Jason Lett

Traditional MMM uses a maximum likelihood approach. This means that the results are purely focused on the correlations in the data over time. The reality is that this kind of analysis uses data that no marketer or manager would trust 100% to guide decisions. The #Bayesian estimation approach allows its users to incorporate multiple sources of information, including but not limited to MTA & incrementality testing results. By doing so, the model is more informed and more consistent with the brand's larger information estate. The more you know!

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So… if a link in an “organic” result is actually an affiliate link, crediting a paid partner with a click, a site visit or a sale… That would mean search engines are monetizing search listings, including allegedly organic Map results for #retail locations. Here’s what we’ve uncovered: https://lnkd.in/dQr_DqiU #SEM #affiliatemarketing #marketingROI #searchengines #ftc

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Your experimentation insights are your one of your company’s biggest assets but……. You experiment knowledgebase is a dumpsterfire of useless data that no one looks at. It's riddled with data integrity issues that ultimately makes the C-level lose trust in experimentation. I've seen this first hand and I know the reason why…. #CRO teams and individuals don’t pay enough attention to the nuanced and systematic ways in which an experimentation program is managed. For them, managing an experimentation program is just easy - dump data into an Airtable spreadsheet and move some cards around a Kanban. Who cares that it gets forgotten about. They've moved onto the next test. For them, any tool (or tools) will do because they just need to “track their experiments” and they don’t think about the value of the data beyond that. I sat back and watched many CRO gurus talked about how easy it can be to just fire up any random project management tool and track experiments and “build culture of experimentation” For years, I didn’t challenge them because I felt it might be conflict of interest or I might be too salesy because I run Effective Experiments . So I kept quiet…. I watched how the industry was running into the same problems year after year. How they couldn’t get stakeholder buy-in. How they couldn’t get experimentation to grow in a structured way. All whilst these gurus were showing them how “easy it is” and leading them down the wrong path. I kept quiet even though I saw how little these gurus truly understood about managing experimentation programs beyond the surface level. So now, I won’t keep quiet anymore. I want to show you how these gurus are holding you back and the promise of fire up an airtable and automate it may be causing the industry to devolve into Airtable experts rather than delivering CRO value to their companies. I will show you how to go beyond thinking of tasks and tickets and Kanban boards. I will show you how to think about an experimentation program beyond the basics of prioritisation, planning and reporting. I'm doing a webinar with Kameleoon where I will show you why Airtable (and others generic tools) falls short as an experimentation management tool. Webinar is on Thursday 11th July at 12pm EST 5pm UK 6pm CET. Link in comments.

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I've seen a lot of Google Ads accounts. The best are doing two things: 1. Taking advantage of every hack/tactic/structure available now. This looks like PMax feed/nonfeed splits, or brand non/brand splits. Coverage across Demand Gen and YT. Well built and segmented DSAs. Keyword match-type coverage. Solid ads. But what else? 2. They're prepping for what's coming Google makes a ton of changes. You can tell what is coming based on where they invest. PMax Vertical Video Broad Match The best accounts have a plan for what happens when DSAs sunset, when match types go away, when Standard Shopping gets de-prioritized/sunset, when vertical video is a requirement of running shopping (PMax). If you aren't ready for what Google Ads is going to look like in 2025, you'll be spending a lot of time next year learning instead of winning. #GoogleAds #SEM #PaidSearch #PPC #PPCchat

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I opened up a GTM container to see this new gem added to the header. "Container quality" While I don't think this feature will offer much to experienced GTM users (and might cause more panic to inexperienced users), having some checks in place are a win in my book. Here are the diagnostic issues you might be presented with according to Google: - Additional domains detected for configuration: If you have multiple domains as part of the user path to conversion, you may need to add your additional domains to your configuration - Config command out of order: your gtag event commands come before your gtag config commands - Consent mode installation out of order: Some of your web pages may be loading consent mode commands in the wrong order - Missing conversion linker: This diagnostic appears when you have a Floodlight and/or a Google Ads tag, but you have not added a conversion linker tag to your Google Tag Manager container - Some of your pages are not tagged: We'll tell you when pages on your website are untagged, the Tag coverage summary will display the untagged pages to help you find and fix Intrigued to read more about this as more information comes out.

