Brad Geddes / PPC Geek
Official Google Ads Seminar Leader.
Author of Advanced Google AdWords.
Co-Founder, Adalysis.
(703) 828-5811‬
Brad Geddes's Theories on Marketing Profit by Impression : The real metric in PPC testing

Profit by Impression : The real metric in PPC testing

The current success measurements for PPC split testing are woefully inadequate. Each metric only gives a portion of the story, they do not actually tell you the entire story.

There are three measurements that truly matter:

  • Profit
  • Profit by Impressions
  • Profit by Click

Profit is a metric for an entire campaign (more below).
Profit by click/impression should be the success metrics for split testing.

In split testing one wants to determine what ad copy, landing page, keywords, etc are providing the best value for your site. The reason profit alone does not matter with split testing is that often all of your tests do not receive the same exposure, therefore you’re metrics could be skewed by a single ad or landing page receiving too much exposure, receiving a high ROI, but poor CTR, etc. In split testing you are learning about your advertising – not making final decisions.

The entire conversion process start at the search query. Not the click. Not the page view. Not the sale. In order to understand the entire chain of search query to ad copy view to click to landing page to conversion one has to use two new metrics: Profit by Impression (PBI) & Profit by Click (PBC).

Consider the current success metrics:

CTR (click through rate) only measure how many people click on your ad. It does not measure conversion rate or profits.

Average CPC (cost per click) only measures what you pay for a click, again, there is no conversion data associated with this metric.

Conversion rate is a percentage of how many people converted compared to saw your landing page. It does not include CTR, CPC (cost per click), ad spend or even profit margin.

Cost per conversion tells you how much it cost you to have a visitor perform a specific action. It does not include how many people converted, the conversion percentage, or even the ad’s click through rate.

ROI (Return on Investment) / ROAS (Return on Ad Spend)-These are percentage metrics of profit compared to costs. They do not incorporate click through rate, conversion percentage, or ad views.

Profit should be a metric that looks at a campaign holistically. If you want to look at individual parts of a campaign, you should also look at individual parts of the profit. (If looking at an entire campaign, please read:Forget ROI: Show me the Profits)

Each of the above metrics is just that, a metric. One piece of information in a sea of statistics.

Let’s consider the entire PPC process.

The search process starts when a consumer has a question to be answered. This could be anything from where do I buy shoes to exploring vacation options. At this point, the searcher forms an idea of what to look for. This idea is translated into keywords, or a search query. After inputting a query into a search engine, the searcher is presented with a set of search results (which include ads). At this moment, your ability to make a conversion has already started.

When your ad is triggered, it’s considered an impression. At the moment your ad has a statistical impression, it also has the ability to connect with the searcher.

The searcher will then make a decision: to click your ad, to click another option on the page, or just search again if they don’t see something that appears to answer the question they are asking.

This is the first metric that needs to be taken into account: impressions (when your ad is presented to a searcher).

If your ad makes a connection with the searcher, then he will click your ad. The act of clicking on an add then triggers two metrics that must be taken into account:

  • CTR: The number of clicks divided by impressions
  • CPC: Your CPC (cost per click)

When CTR & CPC are taken as a whole between all visitors, you can determine:

  • Total ad cost
  • Average CTR
  • Average CPC.

A visitor then arrives on your landing page (the page on your website that you’ve designated to be connected with particular ad), this triggers yet another statistic: page view.

When a searcher inputs a search query, she has certain expectations of information. If your ad meets that expectation, the user will click your ad resulting in a page view. At this point in time, your landing page must meet the searchers expectations. If your page does not meet expectations, she will not follow the action you wish her to take.

If your page meets his expectation and has the proper landing page elements to convince him to follow your action, he will be considered a conversion (A conversion is a predetermined action that measures the success of your advertising campaign. It could be a sale, a download, a contact, etc).

When someone converts on you landing page, this sets off another slew of metrics:

  • Conversion rate (page views divided by conversions)
  • Cost per conversion (number of conversions divided by ad spend)
  • ROI/ROAS (sales amount divided by ad spend – if you’re not an ecommerce site, you need to determine the value of a contact, download, etc in calculating these statistics).

