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Solve Attribution Woes: Adjust Your Settings & Expectations for a More Comprehensive Marketing Strategy

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Very rarely in my PPC life do I bring up the subject of attribution with clients, colleagues, or industry friends without seeing a look of pain cross a face that may have been perfectly congenial a moment ago. Much teeth-sucking and drink sipping ensues when the difficult topic of attribution enters the discussion.

We all fear we aren’t properly attributing our conversions to each platform, be it paid or organic. Namely, this frustration stems from 3 main factors:

  1. The customer journey is more complex than ever before. Customer journeys are not linear, between multiple devices, long sales cycles, and mere impressions (view-throughs) that may or may not have encouraged the user to convert. Facebook and YouTube now have brand-lift studies to close some of the gap, but the cost for these kinds of prove-the-brand-is-improving tests is often beyond the financial reach of smaller brands.
  2. There are more attribution platforms, both free and paid, offered in the digital marketing space. And we have no idea which one has the true data. We are at full saturation and everyone has a solution, including the new Amazon Attribution Beta, and Facebook Attribution, which became available to all advertisers in October 2018 after testing for a year in beta. Third-party attribution vendors crowd the market too, and marketers have decision fatigue.
  3. Getting any attribution source to play nice and line up with another seems like an impossible task, in a world of walled gardens. In the /r/PPC subreddit, it’s common to see cries for help every week regarding two reporting sources misaligning – most commonly, Google Ads and Google Analytics failing to align.

So what can we do to make more educated attribution choices? There are a few main things every marketer must take into account.

1: Pick Your Windows Wisely

Aligning your attribution with the truth starts with the windows you choose in each ad platform. A conversion window is a defined period of time in which a publisher can claim that a click or impression led to a conversion (be it a lead, app install, purchase, or otherwise.) You can set your conversion windows in every single ad platform except Google Analytics, which has reports specifically built for comparing windows.

The Google Analytics Time Lag report is a good place to start if you want to understand how long it takes a user to move from consideration to conversion:

The Google Analytics Time Lag report counts number of days between first touch and conversion

You can use the Path Length report in Google Analytics and segment by specific goals:

The Google Analytics Path Length report counts the number of interactions a user takes before converting

Which window do you choose? 30-day impression, 7-day click? 7-day impression, 1-day click? There are several ways to find out! Your window will depend on:

  1. The Nature of your Business
    • You’ll want to pick longer windows for your conversion settings when your products are more expensive, high-consideration products such as software as a service, home remodeling, etc. Comparison shoppers take their time. This is where tracking different movements of users from trial to paid subscription, email signup to quote request are vital so you can track the entire journey of the user. Each movement – from a potential customer learning about your brand to putting money in your pocket, must be tracked in all the platforms you can, from Facebook Analytics Event Source Groups to training salesmen to properly label leads in your CRM software.
    • You’ll want to set your windows to a short period of time if most of your customers are buying with their gut. This is true for those random products you buy from Instagram without much thought. Pony-Os Instagram ads, I’m looking at you! (I swear, it felt like a good purchase in the 7 minutes it took for me to consider it, toss it in my cart, and purchase it!) If your windows are short, you’ll want to align them with the settings of each and every platform you use, as well as your reporting software.
  2. You’ll want to consider the purpose of the advertising channel. Are you advertising for a conversion result, or a lift in brand awareness? For example:
    • Search tends to be a low-funnel channel and results in more direct conversions due to search intent.
    • Social channels tend to suffer from misguided budget cuts, due to marketers not recognizing that these channels are often first-touch or awareness-based. For example, we have a B2B client who runs LinkedIn campaigns to grow brand awareness among a highly specific, professional audience. Just having these high-quality audiences visit their site is improving the quality of their retargeting audiences and will be worth the investment in the long run. But by no means do we treat these campaigns as a conversion-producing, direct channel.
Pony-O low consideration productPony-Os are the fastest I’ve ever gone from watching an Instagram video ad to tossing my money at an advertiser. If you have a product with a short window like this, consider changing your attribution windows to more accurately reflect your buyers!

2: Learn How Different Platforms Attribute Conversions Differently

For Google Ads, the Attribution Playbook is a good place to start. Google also is helpful enough to provide an attribution tool that allows you to compare different search attribution models before taking the plunge and adjusting your conversion attribution settings:

If you haven’t picked through the Google Attribution modeling tool in a while, you’re missing out. You can model cross-device activity, paths and time-lags (similar to what you’ll find in Google Analytics), and first and last click analysis, among other handy tools to slice and dice your data.

Most marketers agree that “Last click” or “Final click” attribution does not even begin to tell the truth and it is no longer recommended. Industry leaders agree, and this Invoca blog on how Google last-click attribution leads marketers astray clearly lays out the reasons why.

