What is Google up to? Looking at the various Google tools and features, here is what I see is happening at Google.

Google has been developing Google Data Studio (GDS) for several years (this is their free version comparable to Salesforce Tableaux). Both are data visualization tools, which means they pull in data from multiple sources, tools, and databases to create visually appealing reports. Google sees Google Data Studio as a reporting overlay for Google Ads, Google Analytics, Google Search Console, and other tools. All those tools will send data to Google Data Studio. We’ll explain later where we see Google headed with Google Data Studio as it continues to update other tools.

Google Analytics was updated to Google Analytics 4.0 (GA4) in October 2020 and now, a year later, many companies are using it.

Universal Analytics, the previous version, was only a reporting tool. It collected and displayed website data. But GA4 isn’t an update. It’s an entirely new concept of data on the web.

The most powerful feature in Google Analytics 4.0 is predictive analytics. It can predict data. How does it do that? Google bought DeepMind (an AI company) in 2014 for $600m. David Silver at DeepMind developed Reinforcement Learning (a new type of Machine Learning). He developed Alpha Go to play the game of Go (Wei chi). Chess is easy for machine learning: the rules let one predict future steps. Alpha Go however is vastly more complex. How much?

Alpha Go has 10¹⁷⁰ combinations (there are only 10⁸⁰ in the universe). Instead of rules and steps, Alpha Go looks at patterns. In 2016, it won against the world champion. A year later, Alpha Go Zero won 100 straight games against Alpha Go, the previous version.

It’s important to understand these game-playing AI are not controlled. Tens of thousands of games in chess and Go have been recorded over the last three hundred years and are well-known to grandmasters. But the AI often makes moves that have never been seen before. The AI discovers patterns and opportunities that humans have never played.

Google owns this AI software, so it has been applying it to their software tools, Android software for phones, and the Google Pixel phones.

How does this matter for digital advertising?

Google has been collecting data on some two billion people and tens of millions of companies worldwide for twenty years now. Every email you’ve ever sent (including drafts that you deleted), every ad that has ever appeared on your screen, your Android phone movements, air pressure, temperature, and so on, and much more is recorded. Time for a pop quiz: How many Google websites are there? There’s Google Search, Gmail, YouTube, and so on. Perhaps a dozen? Twenty? Forty? There are more than 130 Google websites, and many do not say it’s Google. All of these are collecting data.

AlphaGo Zero looks at that ocean of data on its own to find behavior patterns. It matches behaviors with actions, such as purchases or registration.

Now it becomes interesting. Previous analytics tools (Google and other companies) were like weather reports in the 1970s. They could tell you it rained last week. So what? What’s the weather for the next 30 days? This is important for agriculture, transportation, and many other fields.

Google Analytics 4.0 uses AI to show you future data. It tells you this group will buy in the next seven days. That group will quit your website in the next seven days. Another group will be your top-revenue group in 28 days. This lets you adjust or create new marketing campaigns.

Analytics changes. It’s no more “reports of the past”. It’s actionable future predictions.

The Google Analytics 4.0 data can be imported into Google Data Studio for presentation.

What happens then?

If Google’s AI can find winning patterns in 10¹⁷⁰ data points, what does this mean for marketing? Currently, people enter a few dozen keywords, write a few ads, and manage bids and budgets by hand.

But the Google AI can do this far better. For example, a Responsive Search Ad (RSA) in Google Ads generates over 46,300 permutations. If you place three RSA ads in an ad group, that’s nearly 150,000 ads. Google Ads uses multivariate testing to quickly find the version with the highest click rate. If you have 300 ad groups, that can be forty-five million ads which it uses to find the best ads. Humans can’t do that.

Considering all of this, it’s plausible that within a few years, Google may combine Google Ads, Google Search Console, Google Analytics 4.0, and Google Data Studio into one tool, perhaps called “Google Marketing” which manages marketing on its own.

By Andreas Ramos
Vice President, Digital Marketing
Beasley Direct and Online Marketing, Inc.

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I’ve Come to a New Revelation. We Should Not Accept Bad Email Addresses from List Providers ANY MORE.

I’ve been researching and providing B2B and B2C lists to our clients campaigns for over 27 years. For B2B clients, I’ve used every data provider out there, including D&B, Netprospex, Discover.org, Data.com, OneSource, Lake Group, Hoovers, you name it — and they all have one thing in common: a high percentage (10-20%) of bad data, and even worse, bad email addresses. It’s as though the providers expect us to pay top dollar AND accept a certain percentage of bad email addresses as normal and acceptable. Yeah sure, they’ll replace the bad ones, but by the time you’ve figured out which are bad, the campaign may be long over and your email sending provider (ESP) is hot under the collar from your unacceptable email deliverability rate. Here’s why:

First of All, it’s Getting Harder to Fix the Bad Email Addresses Yourself

It’s become the practice of many marketers to go along with the bad data the providers supply and try to validate or “fix” it themselves by using some kind of a verification tool such as BriteVerify. However, the spam filters are catching on to these verification tools and learning to spoof them by giving false reports or throwing a higher percentage of emails into a catch all bucket, which you shouldn’t mail because they’re not really verified. So you’ve just paid a penny a name or more for validation data you increasingly cannot trust. As a result, it will increasingly bring your email sending domain reputation down by sending to bad email addresses.

Secondly, Improved Database Technology

Secondly, database technology has improved so much with AI and social media verification, that a list vendor literally has no excuse to have inaccurate contact data, let alone bad email data. If they have bad email data, they’re simply not very good at what they do, and they don’t deserve your business.

Lastly, don’t let them be lazy. Make them REALLY validate the data before they supply it to you. Ultimately, that means they have to be creative on their own dime and their own time to figure out if the email addresses are valid.

Finding the Best List Owners

I’ve known some list owners to send a mini survey out on some topic related to your campaign, to see which emails delivered (and which didn’t). This has an added bonus of giving you the response results to the survey. Others have very sophisticated email validation procedures that look at the data from 200+ data points. Bottom line, these list vendors work harder to provide you a good product, and they deserve your business more than the other guys.

You may ask who these mysterious data providers may be. I don’t make it a practice to endorse any vendors in my blog, because they may not offer everyone the same services that they offer me. I have negotiated with my vendors to go the extra mile, do the hard work, and to provide a good product the first time around. And that’s the key. You should negotiate and demand for this level of service from your vendor too.

In conclusion, the time has come for marketers to revolt and not accept bad email addresses from their B2B Data providers ANY MORE.

If you’re in need of quality B2B email lists, our pros can help you. Contact us to find out more.

Read about creating email templates here.

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