LinkedIn Ads testing and strategy pivots - Ep 57

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Chris Dayley

AJ Wilcox

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Show Transcript

AJ Wilcox You're running and testing your LinkedIn Ads. But how do you know when your test is complete? When something isn't working? How do you know when it's time to pivot? We're covering deep testing strategy on this week's episode of the LinkedIn Ads Show.

Welcome to the LinkedIn Ads Show. Here's your host, AJ Wilcox.

AJ Wilcox Hey there LinkedIn Ads fanatics. So we've all been told that we need to always be testing with our ads. Well, sometimes it can be hard to know when our tests are conclusive. Or when it's time to move on to a new test, or even what do we need to be testing? Well, if you test too long, you end up missing opportunities for more learnings. And if you test too short, you risk coming to the wrong conclusion, which can really be costly on your future performance. So this week, we're gonna dive deep, we're going to talk about the different types of tests that you can run, and how to tell when they're complete. Make sure to listen to the end, because I'm going to be sharing my methodology for deciding which tests to run next, after you found conclusive results from your previous test. So first off in the news, I got a chance to talk to a friend who's part of a really cool beta for LinkedIn right now. It's called the audience insights beta. And essentially, what it is, is a really granular breakdown of the audience makeup of the attributes from a matched audience. You can think of it as a really helpful analysis of your target audience, as well as a great tool for understanding the ways that LinkedIn targets better. The way that it works is you'll go into your matched audiences section, and you'll select one of those audiences, then this can be any sort of a matched audience, it could be a website retargeting audience, or anyone who's submitted a form, or anyone who's visited your company page, you get the idea. Then you click a button that says, generate insights and it will open up a dashboard about that audience. And what you get here is a whole bunch of different facets and breakdowns of what makes up your audience. It'll show you your existing audience size. And it will tell you how many of those people fit into different categories. There's interests, so this is where you can find out which interests that your target audience are tagged with. And this can help you with your interest targeting, determining whether to use it, or how many or which types of interests to use. As a side note, I hardly ever use interest targeting because it's such a black box. But now with this, I actually feel a lot more comfortable in finding and using interest targeting. There's organic content, so you can see the trending content that is most engaging to this exact audience. You can see the location, and this is the profile location of where members of that audience are located. There's demographics, there's education, there's job experience. And this gets really exciting because it'll show you the seniority breakdown of your target audience, your job functions that fit within them, your years of experience, and even more. And as you probably know, when you're building a campaign over in the right rail, we get a little bit of an audience size breakdown, but this is really that on steroids. It's a supercharged version of audience Insights. And then as you're exploring here, it's really quick to create a campaign based off of the targeting you're exploring, which is pretty cool. When this feature sees full general audience release, we will definitely let you know more about it. But for right now, I wanted to give you a quick heads up on what's likely coming and how excited we are about it.

AJ Wilcox 3:36 Okay, on to the testing topic. Let's hit it. So first off what is pivoting? You may have heard the Silicon Valley term to pivot. A business needs to pivot. When a business doesn't have product market fit, companies can pivot or adjust their strategies to find the right fit. You've probably also heard the axiom of fail fast, and that originates from Silicon Valley as well. And the concept is that by taking too much time doing the wrong thing, or a less effective thing, you risk so much more than if you were to just make a quick painful one time adjustment and get to that product market fit much quicker. The same risks are present in our ad testing. If you're testing two different ad concepts against each other to the same offer, but that offer is bad, what you're doing is you're wasting weeks of good potential performance that you could have from running a better offer. So definitely, we should always be testing something. And to be clear, not every test will be exactly what you want. Some tests will fail and others will win. And some will just be inconclusive, or some will teach you something but it's just not important. So pivoting is essentially knowing when something needs to be changed, or when to conclude your current test. You can pivot because something is working. You can pivot because something's not working. Or you can pivot just because it's time to want to test or try something new.

