Wednesday, March 25th, 2009
Intuit Payment Solutions has teamed up with SEER on their SEO campaign. Intuit Payment Solutions offers payment processing services, including credit card processing, that allow merchants to accept payments in convenient ways.
Tuesday, March 17th, 2009
I’ve been on the prowl lately for a CPA/ROAS calculator to determine the profitability of my PPC accounts. The main problem that I run into is that most of the free CPA/ROAS tools out there don’t consider all of the factors that I’m interested in evaluating when setting CPA & ROAS goals.
For example, let’s say that I arbitrarily decide that anything over a 600% is good enough for me because it most likely means I’m making boat loads of money. Unfortunately, that’s not always the case. Check out the little test I performed below.
Let’s say I have an e-commerce campaign with the following stats:
Impressions: 108,708
Clicks: 5181
CTR: 4.77%
Conversions: 495
CPA: $9.37
Conversion Rate: 9.55%
Cost: 4,643
Avg. CPC: $0.90
Avg. Sale: $57
Profit Margin: 10%
Revenue: $28,339
ROAS: 612%
Upon first glance, it appears that this campaign is doing well. The main indication is the 612% ROAS. Let’s see how well the campaign is actually doing when profit margins are factored into the evaluation.
Using a neat free ROI tool that Bonnie on the SEER PPC Team found on Twitter, I input a few campaign stats and I’m able to see my actual ROI as well as where CPA needs to be in order to break even.
This calculator just kicked my ROAS’s butt. With a 10% profit margin, my actual ROI is -39%. So instead of making $6.12 on every $1 I spend, as my ROAS lead me to believe, I’m actually losing my $1 investment as well as an additional $0.39 on that dollar. Yikes! At least now I know that in order to break even, I’m going to need to decrease by CPA to about $5.74.
The ROI calculator above inspired me to make my own calculators to measure performance at the account and campaign levels since I’ll be using it quite often now to measure profitability.
Campaign Performance
Even though I don’t consider operating overhead at the campaign level, I don’t want to ignore it entirely. Often times, account owners will want to know how profitable their accounts are when both advertising cost and the agency management fee are factored in. Monthly management fees tend to be somewhere between 10-20% of monthly total online spend. To keep it simple, let’s jump right in the middle at 15%. So in this case, the operating expense is the monthly management fee.
Below are performance stats at the account level over a one month period, along with the 15% monthly agency fee:
So, overall the account is profitable.
Here is how the calculator works:
The orange fields are input fields.
Cost of Goods sold: For this particular account, the average profit margin is 28%. So COGS sold in this example is 72% of total revenue, or (0.72*$59,118.50) =$42,565.
Operating Expense: In this example, we assumed a monthly fee equal to 15% of spend, or (0.15*$6,541.12) =$981.15
Break-Even CPA: (Profit after advertising/Conversions)-(Advertising Cost/Conversions). Or, ($59,118.50-$42,565.32-$981.17-$6,541.12)/922)-($6,541.12/922)=$16.89
Actual ROI: (Profit after advertising/Advertising cost), or ($9,030.89/$6,541.12)=138%
After all of the monthly costs, my client ended up making $9,012.06. Woo hoo!
This calculator is still a bit of a work in progress so if you see any formulas that should be tweaked, please don’t hesitate to leave a comment!
Tuesday, March 17th, 2009
I’ve been on the prowl lately for a CPA/ROAS calculator to determine the profitability of my PPC accounts. The main problem that I run into is that most of the free CPA/ROAS tools out there don’t consider all of the factors that I’m interested in evaluating when setting CPA & ROAS goals.
For example, let’s say that I arbitrarily decide that anything over a 600% is good enough for me because it most likely means I’m making boat loads of money. Unfortunately, that’s not always the case. Check out the little test I performed below.
