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The next main episode of our PPC Ponderings Podcast is out, and it's a big one! Well, not in terms of length. After putting dozens of hours of interviewing and editing and producing into it, we ended up with a tight 30 minutes. We mean, big in terms of topic and questions.

Attribution is something we need to understand better, so in this episode, we interview four PPC/Analytics experts on what attribution is, and then I (Kirk) discuss my ponderings on digital marketing attribution I think need to be weighed into anyone's understanding of attribution.

Because of the length of the topic, we decided to spread out our four guest interviews across two separate episodes.This first episode focuses primarily on questions and concerns with attribution that should be kept in mind, while the next core episode will take a look at how we can practically use attribution in our PPC accounts. Just like in my book full of various PPC Ponderings, we'll start with the philosophy, and then move to the practical.

Huge thanks to our four guests for the next two episodes (and stay tuned for their full interviews to be released as bonus episodes over the next few weeks):

• Amanda Farley - AimClear
• Heather Aeder - Seer Interactive
• Mike Ryan - SMEC
• Nechama Teigman - AdVenture Media

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🎙 Episode 2 on Attribution is here! 🎙

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EPISODE 2 GUESTS

• Amanda Farley - AimClear
• Heather Aeder - Seer Interactive
• Mike Ryan - SMEC (Smarter Ecommerce)
• Nechama Teigman - AdVenture Media

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EPISODE 2 RESOURCES

These aren't the only resources we used for this show, but they're helpful ones we wanted to share.

• The History and Future of Marketing Attribution on Digital Marketing Institute
• The new dot com bubble is here: it’s called online advertising on The Correspondent
• Digital advertising is not the dot com bubble, improper attribution is on MarTech
• Modeling marketing attribution on DBT
• As Big Tech Squares Off, Ad Measurement Is A Familiar Weapon on AdExchanger

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👉 Stay Up on Future Episodes By Subscribing Here 👈

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Episode Transcript

Chris Reeves (00:01):

Attribution is a tricky concept. It's a word that can, onone hand, mean something tangible, while at the same time also being conceptualin nature. And it's not always clear which of those meanings is in use when wesee it written on or talked about in the world of digital marketing. Now, thisbecomes even more complicated when we hear the concept misused ormisunderstood.

Heather Aeder (00:22):

You know, that old, hype cycle graphic that has like thisbig bubble coming up in the chart and the period of hype cycle and then itdrops down into the trough of disillusionment, and then it comes back up moreinto reality, I would say Attribution has gone through that.

Amanda Farley (00:42):

You have a 12-month marketing cycle from like first touchto when they actually become sales qualified, and you're just looking at lastclick Attribution in ad channel, I'm like, "Well, why isn't Google justinstantly giving me leads?" Well, you're not thinking about the fact thatit's a 12 month buyer cycle.

Kirk Williams (01:01):

On November 6th, 2019, Jesse Frederick and Maurice Martinwrote an article for the correspondent entitled, "The New dot com Bubbleis Here." It's called online advertising. Well in case you're confused bythe article headline, the authors aren't real big fans of online advertising.In their post, they spend a good deal of time, on bursting, digital advertisingfor various and sundry negative attributes, all while and here's the key, in myopinion, what they're actually referring to is the online complexity of digitalAttribution, not advertising itself. I replied with my own article on marketingland titled "Digital advertising is not the dot com bubble, improperAttribution is, okay, I've never been one for short titles. And this one didn'tbreak that trend. Go check it out if you want to read my full argument, but Ibring this up because Jesse and Moritz, certainly aren't the first. And won'tbe the last to confuse advertising itself with Attribution and incrementalitymeasurements leading to poor decision makes on the part of the businesses,spending their money. Excuse me. As we advertisers like to say businessesinvesting their money on advertising

Mike Ryan (02:15):

Attribution often gets confused with incrementality. ThisI think is something that comes up a lot and they're kind of related topics,but you know, incrementality is more, the question would a given outcome haveoccurred without marketing pressure or advertising pressure, for example, andAttribution is more about understanding the value of these touch points and howthe picture all came together.

Kirk Williams (02:42):

That's Mike Ryan, Head of retail insights at SMEC orS-M-E-C if you prefer, we'll be hearing from him again over the next couple ofepisodes. As we discuss Attribution back to the article in question, ifanything, this proves the need for further conversation and understanding onAttribution all of us from the most seasoned marketing professionals to theinterns, fresh out of college need to better understand Attribution.Attribution is confusing, disagreed upon misunderstood, undervalued, overappreciated all while being, in my opinion, one of the most crucial digitalmarketing concept up for us to better understand in 2022 and beyond, we got tofigure this out.