AJ Wilcox 5:00 So we're going to do something we haven't done before on the podcast, I'm going to bring on a guest for explaining a certain topic. So please welcome Chris Dayley, CEO of smart CRO, who's going to explain the concept of scientific testing and statistical significance. Alright, we're doing something that we haven't done here on the podcast before, I got to bring in my friend Chris, who is a conversion optimization expert. A longtime friend, partner we met, it's probably been 11 or 12 years ago, maybe even more than that, where we're both doing SEO at the time. And anyway, this is Chris Daly, who runs smart CRO. And, Chris, I brought you on because we're going to be talking a little bit about statistical significance and obviously, this gets into the stats side and the math side of marketing, where many marketers who may have come from the more creative side may not have experience. So first of all, tell us about yourself. And then I'll ask you more of the meaty questions.

Chris Dayley 6:00 First of all, thanks so much for having me on the show, man. You know, I'm one of your biggest fans and so I feel flattered to be on the show. And, you know, like you said, I've got, you know, more than a decade of background in digital marketing, I pivoted to conversion rate optimization about 10 years ago. And I've been running a conversion rate optimization agency for the last eight years, I think it'd be a fun fact, AJ, and I actually started our agencies like within a week of each other. And I called AJ, because I wanted to pitch a company that he was working at. And he's like, Oh, I'm actually not there anymore, I started an agency. And I was like, me too. Cool. So yeah, I've been doing version optimization for about the last eight years. And actually, I hated statistics when I took statistics in college. Probably one of the reasons I'm dropping out of college. But since I started doing conversion optimization, I've actually really fallen in love with a lot of the statistics that because of how applicable it is, and I'm excited to dig into this stuff with you.

AJ Wilcox 7:01 So cool. Well, and the reason why I brought you on, Chris, I mean, every time I'm talking about statistical significance, or anything stats related, it's always parroting something I've heard on one of your, I think I've probably listened to 80 or more podcasts that you've been a guest on, and I've gotten to hear you speak at so many different conferences, and I'm basically just parroting stuff that I've heard from you. So I wanted to bring you on to ask these questions. Because I mean, why parrot what someone else said, why not just go right to the source? So tell us, first of all, what is statistical significance? What's the definition? And I guess why it matters?

Chris Dayley 7:35 Yeah. So well, let me first say why it matters. So anytime you are measuring data, right, like when you're running ads, for example, and you see that one ad has a 50% conversion rate, and the other one has a 10% conversion rate. There's all sorts of questions that come to mind, once you hear that this one has a better conversion rate than the other one. You know, most marketers would want to know is, well, how reliable is that? How much data do you actually have? Are you talking about, you got 10 clicks on both of them, and one of them had five conversions and the other one had two, because that's not a very big data set. And so which makes that data not super reliable, or in other words, there's a huge risk or chance that's involved in saying that one thing is a winner and one thing is a loser when you have such a small data set. And so statistical significance is really a it's a statistical calculation of how confident you are that your results are not due to just random chance, right? Because, again, if you have 10 clicks on two different ads, and one of them has five conversions, and one of them has two, they obviously have a super, super different conversion rate there. But there's a huge likelihood that you might have just had like two people on the first ad that were awesome. And they could have landed on either ad and converted. And so you're not really sure if it's due to the ad, or just due to the fact that a couple of qualified people saw those ads. So anyways, the reason that statistical significance matters is you need to know with certainty that when you say an ad, or in my case, if you say that a variation of a landing page is better, you need to be pretty confident that that result will hold true because there's all sorts of risks that's involved. If you assume that the ad that got five clicks is better than the ad that got two clicks. And you start basing all of your marketing around that first ad, like let's say that that first ad had a video and the second one had an image, if you base all of your future ads off of the fact that you think a video worked better. But it turns out that actually if you had run that test for longer, the image would have performed better. You're going to really screw yourself over in the long run, you're going to end up operating under false assumptions. And so, again, statistical significance is just way to with confidence say that what you think is a winner is actually a winner.

AJ Wilcox 10:05 Oh, yeah. Alright, so one thing I've heard you talk about you like to determine your statistical significance to that 95% confidence? Do they call it a confidence interval? I forget what it's called.