Let’s say I have an e-commerce campaign with the following stats:
Impressions: 108,708
Clicks: 5181
CTR: 4.77%
Conversions: 495
CPA: $9.37
Conversion Rate: 9.55%
Cost: 4,643
Avg. CPC: $0.90
Avg. Sale: $57
Profit Margin: 10%
Revenue: $28,339
ROAS: 612%
Upon first glance, it appears that this campaign is doing well. The main indication is the 612% ROAS. Let’s see how well the campaign is actually doing when profit margins are factored into the evaluation.
Using a neat free ROI tool that Bonnie on the SEER PPC Team found on Twitter, I input a few campaign stats and I’m able to see my actual ROI as well as where CPA needs to be in order to break even.
This calculator just kicked my ROAS’s butt. With a 10% profit margin, my actual ROI is -39%. So instead of making $6.12 on every $1 I spend, as my ROAS lead me to believe, I’m actually losing my $1 investment as well as an additional $0.39 on that dollar. Yikes! At least now I know that in order to break even, I’m going to need to decrease by CPA to about $5.74.
The ROI calculator above inspired me to make my own calculators to measure performance at the account and campaign levels since I’ll be using it quite often now to measure profitability.
Campaign Performance
Even though I don’t consider operating overhead at the campaign level, I don’t want to ignore it entirely. Often times, account owners will want to know how profitable their accounts are when both advertising cost and the agency management fee are factored in. Monthly management fees tend to be somewhere between 10-20% of monthly total online spend. To keep it simple, let’s jump right in the middle at 15%. So in this case, the operating expense is the monthly management fee.
Below are performance stats at the account level over a one month period, along with the 15% monthly agency fee:
So, overall the account is profitable.
Here is how the calculator works:
The orange fields are input fields.
Cost of Goods sold: For this particular account, the average profit margin is 28%. So COGS sold in this example is 72% of total revenue, or (0.72*$59,118.50) =$42,565.
Operating Expense: In this example, we assumed a monthly fee equal to 15% of spend, or (0.15*$6,541.12) =$981.15
Break-Even CPA: (Profit after advertising/Conversions)-(Advertising Cost/Conversions). Or, ($59,118.50-$42,565.32-$981.17-$6,541.12)/922)-($6,541.12/922)=$16.89
Actual ROI: (Profit after advertising/Advertising cost), or ($9,030.89/$6,541.12)=138%
After all of the monthly costs, my client ended up making $9,012.06. Woo hoo!
This calculator is still a bit of a work in progress so if you see any formulas that should be tweaked, please don’t hesitate to leave a comment!
Thursday, March 12th, 2009
There is always something to be learned from history, either from mistakes or successes. With the current state of the economy, I chose to focus on the positive. The other day I was thinking that there must have been businesses that thrived during a time of an economic slump. After all, there is always a need for something, and so there should always be someone who is able to recognize a need and fill it. So, I did some recession research and found quite a few well known and successful companies that have grown during a recession and even during the Great Depression.
So, who are they, what did they do, and what can we learn from them?
It was in 1876 that Thomas Edison opened his first research lab. In the middle of a six year recession known as the Panic of 1873, he created one of the best-known inventions of all time, the incandescent light bulb. GE is now the third largest company in the world.
During the Post World War I recession, Walt and Roy Disney opened the Disney Brothers cartoon studio. They thrived during the roaring twenties, and continued to grow even during the Great Depression, when they launched their first full length animated feature film, providing people with a much needed escape to the movies.
Both radio and print advertising thrived during the Great Depression. They were the emerging new mediums for businesses. This helped both Proctor & Gamble and Chevrolet flourish as they invested in these new marketing techniques.
In 1957, Burger King launched the whopper. Keeping prices low, it sold for 37 cents. In 1958 they took advantage of an increasingly popular medium, television, and aired their first commercial. In the early 70s, they were the first fast-food restaurant to offer an enclosed and air-conditioned seating area for guests to dine.
During the 1973 oil crisis, Federal Express began operations, and in 1975, Bill Gates started Microsoft.
In 1980, CNN and MTV were launched, bringing something new into homes as the first all-news channel and the first music video channel.
