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Judging your PPC Ads by Analytics Metrics

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I thought I’d write a quick blog post as I’ve been working on some ad test results this morning and wanted to share a quick set of techniques for judging winners without conversion data. Most of the time we talk about either click-through rate or conversion rate as the key way we judge our ads. Here at Hanapin we combine the two and use IUC – impressions until conversion – as our key metric. Either way, the story is usually the same – clicks & conversions.

 

What do you do then if your account is predominantly focussed on generating calls and your call tracking software won’t go down to the ad level (mine is campaign level)? In this case your Google Analytics data is probably the best way to judge and there are three key metrics I use:

 

  • Bounce Rate
  • Average Pages per Visit
  • Average Visit Duration

 

It seems pretty logical to assume that the ad with the best set of these stats probably generates the most calls. There will be a few cases where this doesn’t hold true, but generally it tends to work well (and has been shown to work by the increasing number of calls we’re getting).

 

Getting Analytics Data in your Account:

 

The first thing you’ll need to do if you haven’t already is link your Google AdWords and Google Analytics accounts. Here’s Google’s guide if you need help with this step.

 

Once that’s done you need to go back into your ‘my account’ settings and select ‘linked accounts’

 

Analytics1

Then select Linked Accounts -> Google Analytics

 

Analytics2

 

Finally, you should see something like this:

 

Analytics Linking

 

Once you’ve linked a profile with your AdWords account you’ll be able to start pulling Analytics stats into your AdWords interface. To see the data, first wait a couple of days for it to pull through, and then go to your Columns drop down and select Google Analytics:

 

Analytics4

 

There are currently 4 metrics you can include – Bounce Rate, Pages / Visit, Avg Visit Duration and % New Visits. My Ad report now includes these columns:

 

Analytics5

 

All sorted? Great.

 

Analyzing your Ads:

 

So now we have our Analytics data to look at I want to share with you a couple of quick tips for analyzing your data. The first is just to manually go through and select ads based upon the metrics you see in your AdWords GUI. However, if you want to be a bit cleverer and judge your ads over larger data sets, pivot tables are the way to go.

 

Go into AdWords and download your ad data. Make sure you set up the right date range – for accounts with lots of traffic this might be a week, for smaller accounts it could be a month or longer.

 

Analytics6

 

Once you open up your report you’re going to need to do add three new columns – which are ‘clicks * Analytics metric’ (see below). This is so that we can work out our averages correctly later on.

 

Analytics7

 

Once you’ve done this, create your pivot table by clicking anywhere in your set of data and then ‘Pivot Table’ – in my version of Excel it’s under ‘Data’ here:

 

 Analytics8

 

I’m going to Pivot my Description Lines 1 & 2, but you can do it whichever way you prefer – Destination URL, Headline etc. – they should all generate some meaningful data for you.

 

 Analytics9

 

Notice my bottom four tabs in the ‘Values’ section of my Pivot Table builder here – these are all manually added ‘calculated fields’.

 

Analytics91

 

This is where our earlier added columns come in handy.

 

Analytics92

 

To add each of my Analytics metrics into my Pivot Table I am creating a calculated field as shown above – ‘clicks * Analytics metric’ divided by clicks. In my tables I do the same for my 3 chosen Analytics metrics, and for CTR.

 

Here’s the results from the test I just pulled:

 

Analytics93

 

As you can see we have a pretty clear winner in Ad Copy 4. It won’t always be as clear as this, but it still helps you to make judgment calls beyond simply using CTR. If one of my ads had a great CTR but terrible Analytics metrics, I’d know the clicks I’m getting aren’t the ones I want.

 

Hopefully that helps you get started with using Analytics to judge your ad copy. Let me know if you have any thoughts or tips to share in the comments below!

About the Author

Sam Owen

With four years of Internet Marketing experience, Sam joined Hanapin as an Account Manager shortly after moving from Cambridge, UK to the States. He has worked predominantly in online price comparison, overseeing the launch of a number of sites worldwide, and managing over half a million clicks per month spending six-figure monthly budgets. Find him on twitter @SamOwenPPC or on his Google+.
  • http://www.webmaisterpro.com/ Kaloyan Banev

    This is very important aspect and will definitely help to determine which keywords convert and which are not doing any good. Keywords need to be tweaked constantly and for some niche keywords may be seasonal.

    • Sam Owen

      Thanks Kayolan! Definitely agree with you about the seasonality, I saw some big swings in which ads were generating the best bounce rates from fall to winter.

  • Santiago Castrogiovanni

    Thanks a lot for sharing this with us.

    • Sam Owen

      Thanks for reading!

  • Ben

    Why do you need to add the 3 columns in the report? Shouldn’t they just export directly from the AdWords interface?

    • Sam Owen

      It’s because I’m pulling a pivot table – and just summing those numbers will give me a falsely weighted average. For example – let’s take average position: Say you have 1 impression at average position 1, and 1000 impressions at average position 9. If you don’t add the extra columns, your average position will come out as 5. If you weight your results like I have, your average position will show up as 8.99 which is more accurate.

      This is why I add the three columns and use a calculated field. You need to do the same thing if you ever want to work out your CTRs in a pivot table too for example.

      • Haukur

        Same goes for quality score calculations. Nice post Sam.

        • Sam Owen

          Thanks Haukur – this is exactly the approach we use for QS analysis.

  • Ben

    Also why do you label the columns “clicks * bounce rate”, etc.?

  • Ben

    Hi Sam, I think I see what you are saying, so you have “clicks * bounce rate” as the calculated field, but then how do you wind up with the numbers for just “bounce rate” in your final screen shot?

    • Sam Owen

      So to clarify:

      In my downloaded table, I manually add the clicks*bounce rate field and calculate it using formula ‘=(cell with clicks in)*(cell with bounce rate in). For my pivot table I then go and create another calculated field which is (clicks*bounce rate / clicks) – which when you cancel down = bounce rate. The reason for doing this is that it gives you a weighted average by clicks rather than just an average.

  • David

    It will be better to include the number of conversions for each Ad Copy. I am in a tricky situation that lower CTR converts better than the higher one. I am not talking about the huge diff in CTR, probably 0.86 vs 0.70 with conversions 10 vs 12.

    • Sam Owen

      Right – conversions are always the best metric, but sometimes for whatever reason you can’t track them. In those cases you should look at your Analytics metrics.

      For your issue I’d advise judging your ad copy by Impressions Until Conversion (IUC) = Impressions/Conversions – This gets around the issue of one ad having better CTR and worse C/R as it combines the two.

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