Chris Dayley 10:16 Yeah, confidence interval or P value or whatever you want to call it. There's lots of different terms for it. But yes.

AJ Wilcox 10:23 So why do you run your test to a 95% significance level? Other cases in marketing where you'd suggest a 90% or an 80%? Or do you recommend the 95 for all of us?

Chris Dayley 10:34 Yeah, that's a good question. And let me break that down into a couple things. 95% statistical confidence means that you're 95% certain that this winner is actually a winner, right? And the reason that I like using 95%, as sort of a minimum threshold is obviously 100% would be ideal, right? To be 100% certain, but it usually takes a lot of traffic or a lot of data to get to 100% statistical confidence, unless you have a huge difference in numbers, right? Like, if you have 10,000 visitors that saw one ad, and you have 10 clicks, and you have 10,000 visitors that saw another ad, and you have 1,000 clicks, you'll have 100% statistical significance, because the difference, the discrepancy is massive. But again, if you're testing ads, for most datasets, you're going to end up with like, you know, 10,000 views and 500 clicks and 10,000 views, and 550 clicks. And, yes, the second ad had 50 more clicks, but there's only a 10% difference. And so it's gonna take a lot more data to know ,okay, was that for real? Was there something that fluency not variation? Or if you keep running for long enough, are they just going to even out? So 95%, it's a high enough confidence that there's still a very low chance of calling something a winner, that's not a winner. So there's only a 5% chance that if you say this is a winner, it's only a 5% chance that you're wrong. Right, which is, there's still a chance and it'd be great if there was zero chance, but I mean, my philosophy has always been if you're testing enough, like, if you are constantly running AB tests, on ads, or whatever, yes, maybe 5% of the wins that you called were false positives. But if you run enough tests, you're gonna end up with 95 winners, true winners, and maybe five that weren't true winners. But overall, by and large, you have a very, very high win rate there, right? That's the first thing. A 95%. For me, it's high enough that I feel confident, but it's not so high that it's impossible to reach. 100%, I view as very unlikely to get 100%. So the second part of your question is do you have to go with a 95% statistical significance. And I say no to that, I don't always run tests until I get a 95% and here's why. The closer the data is, so again, if you have 500 conversions on one, and 530 on another, you could be stuck at like an 85, or an 80% statistical significance. You might be stuck there for weeks, because there's lots of things that may happen. And one variation might get a few more conversions one day, which is going to decrease your static and then the next day, you might have a lot more conversions on the version, which is gonna increase your stats and so the statistical significance number is going to fluctuate over time. So I usually pair in or I add in a second rule, it's like my backup rule. So I like to shoot for 95% statistical significance. But if I end up with a variation that has been winning consistently for a period of two weeks, and I still don't have a 95% stat sig, then I will still call it a winner. Because even though you know, I might have an 85% stat sig. If I have a winner that has been consistently performing well, then I will use that longevity of data to sort of support okay, yes, I only have an 80% stat sig here. So there's a 20% chance I might not be calling a winner, but the data looks pretty reliable. Right? Like the test is being consistent. 95% If I can get it, and and if not, do I have consistent performance?

AJ Wilcox 14:23 Oh, that's great. All right. So question for you then. What I love about testing to statistical significance, is we as marketers aren't shooting from the hip. We're not just gut checking all of our marketing, because that can obviously lead you down pretty bad roads. I know a lot of marketers do, but I don't recommend it. It allows us to approach this scientifically and actually be certain that you're learning stuff along the way. But how then do you know when you've reached statistical significance, because the LinkedIn ads platform isn't going to tell you, you don't get to register your AB test anywhere and have it monitoring? What tools do you use or what would you make available to yourself to watch this and grade your AB tests?