Each one of these is still just a statistic. Let’s think about the consumer, the one who ultimately determines the success of your campaign.

A consumer’s conversion process contains a list of expectations:

  • Search query results – expecting certain results to examine to answer a question
  • Ad copy information – sets an expectation for the consumer about what’s on the website
  • Landing page information – consumer has an expectation based on search query and ad copy, if this is not met, the consumer will leave. The ad copy and keyword query sets the expectation.

Each of those expectations plays off one another. The search query has an affect on the ad’s CTR. The ad copy has an effect on the page’s conversion rate. The ad’s cost per click has an effect on the total ad cost and ROI. The landing page has an effect on conversion rates. The entire process is interrelated. If you cut off any part of the formula, you are not taking into account the entire search to conversion process.

Since the conversion cycle starts with the impression, this is where the calculation of profit margins should also begin.

Let’s consider the below chart. This chart is based upon a fixed fee $25 per sale. It’s also based on testing 5 (A-E) different scenarios.

A ‘scenario’ is a set of values you are testing.. This could be:

  • 5 different tag lines
  • 5 different titles
  • 5 completely different ads
  • 5 different ads going to 5 different landing pages
  • 1 ad going to 5 different landing pages
  • etc.

Don’t feel limited by what you can test. You can test anything. It’s a matter of understanding the metrics after you’ve run a test that is important to learn.

Highlighted in Red is the top performing row for each statistic.

  Test A Test B Test C Test D Test E
Impressions 6703 9080 7858 8956 5689
Clicks 200 450 420 896 256
CTR 2.98% 4.96% 5.34% 10.00% 4.50%
Avg CPC $1.06 $0.35 $0.52 $0.63 $3.99
Cost $212.56 $156.32 $220.25 $560.32 $1,020.56
Conversions 32 34 44 58 59
Conversion Rate 16.00% 7.56% 10.48% 6.47% 23.05%
Cost per Conversion $6.64 $4.60 $5.01 $9.66 $17.30
Sales $$ ($25/ conversion) $800.00 $850.00 $1,100.00 $1,450.00 $1,475.00
Profit $587.44 $693.68 $879.75 $889.68 $454.44
ROI 376.36% 543.76% 499.43% 258.78% 144.53%
Profit per Click $2.94 $1.54 $2.09 $0.99 $1.78
Profit per Impression $0.09 $0.08 $0.11 $0.10 $0.08


In this chart, there are two obvious winners:

  • Test A for profit by click
  • Test C for profit by impression

It is important to note, neither of these columns are the tops in terms of CTR, Sales, ROI, Conversion Rate or Cost per Conversion.

Why did they win this test if they aren’t tops for any of those statistics?

Simple, those statistics only tell part of the story. Test B has the lowest cost per conversion, the highest ROI, the lowest average CPC. In most scenarios, people would consider that a winner. However, when we look at how much the tests make per click (per visit) the test doesn’t win out because of how the entire conve
rsion process takes place: search query to click to landing page to conversion.

Test C is a clear winner in profit by impression.

It is also important to note, if the above numbers were entire campaigns and not a test (i.e. the results were a campaign in it’s entirety being measured by it’s performance) then Test D would be a clear winner due to it’s profit margin.

If we received the above results on a test, what would be the next step?

There are a few answers:

  1. Analyze Test A to see if there is a way to bring in more clicks.
  2. Analyze Test C to see if there is a way to bring in more impressions.
  3. Analyze the statistical winner to learn why they won that metric.
    • Can Test B tell us how to get more impressions?
    • Can Test E tell us how to convert visitors?
    • Can Test D tell us how to get more clicks?
    • Etc…
  4. Put together a strategy for A & C based on the numbers (let the numbers tell the story. Theory is great, but eventually, numbers will give you facts)
  5. Repeat test.
  6. Measure new results.
  7. Repeat.

