Welcome to the second post in our "Interpreting Your Data" series for affiliate advertisers, brought to you by Google Affiliate Network analysts. View all posts in the series here. 

Calculated metrics provide great insight into how specific variables are performing. A few of these commonly used metrics are conversion rate, click through rate, and average order value.
In this post, we will use examples to discuss how you can use calculated metrics to interpret your affiliate program data. 

Reviewing performance data using calculated metrics:
You’re in the Google Affiliate Network interface, reviewing performance reports and notice your conversion rate has increased. You may think -- “That’s great news!” Or, is it?

Before celebrating, you’ll need to ask a few more questions and take a deeper look to determine whether an increase in conversion rate is really a positive indicator for your overall program.

This is because conversion rate is a calculated metric that is generated by combining two other metrics -- in this case, conversions divided by clicks. Whenever you use a calculated metric, you should understand what’s happening to both of the underlying metrics to to determine an overall trend.

Example 1:

• In period 1, you have 33 conversions and 100 clicks, so your conversion rate is 33%.
• In period 2, you have 25 conversions and 50 clicks, so your conversion rate is 50%.

As you can see, the conversion rate goes up in period 2. Is that good? There are fewer conversions in period 2 and fewer clicks. You should investigate why clicks went down before you can determine if this is a positive or negative indicator.

Understanding metric composition:
With an affiliate program, you should also consider the composition of each metric. That is, the composition of all of the publishers that are driving clicks and conversions. Each individual publisher composes one part of each metric.If you see conversion rates go down, you may think that this is negative indicator. To determine the significance, you should check if the conversion rate is down for every publisher and if you added any new publishers. 

Example 2:

• Publisher X had 33 conversions from 100 clicks totaling a 33% conversion rate. 
• Then you add publisher Y, who generates 200 clicks, 20 conversions totaling 10% conversion rate.
• These two publisher combined generate 53 conversions on 300 clicks, for a conversion rate of 26.5%. 
• The combined conversion rate is down, but that’s only because you added a publisher that drives more clicks at a lower than average conversion rate. 
• You still have 33% conversion from publisher Y that drove 100 clicks.

In this example, it’s important to look more closely at the new publisher. It’s possible they have a lower conversion rate due to their business model. Adding this publisher to the program changes your overall number.

In summary, when you’re looking at the movement of a calculated metric (such as conversion rate and average order value) you shouldn’t stop there. To understand the shift, you should do a bit more investigating to find out why that metric moved.

For additional resources on analyzing your program, please see this analysis checklist help center article. 

Posted by Dan Filowitz, Manager of Affiliate Operations

Welcome to the first post in our new "Interpreting Your Data" series for affiliate advertisers, brought to you by Google Affiliate Network analysts. View all posts in the series here.
Affiliate program success can be evaluated in different ways, so you’ll need to clearly define performance requirements and metrics early. Questions you should seek to answer include:

- What is happening?
- Why it is happening?
- What actions can I take?

In this post, we’ll cover time periods and getting started with performance analysis.

Time Periods
The first thing you'll want to decide is what time periods are important to you. Do you want a broad,  monthly view or do you need to get more granular with a weekly view? Next, decide how you want to compare your time periods.  Year over year will help take seasonality into account.  If you are a new affiliate advertiser, week over week and/or month over month will help show how your program is growing.

Performance analysis
After the time period comparisons are determined, start by looking at your main metrics and see if they’re up or down.  It's important to look at more than just transactions.  An increase in transactions is valuable but you'll also want to see if your clicks increased to really understand the reason behind your increase.  Did your opportunities just convert better or did you have a larger base of clicks?

Now, it's time to dig deeper into your program and examine changes in publisher performance. Take a look at your publishers using the same time comparisons. This will help you identify and sort by publisher activity to find the biggest increases or decreases. If only a few of your publishers saw large gains, review any actions you took with them. Did you purchase an ad placement? Did you increase their commission rate or run an exclusive affiliate offer with them? If you saw increases across many or most of your publishers, this could be a result of a sale, or might be tied to publishers implementing creatives or offers that better appealed to your customers.  Once you're able to make conclusions about the cause and impacts of program changes, you can take the appropriate steps to optimize publisher performance and drive more conversions.

It's important to understand when gains or losses are due to seasonality and when they’re due to actions taken by you and/or your publishers.  For instance, if December is your top month, January sales are going to be down when comparing month over month.  This is why it's helpful to use year over year comparisons and understand the seasonality of your business.

For additional resources on analyzing your program, please see this help center article.
Posted by Elliott Chapman, Account Strategist