All of these companies recognized a need and filled it well. Whether it was an innovative product or a new way to market a product, they were successful at marketing. They found a way to stand out from the competition. They were able to reach their customers and give them what they wanted. Taking advantage of the emerging advertising opportunities of print and radio proved to be successful during the Great Depression, and with the Internet as the emergent medium for businesses today, the possibilities are endless.
Smart marketers understand that this is the time to increase your marketing effortsâ¦and that doesn’t necessarily mean increase your marketing budget. While evaluating your budget changes, evaluate market changes and consumer behavior as well. It means finding out who your customers are and what they want. With social media, it’s even easier today to find your customers and their needs. Use Twitter to try searching for your company brand or products and listen to what people are saying, or try searching for specific needs to find new customers. There’s still a need to be filled during a recession, and consumers are expressing their needs and concerns all over the Internet. Good marketers know their customers. New data from Hitwise suggests that searchers are starting to use more keywords in search engine queries. Perhaps this trend comes from searchers who know exactly what they are looking for, making it even more important for businesses to really know their customers. Know your customer. Be Creative. Explore new ways of marketing to get a bigger piece of a smaller pie.
Friday, March 6th, 2009
Using Google’s Traffic Estimator tool is a good way to get an idea of how much a new campaign is likely to spend once it is launched. Nobody expects this tool to be perfectly accurate, I’m sure, but I always wondered just how accurate it is so I did a little test.
To start, I ran a Placement/Keyword report for all keywords in one of my higher volume campaigns and calculated the average keyword metrics over a 30 day period (CPC, clicks and cost) for 16 unique keywords. Then, I plugged these keywords into the Traffic Estimator Tool so I could compare the estimates to actual keyword performance averages.
For every keyword, the Traffic Estimator tool provides lower and upper estimates for daily clicks, cost and CPCs, assuming the keywords are bid to appear in positions 1-3 (actual average position for my keywords was 3.3). Here is what I found:
Google Click Estimates:
Over a 30 day period, the keywords in my list averaged 7.08 clicks per day. Google lower estimate predicted 6.38 clicks daily, or 0.7 clicks under actual average daily clicks. Google upper estimate predicted 8.5 clicks daily or 1.42 clicks over the actual 30 day average. Pretty close!
Google CPC Estimates:
Over a 30 day period, the keywords in my list averaged a daily CPC of $0.54. Google lower estimate predicted an average CPC of $0.93, or $0.39 over the actual average daily CPC. Google upper estimate predicted $1.53 or $0.99 over the actual day average CPC. Not bad.
Google Daily Cost Estimates:
Over a 30 day period, the keywords in my list generated an estimated total cost of $78.84 daily (based on their average daily cost). Google lower estimate predicted a total cost of $162 daily, or $83.56 over actually daily cost averages. Google upper estimates predicted a total cost of $250 daily, or $176.51 over actually daily cost averages. Not so good.
Google Monthly Cost Estimates:
Multiplying the actual average daily cost per keyword by 30, it is estimated that these keywords have spent a total of $2,353.32 for the month. Multiplying Google lower daily cost estimates by 30 predicts a total monthly cost of $4,860 or $2,506.68 over actual total monthly cost. Multiplying Google upper daily cost estimates by 30 predicts a total monthly cost of $7,500 or $5,146 over actual total monthly cost. Yikes!
Could daily budget caps have prevented my campaign from reaching the cost estimates Google predicted? It’s possible. This campaign in particular spent an average of 55% under the daily budget cap over the same 30 day period. But then again, bid optimization and general tweaking could have played a role as well. Perhaps if I had let the campaign run wild with no supervision Google’s predictions may have been more accurate.
Overall, it looks like the lower estimates tend to be more accurate than the upper estimates, at least for this particular list of keywords. Daily click volume and average CPC estimates are pretty accurate for the time range I sampled. Daily cost estimates are not quite as accurate. So, perhaps there is room for improvement in the accuracy of Google’s Traffic Estimator tool but it is still a great place to start when building an account or even a single campaign to get a ballpark idea of how much the campaign might spend.
