As marketers, we would love to know how all of our leads originated.  Far too often, PPC gets the boot with poor cost per lead (CPL) figures, and can be mostly blamed on attribution issues.  Attribution is simply defined as assigning credit to the source which generated the initial (or final) action.  So for example, let’s say you’re running a paid search campaign and you get 100 new visitors to your website.  However, only 2 of them make a purchase.  Unbeknownst to you, an additional 5 purchases came in over the next 7-10 days, but the source was either “direct” or from some organic search term.  In this case, the marketer might attribute 2 sales to PPC, and 5 to SEO, and SEO might win in terms of lower cost per lead.  So what’s the solution?

Multi-Channel Analytics & Funnel Analysis

Google recently launched a new feature which attempts to solve this attribution problem, and show improved ROI on AdWords.  Of course, Google is always interested in finding ways to show PPC really does work, and in this case, rightly so.  But check this out.  If you’ve properly tied your AdWords account to your GA account, and setup goals, you will see this Venn diagram.  You’ll find it under the Conversions section.

In this small e-commerce website, we can see that 4.65% of the conversions occurred when a visitor clicked on a paid ad, and THEN came in later by typing in the domain name directly.  Without this, the marketer may be quick to assume that the 2 sales from the direct path were not in any way influenced by paid search.  Now, this is neat.  I can also see what my “lag time” is between first click attribution, and the actual point of purchase.  Why do you care about this?  Well, if you have enough sales happening too far after first click, you may want to try to run a promotion to those visitors who are “on the fence” about your product or service.

First Click Attribution vs. Last Click Attribution

Marketers often debate whether to give the “first click” credit for the sale, or the “last click”.  Some might argue that even though the PPC ad created the interest and got the visitor into the funnel, it was the “last click” that finally sold them, and so that should get the credit.  This is certainly a valid point, but you have to look at a few more things, such as: a) was there a coupon or discount that may have led to the final decision?; b) was the PPC landing page set up as a lead generation, or a hard sale (i.e. “Buy Now”)?; c) was there a pricing matrix or downloadable whitepaper that may have led to the purchase decision?  In other words, did the last click lead to a decision-making point?  If so, maybe then you assign the credit to the last click, and measure PPC in terms of “cost per lead” only.

Click Attribution Issues With SEO and PPC is a post from Pear Analytics, an SEO tools and software company.

In one of our recent blog posts, we posted an ROI calculator that helps you figure out whether you’re getting enough value from what you spend on SEO by targeting the right keywords. The results were based on an extremely conservative “fudge factor,” or an estimated margin of error, of about 90%, meaning that we only used 10% of given search volumes in our calculations. However, the numbers weren’t all adding up, so we conducted an experiment using several websites. The results showed some rather major discrepancies in the search volume numbers returned by Google AdWords.

Currently, Music.com ranks #2 for the keyword “music” on Google. According to Google AdWords, this should give them a high percentage of the estimated 124,000,000 global searches that Google says occur monthly. Using the belief of Chitika Research Director Daniel Ruby that ranking #2 for a specific keyword will earn you about 17% of all searches (we use 16.96% in the ROI calculator), Music.com should be getting 21,080,000 visits from the keyword music alone per month. This may seem like an attainable goal for a keyword as common as “music,” but based on traffic estimates from Compete.com, the actual number of hits Music.com gets from this keyword is nowhere near 21 million.
So there appear to be three logical explanations for this discrepancy. First, the percentage of a given search you can expect to secure is unrealistically high. Second, the Google AdWords tool is overestimating the monthly global searches. Or thirdly—both are correct. Our initial hypothesis was that the second option was the most likely, given the large amount of research done by Chitika on the traffic percentage of Google’s search results. Our experiment’s procedure looked like this:

Step 1: We identified 4,000 sample keywords from 7 websites which we have Google Analytics data for (Peer1.com, Serverbeach.com, Voxeo.com, Music.com, Brandstack.com, Unbounce.com, and Missionrs.com)
Step 2: We then found the Google rankings for all 4,000 keywords.
Step 3: We then sorted the 4,000 keywords by the best ranking to the worst ranking and narrowed the list down to the words which ranked in the top 10 results (roughly 1,000).
Step 4: For the remaining words, we gathered the individual search volumes (broad, global)
Step 5: Next, we went to each sites respective Google Analytics account and pulled the actual traffic from each keyword.
Step 6: Per each SERP, we took the actual visits and compared that number to how many Chitika’s calculations would project it should get. The percentage of difference between the two numbers is what we call a “fudge factor”, or the percentage that Google AdWords inflates the global search volumes for a given keyword.