Chris Dayley 15:01 Yeah, good question. There's lots of free tools. I mean, if you Google statistical significance calculator, there's tons of free calculators that you can use out there. I was showing you before this call that I've actually just developed my own inside of a Google Sheet, where I just use an API by just pulling all of the raw data from Google Analytics. And then I calculate my own statistical significance. Even though the tools that I use, do calculate it for me, I still like to have my own statistical significance calculations. You can grab tools online, and if you have a way of plugging in the raw data from LinkedIn, then you can calculate it. You can also just go in and like, you know, for example, Neil Patel on whether you like Neil Patel or not, he's got a free tool on his site, that you can just plug in the number of visitors or the number of, you know, like, if it's an ad, the number of impressions you have, and the number of clicks you have, or the number of clicks you have, and the number of conversions you had, or the number of impressions, you have, whatever, but you're going to plug in the number of "traffic", and then the number of conversions for each of your variations, and then it will give you a statistical significance calculation. So I mean, like I said, free tools, easy place to start, if you're not calculating statistical significance now, just go and grab the data from two of your ads and pop them into one of these tools. And it will calculate the statistical significance for you. The one other thing that I'll just say, say, you know, you'd mentioned that like, it's easy to shoot from the hip as a marketer. And statistical significance is a great way of ensuring you're not doing that. It also ensures and it also helps to put some checks in place so that you don't call tests too quickly. Because I know whether you are an in house marketer, or if you are an agency marketer, you always want to show your boss or your client, like you want to show them when these you want to show them wins as quickly as you can. And you want to mitigate the risk, you don't want to be running a test that is losing money for your company or your client for very long. And so the reason that I see people end tests too quickly, is because they're like, Yeah, but if that variation continues to perform that way, it's going to lose us a lot of money, or the opportunity cost is so high, because I could be generating so many more conversions from this other ad. And so statistical significance is a good way of like putting a check in place for yourself. So I always tell my clients, we're at least gonna run tests for a minimum of a week. Even if we see something just like blowing this other variation out of the water, we're still gonna give it a week, because things can change in a few days. And so you want to run experiments for long enough that you see some historical data in there. And the static will help with that.

AJ Wilcox 17:43 What was so shocking to me when we were talking, this has been years and years ago, but you were showing me one of your tests for a giant enterprise company. And you were showing an AB test. And we were looking at this graph over time, and we could see that by like day five of your test, variation B had statistical significance, it was the winner by like 30%, or something high. And then it may not sound high to you, I know you get higher. But then you showed me the continuation of that graph. As the test kept going into week two, all of a sudden, variation, a took over with, again, statistical significance, and it was winning, and then it reverted back to B. So what I love about what you're saying is run the test for long enough, but realizing that stats can be misleading just because human behavior can change. But we really should be, I guess, tracking things that will stand the test of time, as well as just fitting our statistical significance.

Chris Dayley 18:38 And I would say don't even calculate statistical significance until you have at least a week's worth of data. Because if you calculate stat sig on day one of a test, I almost guarantee, you'll get a calculation that says you have 100% statistical significance, because it's gonna be like, Hey, we have five conversions on this one, and none on this other one that will give you a 100% statistical significance. But it's such a small data set, it would be stupid to like call a winner with that small of a data set. So like I said, I don't even look at static until at least a weekend, because it really doesn't mean anything until then.

AJ Wilcox 19:16 Yeah, and especially on a platform like LinkedIn, where every day is a little bit different. I know that a weekend day performs very different from a Monday, and I know the difference between a Monday and a Tuesday. They're closeish, but they're very different. And then you have the difference between a Friday, totally different. So you don't want to run for a partial week, especially to the LinkedIn audiences, when every one of those days has a little bit different of a personality. Love the idea of running for at least a week love the idea of two weeks, so you have to have each kind of day. And I love the idea of making sure that you're running whole days. You didn't start your test mid day one day.

Chris Dayley Yep, absolutely.

AJ Wilcox 19:55 All right. So kind of a fun little announcement here. Chris and I were talking before the call about creating a joint tool that we can then share with this audience. So make sure that down in the show notes, you'll see the link to both of our LinkedIn profiles. Make sure you're following us. So you'll get the free tool when we release it. We don't know how long it's gonna take, I have a crazy idea in my mind that I don't even know if it's possible. But whatever we come out with, I know it's gonna be cool. But Chris, where can people find you? Where can they follow you? Where do you put your stuff out? How do they get in touch with you? Just take us wherever you want us?

