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Welcome to Josh Baker's Practical Advice for Optimizing Your Internet Marketing blog. Here you will find internet marketing optimization and online strategy articles full of tips, tricks, discussions, and thoughts to help you take your marketing and business to the next level of success.

Archive for Optimization

A marketing test hypothesis is a powerful and necessary part of your marketing optimization program when running tests. I am going to take you through creating a simple hypothesis.

A hypothesis clearly states:

  • What you are testing 
  • What your control and experimental groups are 
  • What outcome you predict will happen (based on an educated judgment) 
  • What is the alternate outcome 
  • What you will need to track specifically in order to prove or disprove your test prediction.

 
Let’s look at a hypothesis more closely.

A hypothesis is clear and specific, testable, and can be proved right or wrong.

Look at these differences between a prediction, a question, and a hypothesis:

A Test Prediction: Not asking for a phone number on my registration form will increase registrations.    
A prediction is the outcome you expect – more or less your educated guess of what will happen.

A Test Question: Will not asking for a phone number on my registration form increase registrations?

A Test Hypothesis: Paid Search traffic reaching my registration form that does not ask for a phone number will  produce more registrations than Paid Search traffic reaching my registration form that asks for a phone number.

A hypothesis states with conviction what results you expect to see from your test, both from your control and your experimental group – it is here that you will state your test prediction.  And since we know that a hypothesis has to be able to be proven either right or wrong, we only have 2 possible outcomes – either my registration form that does not ask for a phone number increases registrations over my control that does ask for it, or it doesn’t produce more registrations(or a tie).


And finally, let’s break down the above marketing test hypothesis to show specifically what it explains:

  • Paid Search traffic reaching my registration form that does not ask for a phone number is the experimental group
  • Produce more registrations is the outcome we expect from the experiment and what we want to track
  • Paid Search traffic reaching my registration form that asks for a phone number is the comparison or control group.

If you wanted to run this test with multiple panels each having a different form field requirements (size), you could in reality replace phone number with less fields:

Paid Search traffic reaching my registration form that has less than 7 fields will produce more registrations than Paid Search traffic reaching my registration form that has 7 fields. ” 

You are still testing your hypothesis of that less fields will produce more registrations than your control of 7 fields, but you will determine from your testing which length is optimal to rollout with if your hypothesis is true.

With your completed hypothesis you can now execute your marketing test to your website visitors and let them prove or disprove it. If they prove your hypothesis to be true then you did a great job with your hypothesis’s prediction, if they disprove it, you still have learned something (make sure you take away lessons from each test!) – The test is not a failure, just try again after formulating a new hypothesis.

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Jan
30

The Other Cost of Cart Abandonment

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How much is your existing ecommerce cart costing you in cart abandonment?  Notice that I didn’t ask you how much revenue you’re potentially losing from those cart abandons.

Take a look at this scenario:

  • Each week 1,000 visitors to your online store place an item or items into your ecommerce cart.
  • You know that from your research each visitor that adds an item to your shopping cart costs you $12 to acquire.
  • You also know from your analytics that your cart abandon rate is 90% (or conversely a 10% conversion rate)

So in this scenario, 900 visitors out of the 1,000 (90%) visitors each week that place an item into your online shopping cart bail and do not purchase, but you have paid for their acquisition anyways. At the $12 per visitor that puts an item into your cart acquisition cost, it’s costing you $10,800 per week ($561,600 per year) for just those visitors that bail out of your cart. How is that so? Continue reading to follow my logic.

So you do some what-if scenarios and believe that with optimization you can get down to an 88% cart abandon rate from your existing 90% abandonment rate (or conversely a 12% conversion rate).

You decide to optimize your shopping cart, and your cart abandonment rate decreases to 87% (better than your prediction of the improved 88% abandonment rate- woohoo!).  Now each week your cart is costing you now only $10,400 per week or $542,880 per year – that’s $18,720 less per year, PLUS the additional revenue of those 1,560 paid sales from your cart optimization efforts.

Ok, so you know just as well as I do that that either way you’re still paying the $12 per visitor that puts an item into your cart. So your actual costs haven’t gone down at all, BUT…

 Why does this matter and how do you really use this information?  