The results of the experiment told us two things: firstly, that Google AdWords was significantly overestimating search volumes and secondly, that this overstatement tended to rise exponentially with popularity of a keyword. Looking at the necessary deflation of Google AdWords’ search volumes for the top ten rankings on Google, we found that, on average, AdWords is overstating global search volume by 42.29%. This fudge factor is an important part of our ROI calculator and determining realistic traffic figures. So if, for example, you were to try to target the keyword “cheap dedicated hosting”, it would be wise to apply this 42.29% fudge factor to Google AdWords’ estimate of 8,100 monthly global searches.

So ranking #1 “cheap dedicated hosting” would earn a website 1,606, not 2,782, because of this 42.49% fudge factor. This is demonstrated on the graphic below.

The graph below illustrates our second finding: Google AdWords exponentially overstates search volume as search frequency of keyword increases. The Y-axis shows the true market share of a given keyword by a website which ranks #1 for that keyword and the X-axis show Google AdWord’s estimated volume. As you can see, once the search volume moves past 3,000, it becomes extremely difficult to obtain any sort of significant share of the searches. The market share is certainly nowhere near the 34.45% warranted by ranking #1. The data for the second through tenth position tell a similar story. It seems the only way to obtain a decent percentage of hits for a keyword is to target and rank for moderate keywords. So while a fudge factor of 36.8% is a good average to use, this will typically need to be increased for higher search volume keywords and decreased for lower search volume keywords. You can see from the graph below that, on smaller keywords, the market share is as high as 160%. Obviously, this isn’t accurate either, so while a fudge factor is an important factor in calculating traffic, it is far from an exact tool.

The take-away from this experiment is to exercise caution when targeting a specific rank and market share for keywords if you are relying on Google AdWord’s estimation of search volume. While it may not be an exact figure, we recommend using 42.29% as your fudge factor. This number becomes more subjective when dealing with extremely high or low volume estimations as well. For very large keywords, with volumes of 15,000 or greater, an accurate fudge factor would be about 90% and for smaller keyword, under 1,500 searches, the fudge factor is actually under stated by about 60%. The range on these percentages, although just an estimation, shows the inconsistency of Google AdWords’ estimations. The bottom line is if you’re going to spend a majority of your budget trying to target a specific position for a high traffic keyword, just know that the reward is a slice of a much smaller pie than Google would have you believe.

How Accurate are Google AdWords Search Volumes? is a post from Pear Analytics, an SEO tools and software company.

SEO is one of those things you know you have to do for your online business, but it may be hard to determine whether the results are worth the cost. In this post I want to help you work out for yourself whether the value of your SEO efforts is worth what you’re spending.

We created a calculator in Excel (free to download) for exactly this purpose, which I will explain in this post. Using the calculator, you can put in 5 keywords that you would like to target and it will give you the ROI for those keywords if you rank in the #1, #2 or #3 spot.

We will also help you figure out whether you are targeting the right keywords.

There are five steps to do this -

Step 1 : Get the search volume of the keywords you would like to target using the the Google Keyword tool.

The example site we are using is www.optimizednow.com and the keyword we would like to rank for is “saas business strategy”. Once you enter the keyword, it will come up with the search volumes for that keyword and related keywords. We look at the global monthly searches (the business in this case is globally-focused; if your company is local, use your country as a filter to get better results) and we can see the search volume is 22.

Step 2: Open Site Explorer – which will give you the current domain authority of the site.

Go to Open Site Explorer and put in the website you would like to find the domain authority for. This snapshot of the tool shows that the domain authority of www.optimizenow.com is 8.

Step 3: Use the matrix below to see whether the word is a reasonable target (we have provided both the tables in the calculator as well)

We can easily tell that the keyword is a Moderate target. This is perfect; you want to shoot for a “Moderate” target, which encompasses keywords of a search volume that you can take advantage of easily–this is the “low hanging fruit.”

Step 4: Finding similar keywords -

Using the Google Keyword Tool, choose 5 similar keywords which are a good fit for your website and which fall in that Moderate search volume range.

Step 5: Calculate the ROI

Click here to download the calculator

To use the calculator you will need to fill all the fields in white.

The keywords you need to target should be in the white columns which you’ve already got from the Google Adwords tool.

Before you calculate your ROI, you’ll need to have some basic business numbers on hand: Average LTV per customer, your site conversion rates and your SEO spend/month.

