Which Algorithm Change Hurt the Site? A causation/correlation issue

[Fammy]

The attached graph is from google analytics, a correlation of about 14 months of Organic Google visits with algo changes, data from moz naturally

Is there any way to tell from this which will have affected the site? for example #1 or #2 seems to be responsible for the first dip, but #4 seems to fix it and it broke around 6, or is the rise between 4 and 7 an anomaly and actually 1 or 2 caused a slip from when it was released all the way to when 7 was released.

Sorry if the graph is a little cloak and dagger, that is partly because we don't have permissions to reveal much about the identity, and partly because we were trying to do a kind of double blind, separating the data from our biases

We can say though the different between the level at the start and end of the graph is at least 10,000 visits per day

JarMzoK.png

[Dr-Pete]

It's really tough (and even inadvisable) to try to pin a traffic change to an algorithm update based solely on spikes in a graph. On rare occasion, it's pretty clear (Penguin is a good example, I've found), but in most cases there's just a lot of gray areas and the graph leaves out a mountain of data.

The big issue I see here is potentially seasonality and knowing what happened to the site and business. For example, you can look at #6 and #7 and call these dips, but that sort of ignores the spike. Is the dip the anomaly, or is the spike the anomaly? What drove up traffic between #4 and #6? Maybe that simply stopped, was a one-time event, or was seasonal.

Why was there volatility between #7 and #14 and then relative stability after #14? You could call #14 a "drop", but not knowing the timeline, it's hard to see how the curve might smooth in different windows. What it looks like is a period of highly volatile events followed by an evening out.

Without knowing the industry, the business, the history, and without segmenting this data, trying to make claims just based on dips and spikes in the graph is pretty dangerous, IMO. This could have virtually nothing to do with the algorithm, in theory.

[Fammy]

I don't understand how dates would help? Was it not clear that the red lines are the dates of algo updates?

By abstracting the data the hope was to gain insight into how to read the graphs in relation to updates, and not just get help related to specific updates which wouldn't help much the next time we have to deal with a traffic drop problem. More a question of who to think rather than what to think.

Trying to read between the lines are you saying different algo changes take different amounts of time to kick in and that's why a more detailed graph is more useful? For example if #1 was the first penguin change, would your response be different if it was the first panda change?

[Felip3]

You can use the Google Penalty Checker tool from Fruition: http://fruition.net/google-penalty-checker-tool/

I would not believe 100% on the tool results, but you can at least have an initial Analise, you'll need to go deeper to double check if this initial Analise is  100% relevant or not.

• Felipe

[jesse-landry]

This doesn't tell me anything. If you at least had dates in there you could compare traffic dips to Google Algo Updates/Refreshes.

I understand you can't reveal the domain but I will be shocked if somebody here can tell you anything without further information. This place is full of brilliant minds, but that would take some sort of a mind-reader to tackle...