Although your numbers may vary – from the dollar cost of each acquisition that you pay, to the number of visitors that put items into your cart, to your cart abandonment rate. This is purely an exercise in reasoning or a cause for optimization or software upgrading, in other words, an additional metric to prove the value of taking action.

What if your current shopping cart is limiting you to what you can test (or maybe for some reason you can’t perform testing on it) because it’s a third-party application that you have no control over, a legacy cart system that additional programming looks to be costly in time and resources, or some other preventive reason that does not allow you to optimize your cart in the ways that you know you need to in order to increase desired performance.

What if you work for an organization that doesn’t fully believe in the power of testing and optimization? Or, maybe you work for an organization where they are tightening the budget in the current economy and are not interested in investing in optimization.

Your current cart, its current conversion ability and its abandonment rate could be in many ways costing you more in acquisition costs (not including the opportunity costs) than it would cost to fix the problem.

This dollar amount, the cost of cart abandonment, is the cost of leaving your cart as is – the true cost of abandonment of your online shopping cart to you.

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What Is Standard Deviation?

Standard deviation is used to measure the average difference between the individual numbers that make up your data sets’ arithmetic mean (mean being the average or center) and their mean value.

Example 1:

You have 4 numbers; 7,10,6, and 7 again, their mean ((7+10+6+7 =30)/4) = is 7.5, but their standard deviation is 1.5 (I’ll explain later how to calculate the standard deviation). So the average of the numbers is 7.5 plus or minus 1.5. You will usually see it reported as 7.5 +/- 1.5. The addition of the standard deviation number enables you to tell a more complete story of the data spread in the data set allowing viewers to see the average difference between the individual numbers that make up your mean and their mean value.

Example 2:

Below is a more of an extreme example, but it shows you the importance of knowing what the standard deviation is when reporting on an average of a data set:

Now let’s assume you are doing a poll of all 4 people that work in your small office (including yourself) because you want to know the average number of pens everyone in your office has at their desk. The answers you get are 5,7,10, and 200 pens. Their mean (or average) is 55.5, but their standard deviation is 83.4.
By reporting it as the workers in your office have an average of 55.5 plus or minus 83.4 pens really lets the reader of the data know that data set has a large spread.  Although the average is 55.5 pens, it really doesn’t give any insight to the fact that 1 of your coworkers has 200 pens while the rest have less than 10 pens.

What happens if all the numbers in your data set are the same? Well, if all 4 people reported that they had 2 pens each, then your average or mean would be 2 pens with a standard deviation of 0 because all of the numbers that make up your data set are the same as the average number itself, so there is no deviation from the average for the number of pens each individual has.

 

Why you need to understand standard deviation in Marketing and Optimization?

So now you’re thinking, OK, so how is this applicable to Marketing and Optimization?

Let’s say you want to know the Average Order Value (AOV) of a particular channel that sends visitors to your eCommerce site (some people dont like to use standard deviation when working with value, so you could replace AOV with average order size in this example). For simplicities sake (to keep this explanation short- so go along with this) let’s pretend that you have 10 orders (yes this is small, but again to keep this simple) during the period you are viewing – 4 orders at $99, 1 order at $1,499, and 5 orders at $5. Your AOV is $192 (all 10 numbers added up and divided by 10), but the standard deviation is $437.91.

Telling someone that you’re AOV is $192 really does disservice to the real truth of your order values. By stating that your AOV is $192+/- $437 lets the viewer take a step back and realize that there is an order or orders that are spread out far from the mean. Yes, some people will remove the outliers, etc., etc.,  but if you do that, you should provide that as an additional metric alongside the mean and the accompanying standard deviation – but that conversation is better set for a different time (kind of like discussing politics as you will get all sides and opinions).

Can you think of other areas where knowing this in your marketing would really help understand the numbers better? Rarely do you have such small data sets that you want to base decisions on for marketing’s sake as I have presented here for simplicities sake.  But imagine if you had 1,000 numbers to report on that were all over the place with varying spreads? How about comparing performance of various test panel results in your online testing?

The closer the individual numbers are from your mean, the smaller the standard deviation will be. And, the further the individual numbers are from your mean, the larger the standard deviation will be.