Plug in these numbers, and the green fields will give you the ROI of potentially ranking for these keywords in the #1, #2 and #3 spots.

The other assumption I have made, which you can play around with, is that it will take you 6 months to see results. If you’re targeting the right keywords, that is a good estimate of when you can start seeing results. But you can change it to a different number to see the ROI by increasing or decreasing the spend.

Hope you have fun playing with the calculator!

Is your SEO spend worth it? is a post from Pear Analytics, an SEO tools and software company.

Before you embark on a potentially expensive paid search campaign, consider this one insight that could change your mind.

While Pear focuses on organic search (SEO), we do mingle around and talk to a lot of experts in paid search. Experts who work paid search campaigns day in and day out. The other day I learned something very interesting.

The longer the sales cycle in your business, the worse your paid search campaign could perform.

In fact, if it takes you longer than 2 weeks to close a potential lead from click to sale, then paid search might not be a great fit for you. This is not an off-the-cuff statement, but rather a conclusion based on a long history of data obtained by a close partner in the business. Now there are always going to be exceptions to the rule, and there are many other factors that go into paid search ROI, but this is a good rule of thumb, and here’s why.

Paid search works better when an impulse action (like a purchase or contact) is involved. Let’s look at a few examples:

Plumbing Service – your bathroom had a backup and is flooded with raw sewage. Do you search 15 plumbers and call for quotes? Not likely. You choose the first or second option you see based on the most credible looking, easiest to contact (maybe they are open 24-hours or offer emergency response) plumber. You probably don’t care much about the price either – you just want it fixed.

Real Estate – most of us don’t buy property in less than 2 weeks. We look at lots of options, talk to lots of people, make personal visits and collect a lot of information before making the final purchase. This is definitely not an impulse purchase. Paid search can work in this case if the margins are high enough to where even if the conversions are dismal, one sale could pay for the whole campaign.

Air Conditioning Repair – it’s already in the 90 degrees here in Texas and turning on your AC is a necessity. Like the plumber, you need the next available company to service your system, and the first or second listing will get the lead.

Home Health Care – getting a parent or loved one into a home health care situation is an emotional experience. Not only is the sales cycle longer than 2 weeks, but the decision is usually not with one person, which makes it a harder deal to close. If clicks are going for $5-10 a piece, you could spend hundreds before getting a new client.

What do you think?  Do you have any examples to share?

The Dirty Little Secret About Paid Search is a post from Pear Analytics, an SEO tools and software company.

I’ve been wondering lately about Twitter.  I wonder when I send a tweet to my 800 followers, who is actually paying attention not only reading my tweet, but clicking on the link I provide.  In reality, it seems to me that we only have a small window of opportunity to be “noticed” or else the tweet just gets buried in the time line.

My guess was that any tweet has a “shelf life” of roughly 2 hours, and assuming about 5% of your followers are considered to be “active followers” (meaning they usually respond to things you post or at least consistently read them), I could expect about 20 click-throughs to the link I provided in my tweet.

The Test

I sent a post at 1:38p CST (right in the middle of the day, when hopefully most are actively using Twitter) entitled “Test: How I Evaluated the Effectiveness of Print Ads: http://bit.ly/19GkSz”; a blog I posted on this site on April 21st.  I used bit.ly to track the results, thanks to my friend @bolora.  I was using BudURL and was getting frustrated with it, so Bo said to try bit.ly and by inserting “/info” right after the .ly, I would get a full report on clicks, etc.

The Results

Right after the tweet posted, it was re-tweeted by friends @erikdarm (678 followers) at 1:53p, and then 2 of his followers re-tweeted the post; @blellowj (2,047 followers) at 1:54p and @stephenlynch (712 followers) at 1:57.  It is now 3:19p and there have been no further re-tweets, so the pass-along value may have reached its limit within the Twitter time line.

Total reach = 4,235 potential Tweeple to read and click on my tweet (my 798 followers, plus the followers of the re-tweets).

If you go to the bit.ly link, you will see a screen like the one below:

So the “Now” screen looks like this and refreshes every few seconds, so the time line keeps moving to the right.  (It would be cool if you could go back and see the clicks at the beginning – there was more activity around 2:00p with one time about 6 clicks came in simultaneously).

I think it’s safe to say the post has now exhausted it’s useful life, with the last click at 2:41p CST (it is now 3:29p and no click activity since then).

This shows the activity for the “full day”, with a total of 34 clicks at the 2:00p mark, and 15 at the 3:00p mark for a total of 49 clicks to the link in the tweet (i.e. 49 potential new visitors to our blog/website).

