Archive for January, 2009
The Other Cost of Cart Abandonment
Posted by: | CommentsHow 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.
Standard Deviation and Marketing – How, Why?
Posted by: | CommentsWhat 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!).
How to Document Your A/B or Multivariate Test
Posted by: | CommentsIt’s imperative to document every effort of your online testing and optimization program. Not only to see the progression of improvement over time, but to also have reference for future tests you are planning or questions that may arise from others you are working with.
How I document my A/B and multivariate testing is as follows:
First, I primarily use Excel for all my documentation efforts due its ease of use.
I create a Master Test spreadsheet that serves as a very top-level summary for quick glancing. This spreadsheet is constructed to have column headers for the following:
- Test name – giving each test a descriptive and unique name
- Test date range – documenting the start and finish date of my tests
- Test hypothesis – why I am running this test and why I believe my outcome will be such
- Results – brief description of the result
Each row is for a single test. I then link the Test Name cell for each test to another spreadsheet that is built specifically for that test (If you have ten tests in the Master Test spreadsheet each test would link to its own Individual Test spreadsheet for a total of 10 spreadsheets plus the Master Test Sheet).
Each Individual Test sheet contains multiple tabs and information, and this is where the detail will go throughout the test.
The first tab contains the test information and summary and broken into sections:
- Test name
- Test date range
- Test hypothesis
- What type of test (a/b, multivariate), and how many panels or combinations
- Traffic data (source of traffic, and current traffic stats)
- What type of metrics I will be using to determine the results and how to determine the metrics (is conversion impressions divided by sales, or impressions divided by clicks on a certain button etc?)
- A space to record final metric results (control performed as such, top performers identified individual performed as such)
- Learning’s (both as the test is live and from the results)
- Ideas for future tests based off of this information
- Next actions (will this be rolled out etc.)
- Miscellaneous notes
I also have other tabs in the Individual Test spreadsheet:
- Screenshot of control
- Screenshots of test panels or combinations (depending on how many there are). If there are too many panels or page combinations to take screenshots of, after the test is ended I take screenshots of the top performing test panels for future comparisons)
- Screenshots of Test statistics (When I am using Google Website Optimizer I take daily screenshots of the stats admins. and store in a separate folder, but the final screenshot from the point at which we end the test is stored in the Individual Test spreadsheet – just in case I transcribe something wrong I have an actual reference to go back to.
- Various other tabs as necessary for reference such as more detailed metrics information, etc.
I also keep a folder for each test (with the folder using the test name) that contains my test spec PowerPoint so that I can see all of the elements or options that we are using, analytics data, screenshots of everything-basically anything used from the conception of the test all the way through to the end.
What this enables me to do is at any point in time have a huge history of each test both from a visual standpoint and data-driven standpoint. The Master Test sheet gives me quick access to the individual tests but also a timeline of the testing I have done.
My Testing Documentation Flow
Using Goals in Google Analytics to Track Conversions
Posted by: | CommentsA simple and free way to track a conversion on your website is to use Google Analytics’ Goals feature.
Scenario:
Imagine that you have just rented an email list from a list broker to promote your latest product. Not only are you interested in knowing how many product sales you make from this email list to your custom landing page, but are also interested in knowing how many people sign up for your e-newsletter after landing on that page. Of course you want to know this so that you can better understand the ROI of this campaign. You could use Google Analytics’ Goals to learn how many landing page visitors also signed-up for your newsletter.
Goals:
A simple non-technical explanation is that Goals count the number of unique pageviews of a specific url that you predetermined during the Goals setup process. When a visitor reaches that predetermined page, Google Analytics increases the numerical count by one for that particular goal. A goal is incremented only once per single visit, so if a visitor during a single visit reaches that predetermined page more than once, the goal count is only incremented one time so as to not inflate your goal count.
During the Goal setup process, you can also specify the urls of pages prior to your Goal page. What this does is enable you to track visitors who go through a specific first page (by selecting the required step checkbox during setup) before they go through a certain set of predefined pages to reach your goal page, commonly known as a Funnel.
An oversimplified but useful explanation of how to use a goal would be if you wanted to learn how many visitors that went through a specific page (let’s call this page.html) went on to sign-up for your email newsletter on a different page. If after they signed up for your newsletter they were redirected to a thank-you-for-registering page or confirmation page, you would set your Goal page as that thank you page (for this example, this page only being reached by filling out the registration form), and your required step first url as page.html that we referred to above. By configuring a Goal like this, you could see how many unique visitors that go through page.html continue on and sign-up for your newsletter.
Now there is a little more to the planning, set-up and preparation than mentioned above in order to help filter out some of the noise to get closer to the true data, but overall set-up of Google Analytics Goals is really a snap. Currently, Google Analytics only allows you to have 4 goals per profile set-up at one time which means you have to plan accordingly.
Tips to Analyzing Poor SEO eCommerce Conversions
Posted by: | CommentsScenario:
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.