Chris Reeves (03:26):

Welcome to the ZATOWorks PPC Ponderings podcast, where wediscuss the philosophy of PPC and ponder everything related to digitalmarketing. Our hope is that through these conversations with professionals inthe digital market space, we can gain a better understanding of what ishappening in the digital landscape and better prepare all of us for the future.

Kirk Williams (03:48):

The concept of Attribution has been around for years, manymarketers probably assume they have a rather complex view of Attribution, but Ithink it's important to get back to the basics before we discuss the moreadvanced how twos. Attribution is such an important weighty topic that wedecided to break it up into two podcast episodes with four guests being interviewedacross those two.

Mike Ryan (04:08):

Now I think it's one thing that's really attractive aboutdigital marketing in particular is that there's kind of a it's unleashed sortof a world of day data here to work with. Where if you think about traditionalmedia in the past, it was a bit more haphazard. The way you could connect thatback. There are ways of doing that too, but with digital marketing, it's just agreat way to help connect the value of marketing back to the business and alsoto help understand what's working, what's not working and where you can improve.And it ends up being a really important tool to understand what's going on inthe marketing

Kirk Williams (04:55):

Today's episode is going to give a brief introduction toAttribution, but we'll spend most of our time looking at ways it is commonlymisunderstood. And I'd like to bring up specific concerns. I have that I don'talways hear others talking about our next episode, then we'll look intopractical use cases of Attribution in our PPC management. As usual, I thinkit's worth pointing out that we try to present our interviewees in as accuratea light as possible based on the context of what they originally said. And itis worth noting that my narration opinions aren't always held by ourinterviewees. There's going to be a lot to think about here, but let's start atthe very beginning, a very good place to start. What is Attribution?

Heather Aeder (05:37):

My name is Heather Aeder. I am the VPF of analytics at SEAinteractive digital marketing agency. So for me, the way I define Attributionis, Attribution is kind of the study of understanding how multiple marketingtouch points on the way to a conversion impact return on ad spend

Nechama Teigman (06:01):

My name Nechama Teigman. And I work at AdVenture Mediagroup, digital marketing agency in New York.

Kirk Williams (06:08):

And if the name AdVenture Media rings a bell with you,that's because it's the home of Patrick Gilbert who wrote the latest andgreatest PBC book Join Or Die as well Isaac Rudansky the king of Udemy PPCcourses.

Nechama Teigman (06:19):

I would define Attribution as a process by which youassign value to different touchpoints that led to conversion.

Kirk Williams (06:27):

As I interviewed Nechama, the thing that stood out to me immediatelywas, well, how much smarter she was than me. Plus her name is way cooler too.

Nechama Teigman (06:34):

When I think of Attribution, I like think of it not justin terms of PPC, but it's really applies to everything in life. So if someonehas a partnership and they have to divide it up, how do we divide that up? Andthat's why if you... Like you getting too practical right now, but if you lookat the models behind the Attribution, some of the biggest ones are taking fromGame theory, which is also just like, how do we just act within life and how dowe assign these values in a fair way?

Kirk Williams (07:07):

Okay. So back in the '50s the grand Pappy of Attribution,the marketing mix model was already being utilized and required a whole lot ofconsumer point of sale data, internal company data, and a bit of guess work inorder to determine the impact of various marketing activities on sales. Whatwas the purpose of the marketing mix model? Well, it was to model the impact onsales of future marketing activities. In other words, to forecast sales, thatis after all, why we also use our new fancy click Attribution models, right?

Amanda Farley (07:38):

Marty at AimClear would say, "Are we delivering thebarrels of money that clients are asking us to deliver?"

Kirk Williams (07:44):

Here's Amanda Farley, the VPF of growth at AimClear adigital marketing agency based in Duluth, Minnesota. Now the first thing youthink of when you hear the words Duluth, Minnesota is, "Gosh, it must becold there." Well, you're not wrong.

Amanda Farley (07:56):

Are we driving the end value? And are we showinginfluenced brand performance marketing halo effects that go into the end valuethat a client's hiring you for?

Kirk Williams (08:07):

We don't just want to know which campaign or keyword sentus sales. So we can probably drop this tidbit of information over drinks at thepub where everyone knows our name. It needs to be useful for predicting futuresales.

Nechama Teigman (08:19):

I don't like being the person who works with the data anddoes like all the hard work and doesn't get to see the results from that. So Iprefer to sit in a place where I get to look at the data, make some realrecommendations off of it and get to actually see the outcomes of that.

