Why Pogo Sticking is Not a Ranking Factor

Posted on Sep 13, 2017 in search algorithms

You’re not alone if you fail to see the distinction between a ranking factor and a factor that was used to train the algorithm. However, this inability to comprehend the distinction has caused a lot of misinformation about how CTR is used in search algorithms. I will try to explain this missing information and hopefully the distinction will be clearer.

Pogo sticking is when a searcher clicks on a link from Google, doesn’t like what they see, then returns to Google to change their search phrase or else to click on another web link.

A ranking factor is something that directly affects the ranking of a single website for a specific phrase. Training often has no relation to the sites that are ranked. I’ll explain.

  • Training refers for example, teaching a machine to understand that a page with the word BUY in it is commercial and not informational.
  • Training can also refer to the example of a query that refers to an entity that is a brick and mortar store. Training on millions of these kinds of entities can teach the machine that when this kind of entity is queried in a specific kind of a way, that a local result is preferred.

When a pogo hop happens, the machine can look at millions of those events and determine that the searchers preferred an informational site over a commercial site for certain kinds of queries then adjust the algorithm accordingly because it learned what the user prefers.

The machine can learn that the search query was vague but that for these kinds of queries, with certain kinds of entity types in them, they could score a better result by showing them a more specific kind of site. This was learned from studying how users refined their vague queries. That way, Google can better identify what a user means when they make a vague query, such as with RankBrain.

The machine can also learn that users prefer sites that are local to them and adjust the rankings, based on previous searches, as learned through millions of data sets.

THEN, this can be incorporated into the modification engine which happens AFTER all the sites have been ranked. What happens next is that the sites in positions one through ten could include sites with poor ranking factor metrics.

They can have very few links for example. But the machine knows that this site with a few links (your friendly neighborhood coffee shop) is the right answer because it is local to you and most users prefer coffee shop results that are local to them.

Traditional ranking factors did not matter in the above search results, like h1, links, etcetera. What mattered were factors such as geographic data or entity type data. And that emphasis was learned from pogo sticking that happened on some other sites that are totally unrelated to the website of the little coffee shop on the corner near your house.

Pogo sticking was NOT a ranking factor for that coffee shop in position 1. Pogo sticking DID provide the data to help the algorithm learn that for coffee shop queries, a local result is best.

Pogo sticking was used for training. It was not used for ranking. Do you understand the distinction now? 🙂

Does that make sense?

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