Conclusion

We would have to do several tests to prove this out, and I’m sure would vary if you were Robert Scoble or Guy Kawasaki, but in general (for the rest of us), from this small experiment we conclude that a “useful” tweet has the following characteristics:

-a shelf life of about 1 hr 15 min, and then it “dies”
-1 to 2% click-through rate on links

Which means that this is not a whole lot different than direct mail for example, without out the cost of course.  What do you think?  Is Twitter really a good way to communicate and share useful knowledge, or is it simply getting lost in the mix?

Test: Is Anyone on Twitter Paying Attention? is a post from Pear Analytics, an SEO tools and software company.

I came across this excellent four-minute video by Phil Fernandez, President and CEO of Marketo, and he explains the importance of nurturing your sales leads into actual sales. He says that 50% of your leads are not yet ready to talk to a sales person, and are not ready to purchase. By properly engaging with these prospects, through webinars, emailing white papers, etc., we can, over time, get 70% of these leads ripe for sales. This is the “consideration” stage of the Awareness – Consideration – Purchase steps in all marketing efforts, as described by colleague Steve Patti of Polarity. Here is the video:

How To Increase Your ROI on Sales Leads is a post from Pear Analytics, an SEO tools and software company.

If you like tinkering with your website, then you have probably heard of A/B or multivariate testing. This is where you can quickly test new things on your website, such as copy, images, call-to-action buttons, placement, etc., and see which combination effectively leads to more conversions. A/B testing is essentially testing two versions against each other that could be completely different, where as multivariate testing assesses multiple areas of the page and tests all possible combinations. So, if you had 3 versions of a headline, 3 versions of an image, and 3 versions of a button, you would have 27 possible combinations in a multivariate test.

Traditionally, multivariate testing has been where each possible combination gets equal play; meaning each combination is displayed equally to your traffic. Once a visitor is exposed to one of the test combinations, they are given a cookie so that each time they return while the experiment is still in progress, they will see the same combination. Google Website Optimizer is considered a traditional multivariate testing tool, where their algorithm will determine the winner based on how many combinations you have, and how many visitors is required to statistically determine a winner.  Their model shoots for a 12% minimum improvement in conversion rate at an 80% confidence level.

Now we have what’s called adaptive multivariate testing, which is offered by a company called Hiconversion. What they do is the same multivariate set up, but instead of giving equal play to each possible combination, they only test page combinations that are consistently performing in producing conversions. They claim that their methodology dramatically reduces the amount of traffic required to reach a statistically correct “winner”. This real-time adaptation also reduces the amount of loss leads or sale conversions that a typical test can produce.

Courtesy of Hiconversion.com demo presentation

This is sort of how Google AdWords works when you opt for the “auto-optimize” feature, which instead of displaying your ads evenly, they display the best performing ads more often.

Well, I personally was never a believer in AdWord’s optimization feature. I thought they determined a “winner” way too early, and so I’ve always turned off the optimization feature, and did my own split testing within the ad groups themselves.

I also had the same disbelief for the Hiconversion tool initially. First of all, how could you really determine a winner if all combinations were not played evenly? If one was played more than the other, then of course that combination would win!

So I began to ponder about the mathematics involved with this (I know…that’s what I do). First let me start by saying that I do not know how the Hiconversion algorithm works (I did ask them, but they said they would have to shoot me), so this is just my rationalization.

Let’s imagine that our multivariate test had 100 possible combinations. As the testing starts, each combination gets equal play, or 1%. The algorithm can quickly see which combination is starting to get higher conversion rates until it reaches a point to where it begins to test itself. (Bear with me). So if a few combinations are “starting” to look like good performers, the algorithm might say “OK, you got 5 conversions on 100 plays in X time…let’s see if you can get the same 5 conversions or better for the next 100 plays in the same time period.” If the combination meets or exceeds the mini-test, it moves up to the next “level” where it is now played 3% of the time, where if it doesn’t, and lets say it falls to 4, or 3, then perhaps it stays among all of the other combinations that are tested at 1% play. So the combinations in the experiment keep getting tested in this manner until there are only a few left, where a “winner” prevails. All of this happens very quickly, and in real-time. It’s kind of this survival of the fittest scenario because the combinations that can’t leap into the next levels, actually get played less and less over time, since the ones that are performing are eating up their play time.

Anyway, that’s my take on how it might be working, but again, I didn’t design the tool. My ultimate experiment would be to do identical experiments in Google Website Optimizer and Hiconversion and see if they arrive at the same result. Of course, I can’t be experimenting with all of our clients leads and sales, so maybe I’ll try this on my site one day.