Kirk Williams (08:36):

We want to understand how our marketing is driving salesso we can lean into what is working and lean away from what isn't working.That's what Attribution is for. Right? Well, it's complicated.

Amanda Farley (08:47):

We're still working in somewhat of a gray box of sometimesdata that can't be connected. And so we can look at influence. We can look atwe can come up with as many workarounds and connections as we possibly can, butat the end of the day, there's just sometimes stuff that can't happen becauseof internal systems.

Kirk Williams (09:05):

It's crucial to understand that the concept of Attributionis somehow both understated and overstated at the same time. At least that'show I see it. In my book of the same name is this podcast, I describe this interms of two ditches. You want to keep from driving your car into either theleft or the right ditch. And the same goes for avoiding the two ditches oneither side of Attribution. The first ditch is when people don't take the timeto think it through or understand what Attribution is.

Nechama Teigman (09:32):

But anytime you're looking at data, specifically marketingdata, but really any data, it's that it has to make sense. That doesn't mean ithas to validate your theory, because sometimes our theories are just straightout wrong, but it has to make sense if it's not making any sense, it doesn'tmean that the Attribution model's wrong. It doesn't mean that you are wrong.What it means is that you have to do a deeper dive.

Kirk Williams (09:57):

You don't want to ignore it because doing so can lead youinto the trap of thinking whatever simplistic view you are seeing in Googleanalytics is the whole truth and nothing but the truth, so help you God.Historically, this has meant accepting last click Attribution as a source oftruth, which can cause the unaware to invest money into primarily the lastchannel to send sales and therefore they risk strangling the funnel.

Amanda Farley (10:19):

Last touch, which we like to talk about a lot. I think inPPC because Google for a long time, got all of the credit, because that was defaulton everything was that they were the last one. They were the deal closer. Sothey got all the credit.

Kirk Williams (10:31):

Strangling the funnel is a marketers, rather grotesque descriptionof describing when a business stops advertising in those initial places, theirconsumers hang out, thinking those customers aren't valuable since they don'tsee evidence of purchasing in a last click model.

Nechama Teigman (10:44):

I'm definitely anti last click and first click. Like Idon't have a problem being strong on either of those two because you, bydefault, you're discounting other ones.

Kirk Williams (10:53):

Like if your customers are all typing your brand intoGoogle and then buying is the answer really to stop advertising everywhere elseto shift all of your ad dollars into brand searches. Well, eventually yourbrand searches are going to dry up. You strangled the funnel. Those people hadto learn of your brand somewhere in order to type it into the search bar, thusyou successfully market it to them previously down the line or up the funnel.You just didn't see that in a last click model, but that's okay because it's anecosystem. A problem with misunderstanding last click Attribution. Is to forgetabout that ecosystem.

Mike Ryan (11:28):

So it can strangle these really important brandingmeasures over time or awareness measures that were actually instrumental. Andyou didn't even know about it. If you looking at last click.

Kirk Williams (11:39):

The second ditch of Attribution, you know, the one on theother side of the road is that people can attribute more power to Attributionthan Attribution actually deserves.

Nechama Teigman (11:49):

When we were talking about the machine learning models,they're really just doing things that we can do if we wanted to, we can sitthere and we can do the math manually and we can calculate the probability ofeach things based off of the data that we have access to as long as a business,you're collecting this data, but it is accessible. So it's data of touch pointsthat people go through getting up to conversion.

Kirk Williams (12:13):

Listen, everyone wants the silver bullet for theirbusiness. We all want to be able to predict the future. And that leaves usvulnerable for someone to come along with fancy words like probabilistic oralgorithmic Attribution.

Heather Aeder (12:25):

The reality is like AI machine learning. Those arepredictive statistical models. Attribution is you using past data to comment oncurrent situation. It is not predictive. So Attribution doesn't really use AImachine learning from a mathy nerdy sort of standpoint. It's more likeanalyzing what happened in the past to tell me what's happening right now tomake decisions right now. So they're actually not related from my point ofview.

Kirk Williams (12:55):

In those more advanced models that we'll get into nextweek. The platform like Google, is taking all of its machine learning andbuilding a model around how it believes Attribution should be best dolled outusing probably millions at times more data points than we can see in a moresimplistic model. I mean, wow, how could that not work out in your favor?

