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In Google Analytics Essential Training, Corey Koberg shows how to use the Google web analytics platform to generate and evaluate information about the visitors to a web site, including data on site traffic, user behavior, and marketing effectiveness. This course covers the out-of-the-box functionality, from account creation to reporting fundamentals, and explains how to glean insights from the vast array of data available.
As we saw in the segmentation intro, we can use that segmentation functionality to evaluate performance. Say, for example, which traffic source is converting better? This is very important to know, after all, if you have one source converting at 94%, and one at just 28%, obviously one is more valuable to you than the other. Or is it? Just like averages and aggregates lie, we'll need to make sure we don't consider metrics in isolation. We'll consider the full context of the numbers we are seeing to truly understand what's going on. Now clearly, in this case, if I have to choose one over the other, I'll take the 28% any day over the 94%, because 28% of 80,000 is a lot, and 94% of 17 is very little.
But context is about much more than just Web metrics. We tend to get so focused on them that we forget the other aspects of our business that are interrelated. For example, we have a lot of travel related clients, some here in the Caribbean, and I can tell you that their Web site will convert a lot differently on a day like this, than on a day like this. So we wouldn't want to conclude the changes we made to the site were a disaster, and we should revert them right away, without realizing those numbers in the context of this external weather event. And more than merely understanding why our performance is that way, if we are savvy about our analytics, we can actually use that to our advantage.
For example, when Hurricane Ivan was blowing over one of our clients resorts, we saw a huge spike in traffic. Now digging into our analytics, we found it was almost all due to hurricane related searches. Initially there was some concern that this was effectively bad press that would do damage to the brand, but some creativity allowed us to react and take advantage. In Caribbean resorts it's common to offer a hurricane guarantee. In this case, the guarantee offers the chance to come back during better weather with all kinds of upgrades and freebies. So by changing the homepage from the picture of the sunny paradise to a huge flash page all about the hurricane guarantee, they were able to salvage that traffic, generate new stories in the press, and get many folks to associate in their mind the guarantee with their brand, not just the scenes of horrific hurricanes, all because they knew how to use analytics to their advantage.
Taking metrics into context is important in lots of ways. I'm reminded of the CEO of the auto company who was ecstatic showing off this chart trending down, and showing how much money they were losing every day. So why was he happy? Because he wasn't losing as much as they were expected to lose, and not as much as their competitors were losing. Keeping the data in relative context is always important. And you can imagine the plight of the Web marketing manager for this hotel next to the Eiffel Tower. Bidding on keywords and ranking in the search engines just got a whole lot tougher for the Hilton in Paris a few years back when searches on Paris Hilton suddenly got way more popular.
And the point here is that it had nothing to do with their Web site, their campaigns, their analytics, or really even their business, but it had a huge effect on the online keyword searches. And we hear this all the time; oh, they're not really my competitor, they just have the same name, or we just share the same keywords. Well, then you are competition online. We call this accidental competition, and it's important to realize that your competitors offline often have little to do with your competitors online. In this next graph, I'll point out two aspects of analysis that are critical to making correct decisions about our site.
I'll give you a second to guess what industry this is. It's actually travel as well. Now, in my house, booking travel usually involves my wife and I doing some research when we get a chance, but then not actually booking until we can both get a free minute to sit down, confer, look at the calendar, etcetera. This always seems to work out to a Sunday night when there is no big plans, and we actually have a few minutes. But just because that's how it works in my house, I can't make the mistake of thinking my personal bias is the same as all my clients. In fact, it looks like, based on this convergence graph, that's not the case at all.
This graph tells me most people roll into work Monday morning and say, I can't handle this, I need a vacation, and jump online. So the key here is that, besides not letting our personal bias cloud the data, we also need to recognize the prevailing trends in our industry, such as days of the week. We certainly don't want to compare how a campaign did that ran on a Sunday and one that ran on a Monday, because unless it was massively different, Monday will win every time, and we will conclude the wrong thing. Most trends are more than just days of the week, but seasonal as well. For example, if one of your keyword was fireworks, and you saw a huge spike here around the beginning of July, do you assume that your new AdWord's campaign must be the reason? Of course not.
The searches go up for everyone around that time, because of the 4th of July. Now, that's reasonably obvious to us. But what if we saw an even bigger spike earlier, like in November. Could it be from Halloween? Well, we know the first step in our analysis is segmentation, and we immediately see that all the traffic is coming from India, and centered around Diwali festival keywords, which, like our 4th of July, causes a spike in firework searches. Now, this trend wasn't initially obvious to us, but by using the tools available, we can understand whether we can claim success due to our marketing campaign, or was it simply a rising tide that floats all boats, and had essentially nothing to do with our actual site or marketing.
Now the key here is that understanding these trends will allow us to compare apples to apples by controlling for those external factors, and keeping our data in context.
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