Welcome to Part 2 of Affinity Targeting! In Part 1 – identifying your affinity segments, we took a look at how we go about finding our ideal affinity segments for our industry, how to set up the necessary tracking codes to pull in the data and explored exactly what affinity segments are.

Today we shall be looking at more advanced techniques on drilling down even deeper within the data to identify potential customers who meet the following criteria:

  • High Conversion Rate
  • High Revenue
  • High Site Engagement.

Once this has been discovered using advanced filters in Analytics, we can use a secondary dimension to break out our categories by age and/or location. This information will then be used to create targeting groups in Adwords and develop highly targeted ads for those groups.

Creating an Advanced Filter

To start creating an advanced filter on the above criteria, we must first navigate to our Affinity Segments tab in Analytics. This can be done by going to the following: Audiences > Interests > Affinity Categories. Once there we should see all the data in the account if all the steps in Part 1 have been completed. 

Set a date range of at least 3 months so we can get a good average amount of data and also organise the data by highest Revenue Drivers/Converters. You should be presented with the highest converting affinity segments/revenue drivers as shown in a screenshot from one of the Search Factory client accounts below:

Affinity-Segments-Highest-Converting-Ordered

However, we want to break this out by using advanced filters to show segments that have a high conversion rate, high revenue and high engagement rate. To do this, you can click on the ‘advanced’ link found next to the search button in our affinity segments breakdown. You should be presented with a view like this:

affinity-segments-advanced-filter

Now you can start filling out all the custom filters to break down the data and remove information that we don’t want displaying. This then gives us the segments we should be utilising as this will change what is displayed from our previous overview of the top 10 above.

The filters that we used on the current data can be seen in the below screenshot; however, this will vary between client accounts and a judgement on ideal averages should be made based on what you believe is a good performing segment compared to site average etc.

affinity-segments-filtered-optionsUsing these advanced filters on the affinity segments, we reduced 88 possible segments down to 7 as illustrated below:

affinity-segments-filtered-data-view

Secondary Dimensions

But, we want to go one step further and break out our Adwords AdGroups by age/gender. This, coupled with the filtered data we have just created, gives us ample room to create highly targeted ads and drill down our targeting methods to provide the best chance for success when using the Display Network.

We can now use age in our filtered overview. Do this by selecting ‘Age’ as a secondary Dimension. When we set this, you can see in this account that the best age range to target is ’18-24′ years old.

affinity-segments-age-secondary-dimension

We can also change the Secondary Dimension to ‘Gender’ to give us an overview of the top performing segments by gender. As you can see from the data below, ‘female’ does not appear as a top performing dimension. Therefore it is safe to assume we can ignore females from our targeting criteria when creating the new display campaign.

affinity-segments-gender-secondary-dimension

When setting the AdGroups up in our new Display Campaign, we can be sure to only set age targeting to ’18-24 male’ users along with all these top performing affinity segments.

Conclusion

As you can see from the above data, we can finely tune the targeting methods used in Google Adwords by breaking out a multitude of filters to achieve optimal results when trying to discover new clients using the display network. Overall tweaks to targeting is necessary and split testing against targeting methods is always ongoing. Using affinity segments is a great way to discover new criteria and target methods from users who have already converted on your site, thereby helping to target similar users who are more than likely to convert as well.