Adaptive Multivariate Testing – Does It Work? is a post from Pear Analytics, an SEO tools and software company.

Amidst the worst financial crisis since the Great Depression, companies are slashing jobs, slowing growth and cutting costs. One of the first costs to get cut are usually in marketing and advertising.

Marketing in a down economy requires you to measure the performance of each campaign.

The problem is the direct correlation between marketing and advertising efforts, and your sales needle. Companies are pressured to cut costs, yet maintain or grow sales, even in down economies. They are going to be forced to do more with less, and be more efficient with spending.  Well, the reality is that you should be doing that anyway and your agency should be helping you achieve better marketing performance.

Are you measuring the results of your marketing and advertising efforts? What if you end up cutting something from the budget that is working? The concern is that companies are widely slashing efforts with high acquisition cost, cutting loyalty and reward programs – all of the things that could be producing the best results.

We recently wrote a post on measuring the lifetime value of the customer, and here is another example of how we can use this kind of data to make these crucial decisions.

In the simplified example above, we look at four different types of marketing channels.  Each has a varying cost per acquisition (or the cost to get a new customer), and we assume that each new customer channel contributes the same net margin per month over varying tenures  (a.k.a. lifetime value of the customer).

If you are not measuring cost per acquisition and lifetime value (LTV), you may be quick to decide to slash the efforts with the highest costs – in this case everything except direct mail.  This is precisely why we like to compare each of these using the Effectiveness Ratio, which is defined as:

Effectiveness Ratio: Value / Acquisition Cost

where Value equals Net Contribution Margin/Mo. times Tenure.

We can quickly see that eliminating paid search would be a detriment to long-term sales.  If we had to cut costs, perhaps we should cut public relations and direct mail in this example.  Also, I am not discounting the sheer number of customers that each channel produces either, hence this is a simple demonstration to point out the importance of marketing measurement, and the types of measurement you should be making.

I’m Not Measuring This Way – How Do We Get Started?

There are some basic data capturing mechanisms you can use to get started on the path to better marketing performance.

1.  Track the source of the sale. Having your front desk attendant asking “how did you hear about us” may not be the most accurate way of measuring leads, so we use software applications to help track the lead all the way to a sale, such as Salesforce.com, or if you want to control the software on your server, you can download a free version of Sugar CRM.  Also, use things such as dedicated 800 numbers or unique website URL’s to segregate promotional offers on print or broadcast channels.  Online advertising continues to be the easiest to track and one of the most cost-efficient channels to producing sales.

2.  Track Spending Per Channel. Track your spend on each campaign effort separately.  Ask your agency to breakdown the costs of direct mail, paid search, and other things rather than sending invoices with general “agency fees”.  Agencies markup media buys, including online buys, so know what they are and how it affects your ROI.  Now that you have your campaign costs and leads broken down into each channel, you can now effectively track ROI and cost per acquisition.  This will factor directly into your net contribution margin and should be subtracted from the revenue earned from sales.

3.  Track Customer Spending – use your CRM system, or if you are a retail organization, your POS system to track customer spending.  Try to uniquely identify the spending by customer ID or email, rather than just by store location or geography.  This gets you to the additional granular detail to further segment your data later by usage and spend, and can do wonders in your lifetime value calculations.  We can also develop patterns to help re-tool marketing efforts by being more relevant and targeted.  From this data is where lifetime value calculations are born.

Are you ready to roll up your sleeves to track the right data and get lean and efficient in your marketing?

Marketing in a Down Economy is a post from Pear Analytics, an SEO tools and software company.

Today we are going to discuss how to measure Cost per Acquisition, which is a fancy way of saying “Cost per Sale”.  If you are like most companies, you probably have several marketing promotions going on across multiple channels. Maybe what you have is some online pay-per-click (PPC), organic search engine optimization (SEO), direct mail and radio.  Good marketing requires that we know and understand what sales are costing us from each channel.

Well, how do you know how much you are going to spend in each marketing channel?

The fact is, most are guessing. In order to properly assess what you are going to spend in each marketing channel, it is necessary to understand what you are willing to spend to acquire a new customer (cost per acquisition), and ultimately, the lifetime value of the customer.

Wait, what is “lifetime value of the customer”?  That is the net dollars a customer is worth to you from the moment they become a customer to the moment they are no longer a customer.  We will talk about this in much more detail in a future blog.