Chris Dayley 20:26 Yeah, so the only social media platforms I'm on is LinkedIn and Twitter. So you can find me on Twitter, it's @ChrisDayley. Last name is D A Y L E Y. Or you can find me on LinkedIn. I'm not on Facebook, not on Instagram. And then my company website is smart-cro.com. You know, and again, I focus on website and landing page AB testing. And so if you're wanting to go from testing your ads to testing your landing page, your website, that's definitely something I'd be happy to chat with anybody about.

AJ Wilcox 21:00 Awesome, Chris, thanks so much for enlightening us, we'd love to have you back on the show. At some point when I can think of a something else that we need your commentary on. But thanks again for for just being willing to come on and sharing your abundant knowledge.

Chris Dayley 21:12 I will talk to you anytime you want to talk to AJ. So thanks for having me on the show.

AJ Wilcox 21:15 All right party on.

AJ Wilcox 21:17 So Chris, and I talked about different tools for calculating stat sig. In the show notes, you'll see a couple links to some tools that we've used to calculate that you can try out. And by way of instruction, here's how you'll use them. So what you'll see is an A and a B. And there's essentially a box for before and a box for after that you fill in. And this can be kind of confusing, but what you'll do, if you want to test the statistical significance of the click through rates on two different ads, what you'll do is in the top box, for your ad, a variation, you'll put in the number of clicks. And the bottom box, you'll put in the number of impressions that ad a received, then the same thing for ad B. In the top box, you put in the number of clicks, which is the number of results. And on the bottom, the number of impressions. So the number that it's out of. If you want to test conversions between two offers, it's the same type of thing, it's just in the top box, you're going to put in the number of conversions or leads. And in the bottom box, you're gonna put in the number of clicks, that's going to show you your winner. And the statistical significance. If there is some between the conversion rates, you could take this way further, if you have enough data on, let's say, sales, qualified leads or proposals sent, you could put the same thing in the number of those results with the number of leads or whatever it is you want underneath. Okay, so now you know how to use these tools, go check them out, go try them, and evaluate some of the tests that you're running. So I guess my first question is, how do you know when you have enough data to actually make a decision about your tests? LinkedIn has a section on their website in their help section that we've linked to in the show notes, so you can go read it. But basically, they say, you want to always be testing, which we definitely agree with. LinkedIn says every one to two weeks, pause the ad with the lowest engagement, and replace it with new ad creative. Over time, this will improve your ad relevance score, based on indicators that LinkedIn members find that that ad is interesting, such as clicks, comments and shares, which will help you win more bids. Since bid actually means something important when they say, which will help you win more bids. I think what they're probably trying to say is, which will help you win more auctions. But we'll let them make that clarification. LinkedIn also recommends include two to four ads in each campaign because campaigns with more ads usually reach more people in your target audience, I would disagree with the majority of that advice. What we found is that the learning phase when you launch ads, usually lasts about one to one and a half days. So if you have ads with really poor engagement, after let's say, your first two days, it's usually pretty safe to say, there's something wrong with these ads, we can take action now by pausing them and taking them off the table. That being said, even if click through rates really aren't great. Sometimes we'll keep them running just so that we can suss out the conversion rates because obviously, getting leads and getting a good cost per lead is way more important than the amount of engagement that an ad gets. But of course, we always do want good click through rates whenever we can. I'm also not in a hurry to pause the low engagement ads, since we're always using LinkedIn's option of optimizing the ads in the campaign to those that have the highest click through rate because that's going to send almost all of the impressions to the higher performing one anyway. So having another ad in there, that's just kind of dead weight. It's getting ignored anyway, so I'm not in a huge hurry, but its okay if you want to. We've talked about this before on the show, but I don't recommend including more than two ads per campaign. Since what it does is it it dilutes your AB test. If you're running an ABCD test, but your ad A gets 60% of the impressions and ad B gets 30%. And the last 10% are split between C and D. That doesn't make for a very good test with a lot of data, we would ideally want a lot more data spread around all of those variations. I get it LinkedIn asks us to put more ads in a campaign because it breaks the frequency caps and allows your ads to be shown more often, which will get you to spend more money. But I care a lot more about the performance of ads getting good performance than just spending all of my budget usually. Okay, here's a quick sponsor break. And then we'll dive into what you should watch for to evaluate your tests.