Friday, March 6th, 2009
Using Google’s Traffic Estimator tool is a good way to get an idea of how much a new campaign is likely to spend once it is launched. Nobody expects this tool to be perfectly accurate, I’m sure, but I always wondered just how accurate it is so I did a little test.
To start, I ran a Placement/Keyword report for all keywords in one of my higher volume campaigns and calculated the average keyword metrics over a 30 day period (CPC, clicks and cost) for 16 unique keywords. Then, I plugged these keywords into the Traffic Estimator Tool so I could compare the estimates to actual keyword performance averages.
For every keyword, the Traffic Estimator tool provides lower and upper estimates for daily clicks, cost and CPCs, assuming the keywords are bid to appear in positions 1-3 (actual average position for my keywords was 3.3). Here is what I found:
Google Click Estimates:
Over a 30 day period, the keywords in my list averaged 7.08 clicks per day. Google lower estimate predicted 6.38 clicks daily, or 0.7 clicks under actual average daily clicks. Google upper estimate predicted 8.5 clicks daily or 1.42 clicks over the actual 30 day average. Pretty close!
Google CPC Estimates:
Over a 30 day period, the keywords in my list averaged a daily CPC of $0.54. Google lower estimate predicted an average CPC of $0.93, or $0.39 over the actual average daily CPC. Google upper estimate predicted $1.53 or $0.99 over the actual day average CPC. Not bad.
Google Daily Cost Estimates:
Over a 30 day period, the keywords in my list generated an estimated total cost of $78.84 daily (based on their average daily cost). Google lower estimate predicted a total cost of $162 daily, or $83.56 over actually daily cost averages. Google upper estimates predicted a total cost of $250 daily, or $176.51 over actually daily cost averages. Not so good.
Google Monthly Cost Estimates:
Multiplying the actual average daily cost per keyword by 30, it is estimated that these keywords have spent a total of $2,353.32 for the month. Multiplying Google lower daily cost estimates by 30 predicts a total monthly cost of $4,860 or $2,506.68 over actual total monthly cost. Multiplying Google upper daily cost estimates by 30 predicts a total monthly cost of $7,500 or $5,146 over actual total monthly cost. Yikes!
Could daily budget caps have prevented my campaign from reaching the cost estimates Google predicted? It’s possible. This campaign in particular spent an average of 55% under the daily budget cap over the same 30 day period. But then again, bid optimization and general tweaking could have played a role as well. Perhaps if I had let the campaign run wild with no supervision Google’s predictions may have been more accurate.
Overall, it looks like the lower estimates tend to be more accurate than the upper estimates, at least for this particular list of keywords. Daily click volume and average CPC estimates are pretty accurate for the time range I sampled. Daily cost estimates are not quite as accurate. So, perhaps there is room for improvement in the accuracy of Google’s Traffic Estimator tool but it is still a great place to start when building an account or even a single campaign to get a ballpark idea of how much the campaign might spend.
Monday, March 2nd, 2009
In the past three years, I have become a convert to Google Analytics. It’s definitely not the be all, end all of analytics tools. But it’s free and more powerful than most people realize. However, Google Analytics, like any tool, is only as good as the data that is in the tool and the people who use it. Below, I’ve compiled five quick tips for managing your Google Analytics profiles and data. Hopefully these tips will help you get more powerful, accurate analysis from GA.
1) Verify your Google Analytics install.
Analyzing your data presupposes that you have good data to analyze. How do you know if you have good data? The number one mistake that we see when clients have Google Analytics installed is that the installation is not complete. Often we find that pages are tagged inconsistently, which leads to holes in the data. A great way to check your pages to make sure they are all tagged is to run a SiteScan using EpikOne’s great tool. It’s amazing that the tool has scanned over 50 million pages on 66,000 sites and has found that 46% of the pages have errors.
At the same time, it’s not enough to only run the scan. I recommend that you click through several pages on the site and visually inspect the code. I’ve seen instances where SiteScan indicates that the code is complete but due to missing tags, the Google Analytics code does not get triggered accurately. Another good tool for verifying accurate code install on a page by page basis is the WASP plug-in. I generally try to click around on a bunch of pages and make sure that “Google Analytics” (or “Urchin”) shows up in the plug-in.