It’s easy to look up how each platform uses attribution modeling. A quick search turns up these resources:

3: Appreciate Lag & LTV when Testing a New Channel or Campaign

One of the biggest mistakes that marketers make is deciding a strategy isn’t working too soon. When testing, make sure you have a specific statistical significance you’re shooting for or even a time period in which you’re willing to stick it out and test. If you need a refresher, this post on calculating statistical significance from our own Carrie Albright is a great place to start! Once you have concrete goals, it will make your analysis a lot easier, although patience is always needed when testing any new channel or initiative.

4: The Source of Truth is Beyond the Platforms: It’s in Your Sales Data

This should go without saying. But I’m going to say it anyway! Your salespeople are sure to know more about lead quality than your marketing team. Train your team to gauge lead quality in their CRM. If you’re an e-commerce company, use internal resources to understand revenue and lifetime value. It is vital to have complete clarity between each marketing dollar spent and trendlines of success in your company.

As an agency, Hanapin is always pushing to get more internal information and reporting transparency because if leads do not lead to revenue, we want to know about those failures as quickly as possible. The same for successes – Have regular meetings between all teams to make sure your marketing dollar is balanced between first-touch and bottom-funnel, brand and non-brand. The ultimate source of truth will be money in your pocket. For new clients, often the process of clarifying attribution is working hard to ensure all tracking flows smoothly from campaigns into whatever system is being used to measure success, be it Bizible, HubSpot, Marketo, Salesforce, Pardot, Shopify, BigCommerce, or any number of propriety systems.

The Best Time to Fix Your Attribution was Yesterday. The Second Best Time is Today

We are having more conversations with our clients about attribution every day. This is natural. The rise of automated systems within platforms (Google’s automated bidding settings, Facebook’s mysterious way of using their algorithm to find potential customers) is going to depend on your attribution settings being correct. So if they aren’t correct, fix them today. Look at your attribution windows. Check your settings. Talk to your agency, and get your sales reports in line.

The marketing stack is more complex today than it was yesterday. But there is no time like the present to evaluate your attribution within and without your digital marketing platforms. Review often, and review thoroughly. And make use of absolutely free tools like Facebook Attribution, which uses advertisers in similar verticals and products in the same price points to inform your attribution choices, and Amazon Attribution – they’re free and comprehensive, why not use them?

I hope this blog has given you some places to start auditing your own attribution settings and systems to cut through to the truth and pave the way for a more informed marketing strategy.

MARKETING

SEO Recap: ChatGPT – Moz

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SEO Recap: ChatGPT - Moz

The author’s views are entirely his or her own (excluding the unlikely event of hypnosis) and may not always reflect the views of Moz.

We’re back with another SEO recap with Tom Capper! As you’ve probably noticed, ChatGPT has taken the search world by storm. But does GPT-3 mean the end of SEO as we know it, or are there ways to incorporate the AI model into our daily work?

Tom tries to tackle this question by demonstrating how he plans to use ChatGPT, along with other natural language processing systems, in his own work.

Be sure to check out the commentary on ChatGPT from our other Moz subject matter experts, Dr. Pete Meyers and Miriam Ellis:

Video Transcription

Hello, I’m Tom Capper from Moz, and today I want to talk about how I’m going to use ChatGPT and NLP, natural language processing apps in general in my day-to-day SEO tasks. This has been a big topic recently. I’ve seen a lot of people tweeting about this. Some people saying SEO is dead. This is the beginning of the end. As always, I think that’s maybe a bit too dramatic, but there are some big ways that this can be useful and that this will affect SEOs in their industry I think.

The first question I want to ask is, “Can we use this instead of Google? Are people going to start using NLP-powered assistants instead of search engines in a big way?”

So just being meta here, I asked ChatGPT to write a song about Google’s search results being ruined by an influx of AI content. This is obviously something that Google themselves is really concerned about, right? They talked about it with the helpful content update. Now I think the fact that we can be concerned about AI content ruining search results suggests there might be some problem with an AI-powered search engine, right?

No, AI powered is maybe the wrong term because, obviously, Google themselves are at some degree AI powered, but I mean pure, AI-written results. So for example, I stole this from a tweet and I’ve credited the account below, but if you ask it, “What is the fastest marine mammal,” the fastest marine mammal is the peregrine falcon. That is not a mammal.

Then it mentions the sailfish, which is not a mammal, and marlin, which is not a mammal. This is a particularly bad result. Whereas if I google this, great, that is an example of a fast mammal. We’re at least on the right track. Similarly, if I’m looking for a specific article on a specific web page, I’ve searched Atlantic article about the declining quality of search results, and even though clearly, if you look at the other information that it surfaces, clearly this has consumed some kind of selection of web pages, it’s refusing to acknowledge that here.

Whereas obviously, if I google that, very easy. I can find what I’m looking for straightaway. So yeah, maybe I’m not going to just replace Google with ChatGPT just yet. What about writing copy though? What about I’m fed up of having to manually write blog posts about content that I want to rank for or that I think my audience want to hear about?

So I’m just going to outsource it to a robot. Well, here’s an example. “Write a blog post about the future of NLP in SEO.” Now, at first glance, this looks okay. But actually, when you look a little bit closer, it’s a bluff. It’s vapid. It doesn’t really use any concrete examples.