Amanda Farley (13:15):

You have to look at all the different models to decidewhat works for you on that side of things. In reporting though, I like to lookat position or custom based modeling to show cross channel influence, butthat's more about push again, pushing for the conversions at the channel levelversus macro reporting.

Kirk Williams (13:35):

It's important to pause here and note a commonmisconception about click Attribution. Attribution never creates sales orrevenue for your business. Let me say that again because it's important tounderstand and you might have missed it. Attribution never creates sales orrevenue for your business. Changing an Attribution model in your Google adsaccount, doesn't actually create new sales. It simply dolls out the credit forthose sales to the various marketing campaigns or keywords or channels in adifferent way.

Heather Aeder (14:07):

I don't ever recommend just looking at one model. So likethe reason that there are multiple models in there, is so that you can actuallyget perspective to on what you're trying to optimize against.

Kirk Williams (14:19):

This goes back to taking Attribution too seriously.Sometimes people can obsess too much over the right or wrong model, when inreality, the model is simply assessing different percentages of credit to thosevarious channels along the customer's touch points with your business. Well,okay, at least the touch points it can see, but we'll dig into that a littlelater. To you see why it's foolish to assign then too much credit to wellAttribution itself?

Mike Ryan (14:45):

We'll never know the mind of an individual consumer. Youdon't want to over fit to that either. It's just about finding therelationship. It's just about finding an approximation of reality that isuseful to you. And so a misunderstanding of Attribution is maybe that this isactually reflecting objective reality, that can't be the case entirely.Attribution is just one view of reality and these different models can havevalue in terms of offering you different perspectives.

Kirk Williams (15:19):

Let me illustrate, let's say you sell a $100 pair of shoesin your Shopify store. Congratulations. You're an E-commerce expert guru ninjanow go sell a course. Sarcasm aside. I'm kind of a hypocrite, I guess because Iactually do have a course, but regardless, what source or sources should getthe credit for your $100 sale? Well, your selected Attribution model decides togive $40 to Facebook the first touch point, then another $4 to each of the fiveGoogle ads clicks in the middle of the buyer journey. And then finally another$40 to the Google organic brand search that led to sale at the end. If my mathis correct, that's $100, it's all dolled out that looks great. It's perfect.

Nechama Teigman (16:06):

When you oversimplify it, just asking, what's theincremental value of each traditional touchpoint? So if I'm comparing someonewho comes in from, for example, like a Google paid touchpoint and then you goon to Google organic and then you have Facebook touchpoint and that's yourjourney. And then you have another journey. That's a Google paid and then aGoogle organic and then a Facebook and then email. What was the probability ofconversion within the first sequence versus within the second sequence? Andwhatever additional probability you have in the second sequence. That's thevalue of email. So that's done at the larger scale, but it's also done for eachand every campaign or each and every keyword.

Kirk Williams (16:48):

You study that. And then you rush off to change budgetsaround based on that sale. Facebook and Google organic are the most profitable.Okay, well hold up. Why $40 and not $30 or $45 in that scenario? Because that'snot the way the particular model works. Well, why are Facebook and Google moreprofitable? Well, because that's how the particular model works.

Amanda Farley (17:12):

I also think it's important to know that this isn'tnecessarily a perfect math. It's more of just a way to look at the data to makesmarter marketing and ad channel decisions.

Kirk Williams (17:22):

So it's complicated. Well this sounds like a job formachine learning. What if we build a model that estimates value per visit basedon just a whole ton of data that we have, that should work right?

Mike Ryan (17:35):

There can be a tendency for Google or anyone, Facebook,whomever to bias their own touch points because it's financially attractive forthem to do that because it helps prove their value to advertisers. You mightfind that you have conversions that are getting claimed by multiple channelsand is where it becomes really challenging to take a bigger picture across yourchannels and harmonize that and try to understand how they're all supportingeach other, how their team players.

Kirk Williams (18:06):

Well, an algorithmic model can get closer to the truth intheory, because it would have a lot of data to track user behavior with, andtherefore assign value based on its modeling, but it's still modeling. It'sstill probabilistic. It's still estimated.

Heather Aeder (18:22):

The, other way it can help and where we can use statisticsto predict missing information is this statistical concept of imputation, whichhas been around for a long time and imputation uses kind of past information topass data trends, to predict what's next, when there is missing data. And theylook at sample data versus a population. And that something that Google isreally good at doing is collecting sample data and then they know the universesize.