The LinkedIn Ads Show is proudly brought to you by B2Linked.com, the LinkedIn Ads experts.

AJ Wilcox 25:56 if the performance of your LinkedIn Ads is important to you B2Linked is the agency you'll want to work with. We've spent over $150 million on LinkedIn Ads, and no one outperforms us on getting you the lowest cost per lead and the most scale. We're official LinkedIn partners and you'll deal only with LinkedIn Ads experts from day one. Fill out the contact form on any page of B2Linked.com to chat about your campaigns, we'd absolutely love to chat with you.

AJ Wilcox 26:22 Alright, let's jump into what to watch for in your tests. First of all, you want to set your threshold. You want to decide what the parameters of your test are going to be. One parameter you could set is say I'm going to run this test for a certain amount of weeks or months or days, we heard Chris talk about how he wants to run for at least a full week. And with LinkedIn specifically, I would suggest running for at least two full weeks, you do also want to make sure that you are working from whole days, which means you'll want to start your test as close to midnight in the UTC timezone as possible. And then finish it around UTC midnight whenever you're finishing the test. But of course, if you see that the results are crazy different, like you have two offers, where after a week and a half, one of them is converting at 40%. And the other is converting at 6%. You don't have to finish the rest of your time period test as long as the data is there. And you can tell yes, definitively, this offer A that's converting at 40% is way better, you can determine your winner a little bit sooner. Another parameter you could set for your test is say we're going to allocate a certain amount of budget towards this, you can say 3000 Euro is going towards this test. We see a lot of marketers do this because their bosses give them a certain amount and they have to apportion it out and budget it across different things that they want to learn. This is certainly possible, but just make sure that by the time you're done spending that budget, you are running a statistical significance calculator across it to make sure that the results that you got can actually be trusted. Another way that you can set a parameter here is saying how much data you want to generate. So you might say, we want to run this test until we have 120 leads, or 400 clicks or anything like that. Again, you just want to make sure that the parameter you set here for the amount of data you want, is actually enough to make a difference. You may also set a threshold of stat sig between two ad variations on the click through rate level. And that's going to come pretty fast actually, because what you're doing is you're showing clicks compared to impressions across two different ad variations. And you could get that statistical significance quite quickly. You could take that a step further and run a test based on statistical significance at the conversion rate level. So now you're seeing which offer converts better. With even more data, you could do the same thing, statistical significance based off of which ad or which offer gets the highest number of marketing qualified leads. Another step further based off of sales, qualified leads, or proposals or closed deals. Now, if you want statistical significance between two ad variations or two offers all the way to the close deal, you will need to be spending a lot of money, this is in the millions per month in order to get here or you have to have been spending for years. I just want to level set you just in case you're thinking that sounds really fun. But if you're spending, you know $5k a month or something that's probably not realistic, I would stick more to like statistical significance at the conversion rate level. A lot of times we'll end up running pretty much the same ad variations, the same AB test across a lot of different campaigns. And so rather than trying to achieve statistical significance, within each one of those campaigns are we're looking at a small number of clicks and a small number of impressions. Instead, with a simple pivot table in Excel, we can combine the performance of all of those ads that were ad A and all of the ads in the account that are ad B add them all together. And then we're going to achieve our statistical significance so much faster. You can do the same thing with your costs per click. Measure which ads or which offers get a better cost per click. This obviously doesn't mean nearly as much as your leads, or close business does, but it is something you can test. Generally, the ads with the higher click through rates are going to get the lower cost per click. But if you're spending enough, something really good to test is your conversion rates. Which ad gets a higher conversion rate? Which ad variation gets a higher conversion rate? Which offers get a higher conversion rate? Which audiences get a higher conversion rate? These are all things that you can test again the same way with static, if you're getting data back from your sales team on lead quality, or if you have a lead scoring algorithm set up, you can judge your tests based off of lead quality or traffic quality that's coming from a certain audience. Then if one of your audiences is producing a higher lead quality, then you'll know that you can adjust your audience. Use more of the targeting that's winning less of the targeting that's bringing in the crappy quality. One word of warning here, though, is that with any social advertising, one issue that we're always going to face is ad saturation, which means changing performance over time. If you try to run the same test, and you run it for two months, chances are at the beginning of that two months, performance will look pretty good. But then about halfway through the test, you'll see performance falling, and then by the end, it might be abysmal. So if you try to lump those two months of performance together, you're going to get something that looks pretty average or maybe even bad. But what you didn't know is the first two weeks or the first month that it ran, it was really good. And you should want to do more of that. As a general rule of thumb, I found that your ads or your offers will saturate after usually about 28 to 33 days. But how do you know? Well, I like to go into the performance chart and look at campaign performance since the day of launch. And I like to look at click through rates over time, as the same people tend to be seeing your ads over and over and over, or they're exposed to the same offers, every time they're on LinkedIn, they're going to be much less likely to click over time and you'll see those click through rates drop. So with your tests, make sure that you're changing things up enough, or you're starting new tests, before your last test fully saturates and you watch performance drop over time. Sometimes I'll be running a test, and I stop the test not because it's finished, or I've achieved stat sig, it's because there's something else that is a higher priority thing that I want to learn. And I think that's just fine. If the opportunity cost of waiting for a test to finish is higher than the upside of what you're going to get out of learning something from the new test. Don't be afraid to either nix it or put that test on pause. And what you should know is, there are different kinds of tests that you can do. Some are easy, some are hard. But any test that we do that's closer to the money is going to teach us something more valuable. What I mean by that is testing things like ad copy. Sure, you can improve results by 5 to 15%, with different ads and different imagery. But by changing the offer, you can double, triple quadruple your results. By working with and coaching your sales team to get them in the right mindset to nurture the leads that you're generating from LinkedIn, that can improve your ROI by 10, 20%. But obviously, the closer you get to the money, the longer those tests are going to take.

