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we're gonna focus on how to run tests on , your marketing emails to get results , that you can measure every email tests , you run should have a strong purpose , behind it each time you decide to test , an email ask yourself the following , questions why am running this test and , what am i hoping to get out of it by , testing or emails you're focusing your , email marketing efforts around data , driven analysis which gives you the next , steps for improving the next send let's , explore how to test your emails to , identify the right next steps to , continue to send emails that will , provide value to your contacts before , diving into the steps you're going to be , using let's first talk about AB testing , what is it and how can you use it to , test your marketing emails a B testing , is the inbound answer to a controlled , experiment it's defined as a method of , comparing two versions of a pagan , app or an email to determine which one , performs better in this case we're , comparing two versions of an email you , could use an a/b test to pinpoint , specific variations of your email and , focus on how to improve that asset which , allows you to focus on data-driven , analysis instead of guessing game , most email marketing tools will have a , specific feature that allows you to a/b , test assets you can also run an a/Brest , on your own without these tools an a/b , test allows you to test variations of , your email alongside one another then , you can review the results to see which , one performed better to get the data to , back up future decisions on your email , signs now that you have an idea of what , a/b testing is let's move on to the , steps you'll take to run tests on your , marketing emails the first step is to , define the goal and purpose of your test , second evaluate the segments of , recipients you're sending to third , design your test , and lastly review and start your test , these are your steps to get you started , on developing test for your marketing , emails you will analyze and report on , these results as well but first we need , to focus on creating the tests when , you'rerunning a test on an email all , you want to focus on is one element that , you'retesting the subject line the body , content or the CTA you are using think , of the test you are running as , experiments where you want to control , and available with this in mind you can , take your first step in developing the , test of your marketing emails the first , step in any inbound strategy is defining , the purpose for doing something fear , testing just test you won't discover , results that give you actual steps to , help you improve while testing your , marketing emails consistently will help , you improve over time keep in mind that , doing something just to do something , will not provide valuable results nor , provide value to those receiving your , email stake a look at how your emails , are performing and decide what you want , to improve maybe specific type of , email you're sending is not yielding the , results you wan tor maybe you're going , through a subcontinent want to test , different colors our logos whatever it , is make sure you have a purpose before , setting out to run a test when setting , this goal for your email test you'll , also be preparing to design your email , test later on take for example looking , at the email elements you can test which , elements can affect open rate sit could , be a few things such as the number of , emails you send to a list the subject , line and the preview text and which , elements can affect click-through rates , the email body copy the body design , layout the body images the Canad email , signature these elements can give you a , starting point for focusing your goal , and purpose from here you can see what's , working Welland what's not to draft a , hypothesis of what you want to test and , thus improve now that you have a goal , and purpose for your test you'll need to , evaluate this segment of recipients , you're sending- you can't run an a/b , test on your email unless it goes to , someone when you'retesting an email you , need minimum amount of recipients to , make the test conclusive this is where , statistical significance comes in , testing significance involves doing some , math to determine the number of people , you want on your email list in order to , run a test if you send an email to five , people to try and test new subject , you might send three out of those five , people the updated subject line and , while they might all love it you won't , be able to confidently say that the rest , of your contacts will you need more , people for the results to be , statistically significant so how do you , know how many people to run a test with , hub spots a B testing tool for example , requires to have at leas ta thousand , contacts on your list to run a test this , is the total number of contacts you wish , to send a specific email to to run your , test you will need to determine , percentage or sample size from that , thousand contacts to send your , variations or versions of email to you , will have your version a which can be , your control the typical email you would , send and then you have your version , the one in which has a variation made to , it whether this is a change to your , subject line body text or other element , if you are testing Indra thousand , contacts you could run a W/W test for , your email send where% get virgin a , and the other W%get firs an dB let's , say though you do have a thousand or , more contacts that you want to send to , you will now need to determine the , sample size that will yield conclusive , results if you are using a tool like , Hub Spot then the tool can help make this , calculation for you you will select the , percentage you wish to send each , variation and the number will be set but , you can also determine that sample size , using a significance test calculator , this will give you the number for each , sample size that will yield conclusive , results this calculator will help you , determine the number of people that will , receive each version of the email the , control and the variation let's walk , through an example together you can see , here on this sample size calculator , there are few different options you , will need to fill out the confidence , level interval and the population and , then finally it'll produce as ample size , let's begin with the population the , population is the total number of , contacts you want to send your email to , for example a thousand contacts you can , get an estimate of this number by , looking at the last four or five emails , you have said , and how many people you sent Otto once , you have your population you will have , to set a confidence interval you might , have also heard this called