to Quality score, we propose to use CTR rather than unobservable Quality Score to fit in our regression model. Firstly, we observed stable trends of CT R i and N i cross monthly data that Google provide, thus in the first static model we assume they are constant parameters. Also we look at the relatoinship between average ranking vs factors suchas freqency of impression, competition and differnt sample time. By studying from the simple regression data (See figures below), we observe some: Positive correlation between average ranking and Freqency of Impression ; Negative correlation between average ranking and Competition for our 66 alternative keywords; Mature ads generally hold stable ranking positions across different time sampling. Above findings suggest that our competitors are not using window bidding strategy and switch their bidding price frequently, especially in our interested period. Which help us to assume 6

7 our bidding price and final optimal solution would be generally effective cross time Static Model As introduced in Table 1.1. Whenever an ad keyword is searched, it has some chance of being displayed on the first page ( impression is defined as top 3 shown ads), and when it is shown up, it has some change of being viewed and clicked. As a simplification, we consider the probability that an ad is clicked on to be dependent on two factors: a) the probability that it is shown, and b) the probability that it is clicked on given that the keyword it bid on is being searched. CT R i = Clicks i /impressions i = P r(click i pos i ) P P OS,i = P ositioned i /N search,i = P r(pos i Sarch i ) We firstly build a price vs. keyword matrix and fetch each individual percentage of impression our of multiple search.as we use average CT R i and N i in weighting function and keep the price and keyword selection as our change variables (x i,j and y i ). y i = { 0, if keyword i is selected; 1, otherwise. x i,j = { 0, if keyword i and price j are selected; 1, otherwise. Then by properly fetching our weighting function data, we are able to maximize our Number of Total Click (per day): W i,j = N searchi P posi,j CT R i,j MaxT otalclick i,j = i,j W i,j x i,j With our integer selection constraints and daily budget limitations: x i,j Cost i,j DailyCapital i,j 7

8 jx i,j = y i 1 i 66 0 j Dynamic Model Suggested in M. Richardson s paper 1, the probability of clicking on an ad drops so significantly, so the CTR and Position (Ranking) has inter correlation for different words and cross differnet time frames. In order to estimate CTR more accurately, we need to further relax our assumption in Static Model. We suggest to collect CT R i and Ranking data in our next step, and use Longitudinal logistic regression to find their correlation to further help us predict a more accurate CTR in our portfolio. In addition, M. Richardson s paper also suggest that following ad features will have significant impacting to CTR in a exponential relationship. CT R i = e ZT i Z = i w i f i (ad) where f i (ad)is the value of the ith feature of the ad, specified as below: Landing page: The URL that a user is redirected to upon clicking the ad. Bid term ( keywords ): The query for which this ad should be displayed (this may be multiple words, e.g., machine learning books ). Title: The ad title, shown to the user. Body: The text description of the ad. Display URL: The URL shown to the user at the bottom of the ad. Clicks: The number of times the ad has been clicked since it was entered into the system. Views: The number of times the ad has been seen since it was entered into the system, as described in section 3. 1 refer to reference 2. 8

9 2.2 Solution Path We take first 4 steps in data mining and next 4 steps in modeling and optimization 2. To find the the best "Keyword Portfolio", we need to get sufficient keywords in our pool first Step 1: Start from "Vegetable Delivery", the best match word of our client business, by script fetching we easily find relavent local competitors and their most frequently used keywords. We define this set of words as our "Most Relavent Keyword Pool" (Pool A) Step 2: It would be more cost effective if we can avoid bidding the same words our competitors bid, so we want to find most relevent and frequent alternative keywords. If word A is bid together with any words in our Pool A, we think they are relavent. We just need to pick the most frequent ones. Therefore we fetched again using 5 closely related business categories (family,health,food,home and realestate) and build our new matrix (companies vs. keywords they bid), then run a frequent pattern mining to filter the most frequent keywords.we define this set of words as our "Alternative Keyword Pool" (Pool B) Step 3: Finally we want to see how our bidding price impacting the impressions (Ad Position), for each individual word in Pool B, we collected the price of the word we plan to bid and the ads positions by repeat searching. Putting the matrix together with our estimated "weight function", we eventually got our targeted "Keyword Portfolio" (Pool C). See below graph of this mining path. 2 See full flow in Appendix 1 9

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11 3 Methods 3.1 Query Script Since Google Adword lets new advertisement or advertisement with lower ranking occasionally show, it is not sufficient determining the rankings based only on one query. Thus, we consider taking average of the ranking of 10 queries would give more reliable result. The script automatically queries Google search engine 10 times with respect to a given keyword and record the ranking of each advertisement. Repeat the process with respect to all the keywords in the pool. Figure shows the snapshot of result of 6 queries. The rankings of are 2, 3, 3, 3, 1, 2. In this manner, we record the rankings of other advertisement. Below figure shows part of the result: query all nature foods 10 times continuously and record and ranking of advertisement.. denotes the advertisement does not show at the query. For example, OrganicLiaison.com only shows at 6th and 8th query. The rest of the time, it does not show. The script can be easily extended and fetch data at the specific sample time, which is important for our future work: sampling the ranking data at intraday intervals and weekly intervals and building dynamic model. 11