But for now, let’s say that the lifetime net value of a customer is $1,000 so I can illustrate how to use this to back into your cost per acquisition thresh hold. Now, depending on the type of company, margins, and a few other factors, the general rule of thumb is to allocate on average, 15 percent of the customer lifetime value to acquisition cost. This means for this example, we are willing to spend $150 to acquire a new customer from any marketing channel.

How To Measure Cost Per Acquisition

Great! Now, that was the easy part. The hard part is setting up each campaign to be able to track leads and acquisitions by source because we want to make sure we are not exceeding our cost per acquisition thresh hold. This is where everyone falls apart, because it takes process, training, leadership, dedication and the proper tools to do this. We can express cost per acquisition in a fairly simple equation:

You can get as detailed as you want on what “total campaign cost” means to you in terms of labor, graphic design, ad expense, printing, mailing, etc., but the most important thing is that you break it down by individual campaign. Keep in mind that your cost per acquisition may be quite high in the beginning as you front-load all of your set-up fees. Those will get diluted as the campaign starts to generate leads and sales over time.

OK. We’ve determined what campaigns we’re going to run, how much (roughly) we should spend to acquire a new customer ($150) each. How much money should we allocate to each campaign? Honestly, it will be an educated guess until you are tracking leads and sales efficiently to really know the answer to this. But let’s look at a direct mail example.

Direct Mail Example
Many companies purchase mailing lists based on a set criteria for demographic, household income, and some level of intent to purchase. Most direct mail campaigns I’ve done usually yield a 1-5% response rate, and out of those, a 10-30% convert into a sale.  So let’s make some assumptions for illustration purposes:

• List size:  10,000 names
• Total Campaign Cost:  $20,000 (includes list, design, printing, and mailing)
• Response Rate:  3%
• Conversion Rate:  15%

So based on the above response and conversion rates, we would get 300 people to respond to the mailer, and 45 people to buy (this is our Total Acquisitions in the equation above).  Now we know that our cost per acquisition is $20,000/45, or $444.44, which of course is higher than our initial cost per acquisition threshold, so we need to decide if this channel is feasible moving forward.

Pay-Per-Click Example
Pay-per-click is an online ad buying method where you run some ads on search engines, affiliate networks, social sites and other, to drive traffic to a landing page where you hope to “convert” the potential customer.  Results on PPC will vary by industry and competitiveness, but for illustration purposes, let’s assume the following:

• Total Click-Throughs: 2,500
• Total Campaign Cost:  $20,000 (includes set-up, landing page design, ad expenditures, etc.)
• Conversion Rate:  8%

So based on the above response and conversion rates, out of the 2,500 who clicked on our ad to arrive at the landing page,  200 visitors converted to a “sale”.  Now we know that our cost per acquisition is $20,000/200, or $100, which is $50 less than our initial cost per acquisition threshold, so comparatively speaking, the PPC campaign is yielding much better results than our direct mail example with the same investment, so we could take the funds spent on direct mail and re-distribute them to our PPC campaign.

So there is a basic example of how to measure your cost per acquisition.  This gets to be much harder to measure on traditional broadcast channels, so try using unique URL’s or 800 numbers to capture and segregate leads from various channels.

What are you doing to measure cost per acquisition?  How many channels are you marketing across?

How to Measure Cost per Acquisiton is a post from Pear Analytics, an SEO tools and software company.

So often we are concerned with the “big conversion” on the website, like purchasing something, for example.  We call this a macro conversion – it’s your ultimate goal.  But what about other activities, maybe not as valuable, but still worth something.

We forget that marketing is basically broken down into these 3 pieces:

Everyone, including upper management, is zoning in on purchase.  But what about awareness?  Remember the circles of trust graphic?  It’s highly unlikely that many will purchase from you when they don’t know you.

My point:  create micro conversions in the Awareness and Consideration stages and measure them!

Things like entering a zip code, joining a mailing list, or subscribing to your RSS feed.  Now you have a chance to converse with some highly potential, future customers on a permission-based marketing system, versus a interruption marketing system.

Now, assign a value to these micro conversions.  A zip code might be worth $1 to you.  Asking for a zip code is great because it further refines what geo-tracking in Google Analytics can’t do.  Now you know what zip code your visitors are from, so it takes some of the guesswork out of your next direct mail piece.

Use this value to compare to the costs you’ve put into the activity, such as SEO, PPC or even web analytics.  Before long, you will be able to see which activity is driving the most value.

Conversion Value – You Need to Know This is a post from Pear Analytics, an SEO tools and software company.