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2 Most Common CRO Execution Problems Other than not optimizing, the most common mistake we see is not matching the CRO strategy to the sample size. Usually, folks push their sample size past the limit (sometimes waaaaay past…A/B testing on 137 conversions a month, I’m looking at you). That’s just playing optimization, not doing it. Lot’s of tools in the CRO toolbox. Make sure you’re picking the right ones for each and every job. Second most common problem - focusing in the wrong area. Who gets the bigger trophy, the optimizer who finds a 100% lift on 1% of the conversions or the optimizer who gets a 2% lift on 100% of the conversions? #conversionsciences

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Bonus point for the F*ck Survey Fraud shirt, Roddy! Good challenge to the industry about what we have done to create the current situation we are in where data quality is still an issue. First, he unpacked the MRX monster: - constrained budgets - pricing pressure - disengaged participants - survey farms and more sophisticated organized fraud - more bots (non-human) “participants” - AI being used for open-ends The answer is that instead of focusing on CLEANING the data we should be getting focused on it being real, representative and reliable data by not allowing fraud to even get into the survey. It’s a commitment to addressing the root of the problem and not spending money just mopping things up. There’s the dtect differentiation for sure. He had some really great figures to understand the actual hard and soft costs of fraud. Why do we find this totally acceptable??? Also fun scratch ticket below. Great presentation. At Quirk's Media here in New York. Little Bird Marketing #quirks #mrx

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There's still quite a bit of unknown of how Google integrating Gemini into its standard search experience (referred to as Search Generative Experience, or SGE) will broadly impact SEO. But more and more data is coming out about the current state of the SGE. Search Engine Journal published an in-depth report with their findings today. Below are the key takeaways: 🔥 This is critical: websites can only accrue organic traffic from the SGE AFTER users click on the source of the AI-generated content. We all know how much adding an additional click to the process of the user journey can negatively effect traffic. This alone will likely decrease how much organic traffic is being driven to everyone's website 🤖 Some results (around 5%) don't even have original sources, meaning the content of the results are produced by Google 📈 The appearance of SGE's varies drastically by vertical, but will holistically increase over time. Of the industries analyzed, Beauty saw the highest rate of SGE at 95% and Finance saw the lowest at 47% 📊 In some cases, Google will show a generate button prior to showing the AI-based results. Safe to assume Google is experimenting with different layouts of this new experience, and testing how users will engage with Gemini if the AI results aren't delivered right away. The discrepancy between these two experiences also varied drastically by vertical 📉 Verticals with a higher portion of SGE results will have less time to pivot their SEO strategy accordingly. The Health industry has the highest portion of auto-generated SGE results, and therefore will be the most impacted by this change 🤑 Primary conclusion - This change will drastically overhaul everything we know about SEO, and everyone needs to be ready to pivot to this new reality very quickly. My personal opinion: everyone will likely see less organic traffic, and have to become more reliant on Paid Search. https://lnkd.in/gRj_Gwp9

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Quality Data In = Quality Data Out 👏 I've seen it time and time again: businesses get lazy with data input and system maintenance, and suddenly, HubSpot turns into a junk drawer overflowing with outdated information and clutter. 🫣 A junk drawer might be fine for old batteries and random knick-knacks, but it’s a graveyard for your business opportunities. Over the years, I’ve helped countless clients clean up their HubSpot instances. Don’t invest in a tool designed to HELP you sell more, only to let your leads languish in disarray.

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CRO should have a dedicated line item in your MARKETING budget (not your Analytics budget). That's because CRO lowers your CAC and improves your ROAS (wait, those are marketing metrics...) Follow along: - You run a few experiments on your site. - You get a lift in conversion rate (CVR). - Which means more new customers for the same ad spend. - Reducing your CAC. - CRO gives you a little AOV boost too - Which means more revenue per customer - And a boost in ROAS Here's a more detailed scenario in case you're a numbers person. 👉 Scenario 1: Baseline Ad Spend: $100k Traffic: 100k Visits Conversion Rate (CVR): 3% New Customer AOV: $150 Assuming 75% of orders are from new customers: New Customers: 100k visits x 3% CVR x 75% = 2,250 CAC: $100k / 2,250 = $44 New Customer Revenue: 2,250 New Customers x $150 AOV = $337.5k NC ROAS: $337.5k / $100k = $3.38 👉 Scenario 2: Sprinkle on the CRO Let's say your CRO program boosts CVR by 10%. New Customers: 100k visits x 3.3% CVR x 75% = 2,475 ✅ CAC: $100k / 2,475 = $40 New Customer Revenue: 2,475 New Customers x $150 AOV = $371k ✅ NC ROAS: $371k / $100k = $3.71 But this is conservative. CRO can boost AOV too. With an additional 10% AOV lift... New Customer Revenue: 2,475 New Customers x $165 AOV = $408k NC ROAS: $408k / $100k = $4.08 (+21% to baseline) Of course, implementing CRO comes with costs (tools, personnel, agencies). But will this cost offset some of your Ad Spend? I bet it would. Do you include CRO in your marketing budget? ...Or does that flow through a different team (analytics, product, ops)? Ps, I share simple explanations weekly on #data and #marketinganalytics . If you learned something, please give me a follow and share w/ your team.