margin of , error lots of surveys use it this is the , range of results you can expect once the , test has run with the full population , and lastly , you need to look at the confidence level , this tells you how sure you can be that , your sample results lie within the , confidence interval the lower the , percentage the less sure you can be , about the results the higher the , percentage the more people you'll need , in your sample size to test for example , inhibit the a/b testing tool use's , percent confidence level to determine a , winner in a tool like this you can , choose percent as a base now let's , apply these values to see what we get we , have our list of a thousand contacts and , we want to be W% confident our winning , email version falls within five-point , interval of our population metrics , here's what we put into the tool , population,W confidence level W% , confidence interval five and this would , produce as ample size of this would , mean that people get version a and , another W get version B each segment , would receive one of these versions then , you'd be able to see which version , performed better , for example version B with your , variation and then send that version to , the rest of the contacts from your , original list who did not receive a , variation an a/b testing tool can help , you do this automatically but you can , also implement your a B test by creating , your different segments once you know , each of the sample sizes that you'll , need now that you know the purpose and , the goal of your email test and you know , the number of recipients you need to , make your test produce results you can , move onto designing the actual test the , design will relate heavily to your , purpose or goal like other aspects of , your inbound strategy your goal is tied , directly tithe content purpose or , outcome you're producing when you set , your goal you'vie identified areas in , your , now that need improving now it's time to , take it a step further and figure out , ways to improve them an important aspect , of testing is to make sure what you're , proposing is feasible if you don't want , anyone to unsubscribe from your emails , don't send any emails great experiment , right not so much when you're , hypothesizing be creative but also keep , your ideas within the boundaries of , reality you want to explore tests that , will provide long-term results for your , business let's look at an example of a , hypothesis and what type of test you , might design in this example when , setting the purpose of your test let's , say you identified that your newsletter , emails are not getting the open rates , you'd like and you want to Fonda , solution by running test to see how , you can improve them your goals to , improve email newsletter open rates from , W% to% during a business quarter , your hypothesis is that the subject line , contains characters or words that are , triggering the recipients spam filters , to test this hypothesis you can design a , test to adjust the subject lines to , avoid exclamation mark sand percentage , sign sand remove sales words like free , and discount you want to aim too closely , line the subject line with what the , email contain sand you'll test if , applying these best practices improves , your open rate another hypothesis and , solution for your low open rates is you , send too many emails so your contacts , are less compelled to open them and you , can design a test to try to reduce your , email frequency for at least one month , and observe if email open rates improve , this is how you can tie your goal to the , design of your test to start to measure , and improve your email signs lastly , you'll review and start the test this is , an important step because it means , deciding how long you want to run your , test for there is no magic number no , perfect time of the week or even day of , the month to run your tests but you want , to run your test long enough to make , sure enough of your contacts have had , time to interact with the content some , email AB testing tool swill have you , set atom frame for the test and at the , end of that time period , the tool will choose a winning email to , send to the rest of your contacts this , is why timing can be so important your a , B test might not be significant after an , hour or even F after hours to decide , on this time frame you can take a look at , your past performance remember you want , to focus on data-driven analysis not , guesswork one of the most common , mistakes people make is ending a test , too soon and this dozen'just mean the , one a B test make sure you're testing , many emails to start to see how things , are trending before making an overall , change the way you send email maybe you , choose to test a few different elements , over multiple email sign sand multiple , months analyzing these metrics will help , you decide on what you want to adjust , for the time being but for a single , email son the time is still important , take a look at past email opens and , clicks and see where things started to , drop off for example what percentage of , total clicks did your email get during , its first day if you found that it got , W% of clicks in the first hours and , then% each day after that it makes , sense to cap your email a be testing , timing window to W hours because it , would'tube worth delaying your results , just to gather Little bit of extra , data if you use an email platform that , has an a/b testing tool then it'll , determine statistically significant , winner if not you can determine the , winner yourself by calculating the , conversion rates of the two types of , emails but what happens if your test , fails what if neither version performs , better than the other or it's too close , to actually determine significance if , neither variation produced statistically , significant results your test was , inconclusive that is okay this is why , testing is important not every test will , produce results for you to take action , on immediately , this might mean adjusting your goal or , looking at the numbers you want to move , but most importantly don'tube afraid to , test and test again after all repeated , efforts can only help you improve this , is where you can start to see , how these tests are performing you might , decide to run the tests multiple times , to determine what you want to change , these are your steps for outlining the , test you want to run on your marketing , emails define the goal and purpose of , your test evaluate the segments of , recipients you're sending to designing , your test and review and start your test , testing is a great way to see how your , contacts are engaging or not engaging , with your marketing emails and by , following these steps you'll continue to , prove your ability to do data-driven , analysis for your business