It doesn’t really read the room. It doesn’t talk about sort of how our industry might be affected more broadly. It just uses some quick tactical examples. It’s not the worst article you could find. I’m sure if you pulled a teenager off the street who knew nothing about this and asked them to write about it, they would probably produce something worse than this.

But on the other hand, if you saw an article on the Moz blog or on another industry credible source, you’d expect something better than this. So yeah, I don’t think that we’re going to be using ChatGPT as our copywriter right away, but there may be some nuance, which I’ll get to in just a bit. What about writing descriptions though?

I thought this was pretty good. “Write a meta description for my Moz blog post about SEO predictions in 2023.” Now I could do a lot better with the query here. I could tell it what my post is going to be about for starters so that it could write a more specific description. But this is already quite good. It’s the right length for a meta description. It covers the bases.

It’s inviting people to click. It makes it sound exciting. This is pretty good. Now you’d obviously want a human to review these for the factual issues we talked about before. But I think a human plus the AI is going to be more effective here than just the human or at least more time efficient. So that’s a potential use case.

What about ideating copy? So I said that the pure ChatGPT written blog post wasn’t great. But one thing I could do is get it to give me a list of subtopics or subheadings that I might want to include in my own post. So here, although it is not the best blog post in the world, it has covered some topics that I might not have thought about.

So I might want to include those in my own post. So instead of asking it “write a blog post about the future of NLP in SEO,” I could say, “Write a bullet point list of ways NLP might affect SEO.” Then I could steal some of those, if I hadn’t thought of them myself, as potential topics that my own ideation had missed. Similarly you could use that as a copywriter’s brief or something like that, again in addition to human participation.

My favorite use case so far though is coding. So personally, I’m not a developer by trade, but often, like many SEOs, I have to interact with SQL, with JavaScript, with Excel, and these kinds of things. That often results in a lot of googling from first principles for someone less experienced in those areas.

Even experienced coders often find themselves falling back to Stack Overflow and this kind of thing. So here’s an example. “Write an SQL query that extracts all the rows from table2 where column A also exists as a row in table1.” So that’s quite complex. I’ve not really made an effort to make that query very easy to understand, but the result is actually pretty good.

It’s a working piece of SQL with an explanation below. This is much quicker than me figuring this out from first principles, and I can take that myself and work it into something good. So again, this is AI plus human rather than just AI or just human being the most effective. I could get a lot of value out of this, and I definitely will. I think in the future, rather than starting by going to Stack Overflow or googling something where I hope to see a Stack Overflow result, I think I would start just by asking here and then work from there.

That’s all. So that’s how I think I’m going to be using ChatGPT in my day-to-day SEO tasks. I’d love to hear what you’ve got planned. Let me know. Thanks.

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What Is a White Paper? [FAQs]

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What Is a White Paper? [FAQs]

The definition of a whitepaper varies heavily from industry to industry, which can be a little confusing for marketers looking to create one for their business.

The old-school definition comes from politics, where it means a legislative document explaining and supporting a particular political solution.

(more…)

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HubSpot to cut around 7% of workforce by end of Q1

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HubSpot to cut around 7% of workforce by end of Q1

This afternoon, HubSpot announced it would be making cuts in its workforce during Q1 2023. In a Securities and Exchange Commission filing it put the scale of the cuts at 7%. This would mean losing around 500 employees from its workforce of over 7,000.

The reasons cited were a downward trend in business and a “faster deceleration” than expected following positive growth during the pandemic.

Layoffs follow swift growth. Indeed, the layoffs need to be seen against the background of very rapid growth at the company. The size of the workforce at HubSpot grew over 40% between the end of 2020 and today.

In 2022 it announced a major expansion of its international presence with new operations in Spain and the Netherlands and a plan to expand its Canadian presence in 2023.

Why we care. The current cool down in the martech space, and in tech generally, does need to be seen in the context of startling leaps forward made under pandemic conditions. As the importance of digital marketing and the digital environment in general grew at an unprecedented rate, vendors saw opportunities for growth.

The world is re-adjusting. We may not be seeing a bubble burst, but we are seeing a bubble undergoing some slight but predictable deflation.


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About the author

Kim Davis

Kim Davis is the Editorial Director of MarTech. Born in London, but a New Yorker for over two decades, Kim started covering enterprise software ten years ago. His experience encompasses SaaS for the enterprise, digital- ad data-driven urban planning, and applications of SaaS, digital technology, and data in the marketing space.

He first wrote about marketing technology as editor of Haymarket’s The Hub, a dedicated marketing tech website, which subsequently became a channel on the established direct marketing brand DMN. Kim joined DMN proper in 2016, as a senior editor, becoming Executive Editor, then Editor-in-Chief a position he held until January 2020.

Prior to working in tech journalism, Kim was Associate Editor at a New York Times hyper-local news site, The Local: East Village, and has previously worked as an editor of an academic publication, and as a music journalist. He has written hundreds of New York restaurant reviews for a personal blog, and has been an occasional guest contributor to Eater.

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