2) Set up multiple profiles — including one for raw data and one for testing.
Another easy way to mistakenly analyze incorrect data is to add filters to your one and only profile. Once a filter is added to a GA profile, the data is analyzed through that filter going forward. If you realize two months later that something was incorrect in that filter, you cannot go back and “reanalyze” the past data without the filter. The first thing I do when I set up an analytics account is to create two additional profiles. The first profile I usually name something like “raw data” and I make sure that I never add any filters to this profile. The second profile I name my “test” profile. I use this profile to try out different types of filters before I add them to additional profiles. I also test all my goal configurations in the test profile first. This ensures that I never have to remember, “Oh that week of data was incorrect because there was an error in the goal configuration.” After I have my raw data and test profiles, I create additional profiles for all the other segments I want to analyze.
3) Add dates to your profile names.
This is a great time saver because you’ll easily know how far back your historical data goes. My Google Analytics profiles are named things like “SEER — All Traffic 01.01.2008″ or “SEER — PPC Traffic 02.01.2009″. Then when Wil asks me for historical analyses, I can quickly let him know that we only have data back to the beginning of 2008, and I don’t have to play with the calendar dates to see when the data starts. It’s also easy for the rest of our team to know when the data starts without having to ask me every time.
4) Keep a log of your Google Analytics changes.
Because there is no “change log” within the Google Analytics interface, it’s really important to keep your own log of changes made to your Google Analytics profiles. When I review past data and notice changes in traffic patterns, I often can’t remember if that’s a true trend or a result of some configuration changes that I made. For quick reference, I’ve created my own log of all changes I make in any Google Analytics profiles. I make note of dates when I make changes and what changes I made. I also note if I review a change and any subsequent changes that I make upon review. Use any program to document your changes (Excel, Google Docs, Zoho, etc.). You’ll be happy when someone asks you, “Why did our natural search traffic drop last week?” and you are able to reference your log and say, “It’s because I removed a few more branded terms from our natural search profile last Monday.”
EpikOne has another helpful tool for tracking changes as well — the Google Analytics Notes Extension (page no longer active). Your notes can be displayed with each profile, are stored using the Google App Engine, and can be exported for easy backup purposes.
5) Learn the basics of regular expressions.
Regular expressions are so powerful in Google Analytics. Here’s a good example: suppose you want to see a report of all of the visits to your site from branded terms. For our site, it’s pretty easy because I can run a report for any keyword containing “seer” which will capture terms like “SEER Interactive,” “seerinteractive,” and “thinkseer.com.” However, we have clients where visitors often misspell or abbreviate the brand name, resulting in hundreds of branded phrases, or where individual words within the brand name are generic terms that could be part of legitimate natural search phrases. Using regular expressions like wildcards (a “.”), quantifiers (like * and ?), and the “or” operator (a |) can help identify all branded phrases. For example, we have a client called FirstOnMars. If I only looked for “firstonmars” I’d miss people who type spaces between the words. If I look for terms containing “first,” I’d capture too many broad phrases like “first episode of grey’s anatomy.” However, I can write a regular expression (first.*mars) that captures only the branded phrases I want. (In English, that reg ex basically says match anything containing “first” with any character including spaces any number of times before the word “mars”.)
For some basic info on regular expressions, take a look at this Google Analytics Help page. I’ve used reg ex for IP filtering, keyword filtering, URL filtering, referrer filtering, etc. Of all the things I’ve taken time out of my days to learn, it’s probably one of the skills that has had the most impact on improving my analyses. I keep a couple of handy reg ex references on my desk at all times.
These are five of my quick suggestions for how to manage your Google Analytics profiles and data. They’re pretty basic (I didn’t get too much into things like creating profiles for segmentation) but I’m always learning new things, so I’d love to hear other ideas on how to manage Google Analytics profiles.