Heather Aeder (18:55):

So they can predict up to the population. That statisticaltechnique has been around for a really long time. Would I again call it AImachine learning? Probably not, but that might just be because I'm old and I'mtraditionally trained statistician, I don't call myself a data scientist. Ithink for me, I still think this like data sciency world is a very like flashyterminology, AI machine learning. It's all like wrapped up into this coolthing. But in reality, it's all just to statistics, but statistics is a verybroad field and different techniques are used and applied in different ways.

Kirk Williams (19:40):

Okay. Now we come to major Attribution issue number two, ifyou're kind of counting. Not only is it still estimated in inherent biases arealso difficult to avoid, and this brings us to a core weakness. I want us toremember that Attribution contains. Attribution cannot and possibly never canmeasure the emotional impact of a visit.

Heather Aeder (20:02):

It's an interesting question. I'll say, I wouldn'tconsider myself to be an expert in emotions as a statistician, not my strongsuit, but I think how I might translate your question into the practicality ofhow marketers make decisions of what to put in market, is what contentresonates. And so if I translate emotion to various types of content, when Ihear the word content, then I can think, okay, well, when I think content, Ithink testing.

Kirk Williams (20:40):

As an aside, I do admit that mood measuring wearablescould perhaps measure this sort of thing someday. But my assumption is by thetime those have any sort of widespread adoption that privacy laws will preventthem from being utilized by companies anyway, for ad targeting. So just anaddendum there. But emotions, we as humans are emotional beings. So how doesone quantify the impact a specific ad on Facebook has as we scroll by it, notsimply quantify the actual visits themselves. How does one quantify theemotional impact that that ad has?

Heather Aeder (21:15):

If I think about how does content come into play withAttribution? I would say there's not a lot of analyses that are done in thatarea today. I would say content and evaluating how content triggers a reactionin a consumer. Most of that evaluation is done within the construct of AB ormultivariate testing, where you're serving up different content, ideally to asimilar set of users and then measuring the incrementality. I don't think itcomes out that much. It'd be interesting to do some work in that area, I think.

Kirk Williams (21:56):

I'm really into cycling and there's a specific cell phoneholder for my bike that I've been eyeing for about a year now, but I stillhaven't pulled the trigger. I have no doubt in my mind that I will. It's afantastic looking phone holder. I just haven't at this point winter in Montana,I'm also not going to for at least a few months now. When I eventually dopurchase it though, probably sometime in spring, when I'm excited to get backon the road again, it will undoubtedly be outside the window of theirAttribution model. So even though my first ad impression and visit made anindelible impact on my purchase decision, I just don't need it enough yet, ordon't want it enough, I guess, to spend the extra money.

Kirk Williams (22:33):

I don't have a great reason. I just haven't bought it. Soduring the last few clicks, Google measures with DDA, when I finally dopurchase, probably by typing in the brand name into Google and clicking on anorganic link when I'm shown an ad on Instagram again for the thousandth time,well then Google will get a look at a part of my customer journey, but they'llstill have no insight into the core emotional triggers I experienced when Ifirst saw that ad, they just see clicks.

Kirk Williams (23:01):

And Hey, in this example, they're not even going to haveinsight into those first few clicks if I'm outside of their Attribution lookback window. Okay. So that's one example, right? Maybe not even a great one,but it's not that much of an outlier. Many people do though purchase low costthings by making immediate emotional decisions. And those are simple maybe foran algorithm to track, but things get significantly more complex with longersales cycles. My point being to at least note that Attribution is more complexthan simply choosing an algorithmic model like DDA.

Mike Ryan (23:35):

There's going to be a certain percentage of users who willclick on an ad and, buy right away. And then there's going to be a certainamount of people who maybe spend some time making a decision, building a cart,comparing options on other websites. And they might complete a transactionhours later, days later. And what gets tricky is when, for example, a highvalue industry with like these larger conversions are something where we peoplereally need to consider awhile before they make a decision, is that they canlapse outside of the cookie window.

Kirk Williams (24:12):

Are you tired yet? Kind of wish you could go back to a periodof time. Like, I don't know before this podcast where Attribution was full ofnumbers that you could trust? Let's keep going. Let's look at a final concernwith click Attribution, where privacy fit into all of this? Consider if youwill, our world, in which marketers rely on machines to analyze hundreds orthousands of sales in order to determine how to accurately assess the behaviorconsumers in relationship to the marketing channels they engage with. Okay, nowconsider a world, a future world, a coming world in which a decent portion ofthat click data is made invisible to the machine learning algorithms due toprivacy concerns or laws. That's not good, "Man," the engineer shrug,"We'll just model it." Modeling here meaning making assumptions basedon the data that is present. Okay, sure. But at what point doesn't losing asignificant portion of your data begin to impact the accuracy of your models?According to Nate Woodman on ad exchanger, Google tries to solve this partiallywith its data driven Attribution model, by focusing solely on Google ownedchannels within that model.