AJ Wilcox 33:36 So here are some of the types of tests that we like to run. There are ad tests and the first ad test that we like to run is same image, same headline, but we vary the intro in the ads. We like to test motivation there. So an example I like to use is maybe one of those makes them feel like the hero and the other one warns them that if they don't take some sort of action, they'll look bad or be disgraced. But you can definitely also do imagery or video ad tests, keeping the intro and the headline the same, but just varying visual. Testing offer against offer. So an ebook against a guide, or a checklist versus a cheat sheet, a webinar versus a case study. These are all good examples of offer tests you can run. What about how often should you fail before you decide that it's time to pivot and change your entire strategy? I'll give up on an offer if I've run three A B tests have messaging against it, and all six of those ads have failed. If that's the case, after our best effort, I'm certain that the offer just isn't that great. There's no amount of lipstick that I can put on that pig and make it look pretty. I guess this is gonna be my rule of threes because the same thing applies if I've tried three different offers in the same kind of vein. And if none of those offers work, that I'm going to guess we either don't have the right audiences or we don't have product market fit or we just haven't figured out what it is that this audience cares enough about. I just got a chance to speak at Social Media Marketing World in San Diego last week. And one of the speakers that I heard said something really interesting. We solve migraine problems, not headache problems. And what that means is your offers, they really do have to solve something really significant, that's causing a lot of pain, because someone's not going to go out of their way to go and sign up for something, or talk to a sales rep about something or download a guide about something that is just kind of a meh problem. If it's a headache, they can work through it. If it's a migraine, you have to stop everything and focus on it. So how do you then determine what your next test should be after you've finished one? If I have a brand new offer, my first test is almost always going to be an intro versus intro in these ads test against the same offer. I want to find out what motivation or how do we call out to them to get their attention best. If I've been running the same offer for more than a month, then my favorite test to line up is an image versus image test. And this is because if people have been seeing the same image over and over for a month, they're going to saturate, they're going to say, Ah, I've already seen that, and not pay attention to it. But if you can change up the imagery significantly, you'll get people to take a second look. And they may realize, ooh, this actually would be good for me. If you know what your audience likes already, you can start to do offer versus offer tests. So use the same motivation, the same callouts, but push them to one offer or another. Let's say you have two different offers. One is a guide that teaches them how to solve a certain problem. And the other guide teaches them how to investigate and analyze some of the results they're seeing. Test offer against offer and find out which is their bigger headache, or which ones their migraine. Maybe some of you have done market research. This is more on the PR side of marketing. But we get to do a lot of this with the level of testing that we can do on LinkedIn. Because the targeting is so good, we can break our audiences up into these little micro segments that act like little focus groups. So maybe you're trying to decide do operations folks, or do IT folks resonate more. Which one is our better customer? Do manager level seniorities interact with us in a different way than chief level or VP level? These are all tests that you can run simply by breaking these audiences up into separate campaigns and measuring their results against each other. The advice that I always give to my team is make sure that you keep a testing journal. This could be a Google sheet, it could be a physical notebook that you keep next to your desk, whatever it is, what this is going to be is a record of every test that you're running, and you want it to have a few things. First of all, you want to put the date. Second of all, you want to put the expected outcome of it. For instance, you might say I'm testing offer A against offer B. My hypothesis, so you include the hypothesis. My hypothesis is that offer B is going to perform better because I think it provides more value. Next you want to write down your parameters. So are you testing for a certain amount of time or after a certain amount of budget. And then lastly, you have to take action on this, you can't just leave the notebook there and never come back. So I like to put something on my calendar. On Friday at three o'clock, I'm going to go back and reevaluate this week's test. I'm going to go back to that testing journal and write everything down. Once you have several tests, you want to share these things, share them with your team. Freak, reach out and share them with me. Anything cool that you learned about your audience, or your offers or pain points, or messaging, these are all valuable things. These are hard fought victories. You need to remember them and share them so that you can then go and create new offers that take advantage of it. New ad copy that takes advantage of those learnings. And then you'll have higher performance from then on out. So I can't encourage you enough. Definitely make sure that you're keeping a testing journal so you can make sure that you are taking advantage of all of your learnings. Alright, I've got the episode resources for you coming right up. So stick around