12 3.2 Frequent Pattern Mining We adopt the most popular data mining algorithm fp-growth 3 for our Frequent pattern mining. Given portfolios of top-ranked companies, local competitors and similar companies in other states, we want to pick up keywords that are highly related to the portfolios. View each company as a transaction, each keyword as an item, we apply fp-growth algorithm to find out the frequent keyword sets. Among these sets, those contain the element in portfolio (competitors and top companies) are the most related words. Through this process, we successfully shrink 5000 keywords in the pool to 66 most related words! 3.3 Bidding Information Fetching Fetch bidding information: To optimize the bidding strategy, we would like to know the relationship between bidding price and ranking of the 66 keywords. We fine tune the bidding price (Delta= 10 cent) and observe its ranking. 3.4 Integer Programming and Optimization Results After simplification, the problem is boiled down to just a integer linear programming as stated in section Here we adopt the most popular software AMPLE (using cplex solver) to obtain our final results. 4 However, due to the variable and constraint limitation of the software, for future larger dataset we propose to switch to Matlab instead. 3 Refer to Appendix 2. 4 See results in Appendix 3 12

13 4 Results and Discussion Follow the methods described above, we derived our final keyword portfolio. 5 In general, the effectiveness of our frequent pattern mining is well verified by the final portfolio, about 60% words are selected from alternative pool. 4.1 Problems Observed By carefully studying the differences between each set of keywords, it is not hard to find that: General click rate is low for keywords related to our business. For example, in our alternative pool, only 4 words are frequently searched in North Carolina, which means even our mature competitors can t get enough search on these words. Notice that a interesting keyword Farmer s market is not appeared in our final portfolio, while it is a high search rate words both in North Carolina and our Competitors list. It might because our over simplified weighting function and our low quality score as a new comer. As a new advertiser, we found our Quality Score (ranging from 0.6~0.7) is low, especially on the hot keyword like Farmer s market. We may use our dynamic model to further improve our CTR and competitiveness on these kind of words. 4.2 Fp-grwoth Low Threshold Problem Given large amount of data ( 5000 keywords and various companies), fp-growth seems to be the inevitable choice. However, to get enough keywords (66 keywords), we have to lower the threshold ratio to The low threshold may weaken the power of persuasion of our result, it also suggests that the choices of companies are very diverse; thus, it is difficult the find out a keywords that most the companies buy. Overall, the market of vegetable delivery service is still a new market. The business owners have not tried to attract customers from competitors; thus, words they choose are rarely overlap. Besides, we find top company by observing the raking on Google Adword. Nevertheless, we found the top ranking was taken by other companies. Also, an advertisement may not maintain its ranking once it changes its bidding strategy. Choosing top companies without examining its history seems risky. Furthermore, a top ranked company on Google Adword may not be the top company in real word. The correlation between the rank in virtual world and the rank in real world needs to be verified. 4.3 Security Problem It is surprising that Google Adword provides loose protection for advertisement viewers. An advertisement can show up on Google s page immediately after proper bidding price is offered 5 See results in Appendix 3 13

14 without approval or review. It takes few days to prove / disprove an advertisement; thus, there is a few days gap before an illegal advertisement is removed. A low quality or even harmful website shows up for days. Google Adword allows advertises show a nice-looking URL, so that viewers can not know the real URL at first glance. The nice-looking URL may direct views to a harmful website. This may cause security problems. It seems that Google Adword does not track the history of advertisers. A malicious advertiser would not be suspended after its advertisement is disproved. Just slightly change the title, same material can show again. 14

15 5 Conclusion Starting from a single targeting keyword "vegetable delivery", we successfully mined 66 most relevant keywords through "Frequent pattern mining" among six industries ( 4800 keywords). The effectiveness of our frequent pattern mining is well verified by the final portfolio, about 60% words are selected from alternative pool. Further, by carefully study the competitor strategies and statistics of desecive factors, we develop our static and dynamic model to simulate "total clicks" and by optimization, we finally derived 21 words with proposed prices as our final portfolio. The 21 words picking by our algorithm are not only incluede some alternative keywords but also some highly searched words like Produce Market, and are well meeting in our small amount of daily budget. But there are some areas we can futher improve in our future study: a) improve marketing study to further integrate more useful terms into our original pool, in order to get higher search rate keywords pool in begining. b) improve our model to capture more realistic factors, in which one way is to predict CT R i and N i by doing longitudinal regression, and c) improve our geo-targeting to filter out non-local ads and obtain more useful keyword pool to save manual competitor anlaysis work. 15

16 Appendix 1: Solution Flow Note that we haven t finish the dynamic modeling, we will need to actually run the campaign and fetch more data.. 16

17 Appendix 2:Frequent Pattern Mining Algorithm Fp-growth is first proposed by Jiawei Han. "Mining frequent patterns without candidate generation: A frequent-pattern tree approach, J Han, J Pei, Y Yin, R Mao - Data mining and knowledge discovery, 2004". Please put this at the reference. 17

18 Appendix 3: Keyword Pool Results 18

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20 . References [1] Jiawei Han, Jian Pei, Yiwen Yin. Mining frequent patterns without candidate generation. SIG- MOD [2] M. Richardson and E. Dominowska, R. Ragno. Predicting Clicks: Estimating the Click-Through Rate for New Ads. International World Wide Web Conference Committee, [3] S. Gupta, M. Bilenko, M. Richardson. Catching the Drift: Learning Broad Matches from Clickthrough Data [4] Google Adwords Online Documentation: [5] Berman, Ron and Katona, Zsolt. The Role of Search Engine Optimization in Search Rankings. 20