Mike Ryan (25:21):

There can be a tendency for Google to bias their owntouchpoints because they know more about them.

Kirk Williams (25:28):

But man, that leaves a significant portion of the userjourney. All those pesky non Google properties, like I don't know the rest ofthe internet. It leaves them out of the model entirely. That can't be veryaccurate. So this is the world we're hurdling towards. And it's an importantone to keep in mind is we discuss Attribution.

Amanda Farley (25:46):

I think part of it's going back to the marketing basics.So it's going to have to be streamlined offers. And back in the day it wasdifferent... I don't want to say coupon codes, but it's like using differentcodes and different offers and different messaging where you can then attributecleaner than you would without it.

Kirk Williams (26:06):

Okay. Let's pause for a moment and catch our collectivebreaths or is it singular breath, breaths, breath. Anyways, let's pause for amoment. At this point, you are wondering if Attribution has, I don't know,perhaps insulted my mother or maybe I'm being paid by some ultra top secretsociety, anti Attribution Illuminati like villains who just really hate dataand modeling and statistics. Attribution is a great tool that has its place inevery marketer's toolbox. It's the blind acceptance of any particular model ora misreading of what Attribution is capable of doing that I hope I've causedyou to question in this episode, I'm not trying to get us all to hateAttribution. I'm simply trying to help us realize that we can never and shouldnever take what we're seeing in a single model, even a complex algorithm modelas the gospel truth.

Nechama Teigman (26:58):

So the first thing I would just say is, you should have anidea of what the data should be, and if it's not, then do everything you can tofigure out what's going on. And if after you do a real thorough check and youkind of look through everything, if it still shows that then at least have atheory at the very least of why the model could be right before leaning allinto it. The second thing is that I would look at it more directionally.Whereas if it's saying that a specific campaign is driving most of yourconversions and is the most efficient then it probably is the best campaign, ifwe're being honest, as long as we're looking at the model correctly, but thatdoesn't mean that the ones are necessarily bad.

Nechama Teigman (27:45):

And it doesn't mean that it's, 10 times better than theone it's saying it's 10 times better than. So I would look at it with the grainsalt, but still putting a decent amount of trust at it because on it, becauseat the end of the day, you need to trust something.

Kirk Williams (27:58):

Attribution is meant to be a directional guideline forforecasting, but I believe there are times a great marketer may look at whather Attribution data is telling her and then set it aside because of what sheknows about a specific audience and creative that hasn't and perhaps never willbe yet been proven in the data. Perhaps the off quoted advertiser adage,"Don't bring your opinion to a data fight" needs to be reconsideredhere.

Mike Ryan (28:23):

A misunderstanding of Attribution is maybe that this isactually reflecting objective reality, that can't be the case entirely.Attribution it's always going to be just one view of reality and thesedifferent models can have value in terms of offering you differentperspectives.

Kirk Williams (28:46):

So let's bring it home. In our next episode, we're goingto discuss the practical application of Attribution in the world of PPC. Howshould we be thinking about the specific Attribution models we have access toin Google ads? How can we best manage our accounts with these things in mindthat we've discussed in this first episode, other dangers in changing theAttribution model in our accounts, or is there even a wrong Attribution modelto use?

Nechama Teigman (29:09):

So I would just say like knowing your Attribution modelsand knowing what's going on is important to be able to decide how much creditto put in the data at the end of the day.

Kirk Williams (29:19):

We'll dig into these questions and more next time. But fornow, I hope I have convinced you that click Attribution like anything else hasits pitfalls to avoid. Don't let your Attribution model stifle creativity inyour marketing department. Great marketers across the centuries have createdmemorable and astounding campaigns simply because they had a vision, a soundstrategy, and someone with the guts to approve their idea without any clickdata to back it up, use data as a wise counselor guiding you in your ultimatedecision, but don't treat a specific Attribution model as your Lord and savior.I'm Kirk Williams and may the auctions be ever in your favor.

Speaker 7 (30:11):

(singing)

Chris Reeves (30:14):

This podcast was recorded in the Zeta works publishingstudio in Billings, Montana. Zeta works publishing is a subsidiary of Zeto apaid search agency focused on E-commerce brands owned by Kirk Williams. We lookforward to seeing you again next time.

Speaker 7 (30:36):

(singing)