SEO vs PPC

Why pay for clicks when you can get them for free?

This question often comes up when facing relatively inexperienced advertisers deciding between PPC and SEO. While I cannot refute any argument based solely on this logic (organic clicks are free and paid clicks are not), I can make a case for PPC even when SEO is at its best – which certainly is not the case for the majority of sites out there.
There are three distinct advantages that paid listings have over organic listings, and as a result these are the three most compelling arguments for integration of SEM into any marketing scheme:

1. If you don’t use SEM, your competition will poach your traffic.

It’s your algorithm!

Most SEM professionals will agree that a solid SEM program strategy is multi-faceted. The program should focus not just on bidding, but also on Quality Score, data gathering and reporting, and aggressive testing of all aspects of the program. That being said, the great promise of bid automation remains a viable, debatable, and important topic.

There is no doubt that autobid algorithms are important to a well-rounded SEM bidding strategy but they can also be detrimental. Success with autobid depends on a number of factors. Obviously, one crucial factor is how well constructed the algorithm is. Perhaps more important, though, is the level of customizability made available by the algorithm. Does it treat core and long-tail terms similarly, or is it specialized for success with a particular kind of keyword? Is it customizable in terms of defining quantity of data analyzed, statistical relevancy, convergence speed, and data tolerance leading to a bid change? Can you influence bid security, day parting, data aggregation?

Heads vs Tails

Traditionally, search marketers base their search results on a “last click wins” basis. This means that the last click a consumer makes always gets attributed the sales revenue or conversion, regardless of how many other searches were made prior. The result is that brand terms often appear hugely profitable and costly generic terms appear to offer an extremely low ROI, if any at all. This makes it difficult to correctly classify “head” vs “tail” terms.
To combat this discrepancy, whenever we estimate performance for a keyword, we also calculate a confidence interval related to that prediction. When the confidence interval is too large, it means the prediction is useless (typical for keywords with very low traffic). We then need to aggregate in a relevant way (which is usually different from the way keywords are structured in ad groups) to get a critical mass of stats.

For this reason, we offer two different algorithms for automated bid management:
- Long-tail (tail): analyzes a bucket of clicks from a given set of terms and makes bid changes based on statistical significance.
- Core (head): analyzes past performance by week, day, and hour, making bid decisions based on a variety of statistically significant externalities.

Our automated bid management analyzes traffic patterns observed at similar times throughout an account’s history and makes preemptive bid decisions to effectively anticipate consumer behavior and minimize CPCs which effectively maximizes ROI. Automated strategies base decisions upon the smallest possible set of terms. If a given KW has enough data, then the decision will be based strictly on that term’s data set. The data set is expanded until enough data is available to make a statistically significant decision. The first expansion is to the ad group level, and then to the sub-category level in terms of the KWs portfolio group.

Jacqueline Brown