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If we all collectively comment on Google Analytics posts/topics “cool that’s great but where’s the Annotations?” maybe we could have a *checks notes* BASIC FUNCTIONALITY NEED by end of year. UA had this and I’m still blown away that #GA4 launched without it… and still has yet to do it. Salesforce integration? That’s cool. Let’s have some basic functionality back that benefits everyone… not just a corporate partnership where we pat ourselves on the back and call it a better product. #analytics #marketing

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Is Bing intentionally monetizing organic map listings in search? For a large #retail client’s  #affiliatemarketing program, we uncovered an anomaly in the Bing Map SERP (search engine results page) that turned a seemingly benign search into an affiliate-sponsored result. We’ve outlined some findings here: https://lnkd.in/dQr_DqiU #SEM #affiliatemarketing #marketingROI #searchengines

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If you read anything today - make it this. Mark Ritson talks to the advantages of running testing on ads, and rightly so. And the same principles apply to your packaging. Very often, the first engagement a consumer has with your product is when they see it on the shelf. If that packaging does not capture attention and convey a reason to buy - then it won’t work. Even tiny changes to your packaging front can affect how shoppable it is - yet you are blind to this unless you test the impact. The money needed to understand performance pales in comparison to the downside if you get it wrong. Why take that risk?

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An easy way for you to forecast reach and audience penetration is to use Census data. How accurate is it? Well, it depends how accurate you need your forecasting data to be? Census data can be a useful tool if you're building out a geographically-specific persona or researching the potential audience reach within a community or to integrate via a data append. There are hundreds of use cases. But, if you need more precision, and not just directional data, consider harnessing customer insights from real-time brand shoppers using primary research. Nothing is better (and more accurate) then curating insights directly from your customers. https://data.census.gov/ #research #customerinsights #censusdata

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I have some sympathy for anyone who works in metrics or audience between now and the end of the month! 📈 An email from Apryl Pilolli reminded me that the clock is ticking on downloading your historical Google Analytics data. Of course, the manual download option is the only free option, but here are some other tips on how to approach this: https://lnkd.in/gDwV6tT9

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Did you know that when GA4 shows "User" metrics, it's really showing "Active Users"? We take a look at how Google defines users in our latest installment: https://bit.ly/4cY3fAO #analytics #ga4 #marketing #google

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I'm seeing a ton of cases lately where organizations are noticing that Landing Page is showing up as "not set" in their GA data. There are a few common reasons for this to occur, but one reason in particular has stood out to me lately. Apparently it's common for organizations to set up timer-based events to measure "engagement" in some increment of time. For example, 15/30/60/90 second "engagement" events being fired off to GA. These events aren't directly tied to any particular user action; they're just firing after a page loads, dependent purely on elapsed time. I'm seeing this cause rampant "not set" issues, and massive session inflation. Here's why: GA4 naturally lists Landing Page as "not set" for sessions that don't have any page_view events. And these kinds of timer-based engagement events are inadvertently creating large volumes of sessions with no page_view events. Consider this scenario. 1. Users are browsing the site 2. Their sessions time out due to inactivity 3. Later, the user does something to trigger events to start firing again (for example, they return to the browser tab that the website was on) 4. Events are dispatched to GA4 without a page_view event happening. These events have to have a session, so a new session is instantiated. I'm not sure how valuable these kinds of events really are, but at a minimum, organizations should be aware of the potential unintended consequences, which include: - Wonky-looking data your stakeholders will notice and question - Serious overcounting of sessions - Major skewing of session-based metrics (e.g. artificially deflated conversion rates) Check your Landing Page data and see if you have a non-trivial amount of "not set" showing up! h/t Todd Bullivant for nudging me down this rabbit hole.

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