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Maximizing PPC Campaigns with Predictive Analytics: Bid Optimization Strategies

Explore how predictive keyword scoring can optimize PPC campaigns, improve cost efficiency, and boost ROI. Learn about bidding strategies, keyword scoring models, and predictive methodologies.

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Maximizing PPC Campaigns with Predictive Analytics: Bid Optimization Strategies

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  1. Predictive Analytics World Predictive Keyword Scores to Optimize PPC Campaigns Vincent Granville, Ph.D. Click Forensics February 19, 2009 CONFIDENTIAL 1

  2. Problem • Advertisers bidding on keywords on search engines (PPC programs offered by Google, Yahoo, etc.) • Bidding strategy should achieve some goals, typically • Profit optimization • ROA (revenue on ad spend) optimization • Minimization of cost of user acquisition • Maximization of user lifetime value

  3. Short Term Goals • Short term ROA can be negative • Paid + organic search usually provides positive ROI • Organic search used as a leverage to buy traffic and increase reach • KPI’s: • Clicks per keyword • Conversions per keyword • Revenue, profit or return • Conversion rate

  4. Issues • Keywords with few clicks (“long tail”): difficult to predict • Attaching a conversion to a click: data quality (cookies) • Revenue numbers not known until tomorrow • New bid => Google needs to “learn” how to handle it • Real time implementation of keyword bidding subject to high volatility • Focus on end-of-day or bi-weekly algorithm • Pitfall 1: if max bid is much higher than actual CPC => Google will eventually notice! • Pitfall 2: keyword performance can be impacted by “poor” keywords in same ad group, or by impression fraud / click fraud spikes • Match type

  5. Keyword Scoring • Same as click scoring / credit card transaction scoring • Scores computed at the keyword / ad group level • Response: RPC, Conversion rate, etc. • Independent variables: binary rules • Actually, there are highly auto-corraleted • Model • Logistic regression (ridge or constrained regression) • Naïve Bayes (related to logistic regression via the odds ratio) • Decision trees or combo • Score is predictor of RPC, return or conversion rate, etc. • Conversion blending

  6. Bidding Strategy • Goals • Be able to predict response for keywords with very little historical data • Be able to predict response for new keywords • Conversion rate = f(score) • New bid = g(previous bid, keyword score, ROI, RPC, …) • Methodology • Permanent multivariate (A/B/C) testing • g is a parametric function • A/B/C: each case corresponds to a particular parameter set • Moves in parameter space driven by a simulated annealing algorithm

  7. Examples of Rules • Text mining rules used in the keyword scoring engine • Length of keyword • Number of terms • Keyword contains “free”, “new”, “2009” • Keyword contains digits • Keyword contain top 1-term word with known response • Keyword contains 2-term word with known response • Example • Keyword “used car Honda 2000” contains the 2-term word “used car” • All keywords containing “used car” have on average a 5% conversion rate

  8. Issues • Keyword cleaning • Each keyword contains multiple 1-term and 2-term words • Scalability • Most keywords contain at least one top term • With 50MM keywords and 25K top terms, 95% of the keywords contain one top term (at least) • Response is not known for most of the 50MM keywords (too granular), but it is known for each of the 25K top terms (aggregate level) • Works with new keywords

  9. Results Test Data

  10. Results: Interpretation • Keyword score is a good predictor of conversion rate • Bids are too high on good keywords, too low on poor keywords • Simple corrective action suggested • A/B/C parametric bidding strategy not discussed here • Cross validation: see next slide

  11. Results: Cross-Validation • Process 15 days worth of data using score lookup tables based on training set • No time period overlap, between training and test • Keyword overlap • Large volume of new keywords (“new” means a KW not found in training set) • Robustness against missing data / new keywords • Predictive power somewhat reduced, but still good

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