How to Calculate Standard Deviation in Excel

In Excel to get to standard deviation is for the most part really simple:

If your data is from a SAMPLE, use this Excel standard deviation formula (a sample is a portion of the total population, i.e. 300 employees chosen randomly, 500 orders chosen randomly):

=STDEV(Cell Range)
 I should note that you don’t want to actually type in Cell Range, but put in the cell range or individual cells that you are your data set: so if you have your data in cells A1, A2, A3, and A4, then you would use the formula =STDEV(A1:A4) to get your standard deviation.

If your data is from a POPULATION, use this Excel standard deviation formula (a population is the total population, i.e. all employees at work, all orders received):

=STDEVP(Cell Range)
 I should note again that you don’t want to actually type in Cell Range, but put in the cell range or individual cells that you are your data set: so if you have your data in cells A1, A2, A3, and A4, then you would use the formula =STDEVP(A1:A4) to get your standard deviation.

Hand Calculating Standard Deviation (not advised for large data sets)

  • 1. Add up all the numbers in your data set to get a total
  • 2. Take that total and divide it by how many numbers you have in your data set (this is your mean)
  • 3. Subtract the difference of each number from the mean and square each difference
  • 4. Add each of the squared differences together
  • 5. Take the sum of the squared differences in step 4 and divide it by how many numbers you have in your data set (this number is your variance)
  • 6. Take the square root of the variance
  • 7. The answer is your standard deviation

Online Standard Deviation Calculator

A nicely done online standard deviation calculator can be found at Math is Fun Standard Deviation Calculator page. Definitely take some time to play with this; well I think its fun at least.

 

So the next time someone gives you some “average” ask them what the standard deviation is and you probably will throw them what they think is a curve ball, but it will give you much clearer insight into what the real info is behind that average. And for your own marketing purposes, it will give you a better understanding of your data and those who need to view it (after of course explaining what standard deviation is and why it’s important!).

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Scenario:

Like clockwork as you do every Monday morning at 10am (after you third cup of coffee and a morning snack) you log into your analytics account to view how much organic traffic is being driven in to your eCommerce website. You’re extremely excited when you see that the number of visits from organic search is growing steadily from last week’s numbers, and the week before, and even the week before that.  Your hard work optimizing your pages for the SERPs is starting to see results and your boss is going to be thrilled.

But wait, although organic traffic is climbing up, up, and away, week over week, you cross-reference your sales data again and notice that you aren’t getting any more orders with all this new organic traffic that you have been receiving. How can this be? What could be going on?

 

Investigation Scenario Tip #1: What keywords are they arriving on?

It’s really important to make sure that you are driving the right search engine traffic to your website. Extract from your analytics account what keywords are driving this newly acquired search engine traffic that you are receiving. To oversimplify, if you’re selling toothpaste and your recent boost of traffic is from searches on arts and crafts paste, you’re driving more traffic, but it’s not the right traffic. 

 

Investigation Scenario Tip #2: What pages are they landing on?

If you notice that many of the keywords that are sending organic traffic in to your site are in fact relevant to what you are selling, then you must dig deeper into your analytics and see what pages visitors are landing on when they are searching on those keywords or phrases. Let’s use the toothpaste example again; you sell toothpaste and your visitors are searching on Google with the end goal of purchasing toothpaste.  You notice that they are landing on the page about how toothpaste is manufactured. A closer look and you notice that the bounce rate is high and reviewing your page there is no obvious way to know that you actually are selling toothpaste on that page.

In this situation you are getting the visitors that you want to sell to (those searching for a product that you do indeed sell), and they are searching for keywords and phrases that are relevant to your business – but you are not giving the visitor what they are looking for or a clear way to get to what they are looking for when they land on your page.

 

In Closing

If either of these two scenarios is happening to you then you will need to work on the optimization of your pages from both an SEO perspective of getting the right traffic, and getting the right traffic to the right pages and from a conversion perspective of keeping them in the continuity of continuing on for what they came in to potential purchase with the least amount of effort and friction.

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Today I received an email from the Web Marketing Association that made me chuckle when it arrived in my inbox and I read what the From Line stated. Slightly immature on my part, but at the same time a good reminder to always review all aspects of your emails. Although this is their proper name and it did get my attention (and as a matter of fact I forwarded the screenshot for others to look at too), Outlook only displayed a portion of the from line for me.

Click on image below to see full size:

Your email from line and why you should view all aspects of your emails.

 
I should note that this was in Microsoft Outlook.

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