Almost Timely News, 1 August 2021: What If You Can’t Afford AI, Google Analytics Tracking

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Almost Timely News: What If You Can’t Afford AI, Google Analytics Tracking (8/1)

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What’s On My Mind: What If You Can’t Afford AI?

One of the most common questions whenever I present about the use of AI in marketing is, “What if we can’t afford an AI engineer and a data scientist, or can’t afford an agency or vendor?” In the past, I’ve struggled to answer this question in a satisfactory way for a couple of reasons. First, I’ve struggled to answer it because I use AI every day, so I have trouble imagining what it would be like to not have access to the tools. It’d be like trying to understand someone who didn’t have access to spreadsheets – they’re just part of my everyday work.

The second reason I’ve struggled to answer the question is because the problems I face at work every day are large-scale problems that are well-suited to AI. Problems involving small data generally don’t land on my desk; someone else has already solved them, and I’m not needed. It’s this train of thought that has led to what I think is a satisfactory answer to that question.

AI is good at three things: processing data faster (and thus being able to handle a lot of it), processing data more accurately, and processing data in routine ways. Google’s Chief Decision Scientist Cassie Kozyrkov calls AI and machine learning nothing more than “problem labeling machines”, which is accurate. We use AI to turn data into numbers that can be calculated and processed, clustered and predicted.

But that presumes we have enough data to do all that labeling and processing. AI fails when we don’t have enough data. And therein lies the distinguishing factor, the real answer to the question.

You need AI when you have machine-sized problems.

You can use human solutions when you have human-sized problems.

For example, suppose you want to know what works in tweets, what topics to cover. If you downloaded all your tweets, and sorted by the most engaging, you could probably get a good idea of what works by reading – manually – the top 100 tweets, and doing a bit of legwork to group them together by topic and language. You don’t need AI for that.

If you need to do that for an entire industry sector, you’ve now got a machine-sized problem, and that’s where AI shines. There’s no practical way to sort and process hundreds of thousands of tweets in a timely fashion.

Companies like Google process more data with AI than they ever could with humans. They’d have to employ most of North America just to deal with a day’s worth of data. AI is called for.

If you need to understand the language used on a company website, you can have a person read the top 10 or even top 100 pages – that’s a human-sized problem. If you need to understand the language used on Wikipedia? That’s a machine-sized problem.

So, here’s my answer to “What if we can’t afford an AI engineer and a data scientist, or can’t afford an agency or vendor?”. Find a way to reduce the data down to a human-sized problem and solve it with humans until you have enough resources – money, time, people – to work with the full-size dataset. Sampling data is a time-honored method to make big data smaller, and doesn’t require anything more sophisticated than a semester’s worth of statistics classes in university (assuming you did well in the class, of course). Make the data and the problem fit the resources you have to solve it as best as you can.

What do you think? Does that answer make sense to you?

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ICYMI: In Case You Missed it

If I had to suggest only one of these articles to read from this week on the blog, it would be the piece on how Google Analytics works when it comes to attribution and tracking codes. A lot of folks don’t know what happens behind the scenes, so this explainer should help clear things up and underscore the absolute importance of properly using tracking codes.

Skill Up With Free Classes

These are just a few of the free classes I have available over at the Trust Insights website that you can take.

What I’m Reading: Your Stuff

Let’s look at the most interesting content from around the web on topics you care about, some of which you might have even written.

Social Media Marketing

Media and Content

SEO, Google, and Paid Media

Ad: AI For Marketers, Third Edition

With every software vendor and services provider proclaiming that they too are an AI-powered company, it’s more difficult to demystify artificial intelligence and its applications for marketers. What is AI? Why should you care? How does it apply to your business? In the newly revised Third Edition of AI for Marketers, you’ll get the answers you’ve been looking for. With all-new practical examples, you’ll learn topics like attribution modeling, forecasting, natural language processing, and influencer identification.

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Tools, Machine Learning, and AI

Analytics, Stats, and Data Science

All Things IBM

Ad: Google Analytics 4 Readiness Audit

Google Analytics 4 is the next generation of Google Analytics and at some point will be mandatory for everyone using GA. Are you ready? If not, get a checkup with my company, Trust Insights. We’ll help you:

Get your existing Google Analytics account in shape with proper goals, tracking cleanup, and best practices
Identify key issues that will block your ability to use Google Analytics 4 and help resolve them
Help set an analytics strategy for this that focuses on answers, decisions, and growth rather than pouring more data in your inbox
Build a migration plan for Google Analytics 4, including Google Tag Manager and Google Data Studio

Click here to order a Google Analytics checkup »

Good Reads, Long Reads, Interesting Stuff

Fun, Games, and Entertainment

Economics, Politics, Environment, and Society

Ad: How to Prove the ROI of your Marketing Agency

I put together a brand new talk on how agencies could use data-driven marketing as a way to showcase their value and real results they obtain. Fundamentally, agencies need to take five steps to make this journey:

Become data-driven. Making decisions with data allows you to act faster and make better decisions when done right.
Be crystal clear about KPIs. What’s a KPI? It’s the number you get your bonus for (or fired for).
Build an agency cookbook. Cookbooks set apart good agencies from great ones.
Use data to become proactive. Impress clients by being more proactive and pushing them.
Squeeze all the juice from your tools. You probably don’t need to buy more tools.

An agency which takes these steps becomes more and more valuable to its clients. For folks on the client side, these are the things you should expect of your agencies, things you should ask for when agencies are pitching you. Agencies not doing these things will not serve you as well as they could. There’s obviously a lot more detail, so go ahead and watch the talk now.

Watch the talk now by filling out this form »

How to Stay in Touch

Let’s make sure we’re connected in the places it suits you best. Here’s where you can find different content:

My blog – daily videos, blog posts, and podcast episodes
My YouTube channel – daily videos, conference talks, and all things video
My company, Trust Insights – marketing analytics help
My podcast, Marketing over Coffee – weekly episodes of what’s worth noting in marketing
My second podcast, In-Ear Insights – the Trust Insights weekly podcast focused on data and analytics
On Twitter – multiple daily updates of marketing news
On LinkedIn – daily videos and news
On Instagram, mostly personal photos – personal photos
My free Slack discussion forum, Analytics for Marketers – open conversations about marketing and analytics

Events I’ll Be At

Here’s where I’m speaking and attending. Say hi if you’re at an event also:

MAICON, September 2021, virtual
MarTech East, September 2021, virtual
Content Marketing World, September 2021, Cleveland, OH
MarketingProfs B2B Forum, October 2021, virtual
HELLO Conference, October 2021, New Jersey

If you’re an event organizer, let me help your event shine. Visit my speaking page for more details.

Can’t be at an event? Stop by my private Slack group instead, Analytics for Marketers.

Required Disclosures

Events with links have purchased sponsorships in this newsletter and as a result, I receive direct financial compensation for promoting them.

Advertisements in this newsletter have paid to be promoted, and as a result, I receive direct financial compensation for promoting them.

My company, Trust Insights, maintains business partnerships with companies including, but not limited to, IBM, Cisco Systems, Amazon, Talkwalker, MarketingProfs, MarketMuse, Agorapulse, Hubspot, Informa, The Marketing AI Institute, and others. While links shared from partners are not explicit endorsements, nor do they directly financially benefit Trust Insights, a commercial relationship exists for which Trust Insights may receive indirect financial benefit, and thus I may receive indirect financial benefit from them as well.

Thank You!

Thanks for subscribing and reading this far. I appreciate it. As always, thank you for your support, your attention, and your kindness.

See you next week,

Christopher S. Penn