Thank you for listening to the LinkedIn Ads Show. Hungry for more? AJ Wilcox, take it away.

AJ Wilcox 39:32 Alright, here's our resources from this episode. First of all, Chris Dayley, you'll see down in the show notes, we have links to his website, his Twitter and his LinkedIn. You'll also see the link to my profile as well so you can follow me for when we come up with that really cool LinkedIn Ads, test evaluation tool, whatever we want to call it something that calculates statistical significance ongoing over time. You'll also see the links to two different statistical significance calculators. One on Investopedia and one run on HubSpot as well as the link to LinkedIn advice for how to optimize and run tests. If you are new to LinkedIn Ads, or if you have a colleague who is definitely check out the link to the LinkedIn Learning course that I did with LinkedIn. It's by far the least expensive and the highest quality of any LinkedIn Ads course out there to date. Look down at your podcast player right now, whatever you're listening on, and make sure you hit that subscribe button, especially if you want to hear more of this in the future. If you hated this, I don't know why you're still listening. But yeah, you probably don't have to subscribe, but I hope you do anyway. Please rate and review the podcast and anyone that who reviews will give you a shout out live on air. And of course with any feedback, any questions about the podcast, suggestions, you can reach out to us at our email address [e-mail]. And with that being said, we'll see you back here next week. cheering you on in your LinkedIn Ads initiatives.