When running a test, one has to both understand when a variable is the best to reuse, but also why certain variables won individual metrics (i.e. why an ad has a high CTR, why a landing page has a high conversion rate, etc). By combining the Profit By Click and Profit By Impression winners with the knowledge of why the other variables preformed best in specific metrics then one can leverage the entire testing statistics in putting together new ads, landing pages, and tests.

The above test was run with a static item cost ($25). For many sites, the total order and average order amount will also be statistics you wish to measure.

In the below chart, the base numbers are the same, the only number that was changed was Sales $$. This is the combined value of all the sales made during the test period. Of course, if the sales value is changed, the profit margin will also be affected. Average sale was added as a metric.

  Test A Test B Test C Test D Test E
Impressions 6703 9080 7858 8956 5689
Clicks 200 450 420 896 256
CTR 2.98% 4.96% 5.34% 10.00% 4.50%
Avg CPC $1.06 $0.35 $0.52 $0.63 $3.99
Cost $212.56 $156.32 $220.25 $560.32 $1,020.56
Conversions 32 34 44 58 59
Conversion Rate 16.00% 7.56% 10.48% 6.47% 23.05%
Cost per Conversion $6.64 $4.60 $5.01 $9.66 $17.30
Sales $$ $560.23 $200.23 $567.23 $1,420.00 $1,489.12
Avg Sale $17.51 $5.89 $12.89 $24.48 $25.24
Profit $347.67 $43.91 $346.98 $859.68 $468.56
ROI 263.56% 128.09% 257.54% 253.43% 145.91%
Profit per Click $1.74 $0.10 $0.83 $0.96 $1.83

Profit per Impression
$0.05 $0.00 $0.04 $0.10 $0.08


The above numbers can be analyzed just like the first chart above. It’s important to show how average sale and total sales can affect the test. The two previous winners are no longer the top performing tests. Test D, which only won on CTR earlier, is the clear Profit by Impression winner. Test E is the clear Profit by Click Winner.

In this scenario, besides the information that we already walked through that needs to be analyzed, there is an emerging trend. Test E is full of converting traffic (which could be the keywords, ad copy that speaks only to buyers, etc). Determining why it has such a great conversion rate and then applying those results to Test D (which has the higher CTR, 3rd lowest CPC, 2nd highest conversions, 2nd highest sales, 2nd highest avg sale, highest profit)  would bring about some pretty significant changes and improvements.

These tests are but two simple scenarios in a sea of online metrics. One could easily add page views per visitor, time spent on site per keyword,Value of a Lifetime Customer, catalog requests, etc. Metrics are easy to add, what’s important is assigning a dollar amount to those metrics. Determine what a phone call is worth, what a catalog request brings in sales, what an email does for your bottom line. Determine not only what action you want someone to take – but what that value is to your company.


Profit by impression (PBI) and Profit by Click (PBC) are the best metrics to determine when split testing ad copy or landing pages. One can determine profit by impression/click numbers by individual line of an ad copy, by the entire ad copy, by landing page, etc.

When split testing, think about the actual goal: sales. By understanding how each line of an ad interacts with a landing page and with a search query, by understanding how a landing page is affected by a keyword, by understanding how a keyword interacts with a specific ad, by understanding all of your metrics you can finally determine which is the proper combination to be running.

If you know these statistics:

  • Impressions
  • Clicks
  • Cost of ad campaign
  • Conversions
  • Sales $$

You can derive these statistics:

  • Click through rate (CTR)
  • Average cost per click (CPC)
  • Conversion rate
  • Cost per conversion
  • Average sale
  • Profit
  • ROI
  • ROAS
  • Profit by Click
  • Profit by Impression

These are elements in a PPC campaign that you control. If you can measure it and understand the metric, you can change it. Knowledge of metrics is key. Knowledge of what to do with the metrics is crucial.

Profit by Impression & Profit by Click might be a new concept in the world of PPC advertising; however, it’s a concept I’ve been successfully using for several years.

Only by understanding how the entire PPC cycle works, from query to ad copy to click to landing page to user action can one really understand the value of advertising.

Understand the value. Measure the value. Improve the value.

Three simple steps to successful advertising.

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