INBOX INSIGHTS, June 28, 2023: Monthly Reporting Part 4, Common Crawl in AI

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Monthly Reports, Part 4: Performance

Wow, you’ve stuck with me this long, I’m so glad to see you back again! This is where we put all the pieces of the puzzle together.

As a quick recap, we walking through how to create more robust monthly reports using the 5P framework. We’ve covered Purpose, People, Process, and Platform. The fifth P is Performance.

To refresh your memory, we created these user stories:

  • “As a marketing manager, I want to measure my content marketing, so that I know where to allocate budget.”
  • “As the content writer, I want to know what topics our customers care about, so that I can write valuable content.”
  • “As the content editor, I want to publish clear and succinct content, so that bounce rate is low.”
  • “As the analyst, I want to measure page views, so that I know what content resonates with our audience.”
  • “As a customer, I want to consume content that demonstrates authority, so that I know which company to hire”
  • “As a customer, I want to consume content that answers an immediate question, so that I know this company can help me”

Then we developed a process to automate data extraction into Looker Studio using metrics from the Google Analytics and Google Search Console platforms.

The last P to consider is Performance. Believe it or not, this doesn’t refer to the metrics in your report. Your performance, or measure of success in this case, is whether you answered the question stated in your Purpose.

In this case, the purpose is budget allocation. Take a look at the examples above and tell me if you think the stakeholder can make a financial decision. I’m going to go ahead and say that we have more work to do.

We got ahead of ourselves and skipped over the initial purpose when developing the processes. Here’s the good news, you can go back to any of the previous pieces and add what’s missing. In this case, we want to pull some of the attribution data provided in Google Analytics so we can see what channel are performing. We first need to know where our content is being published. This could be on our blog, in our email newsletter, or as a post on social media. To make sure you’re really clear, I’d recommend creating more user stories.

“As a content marketing manager, I want to publish content in our email newsletter, so that we can retain our subscribers”

Great! Now we know that email is a channel we need to pay attention to. As we’re refining our analysis process, we can include that as a metric. This should tell the marketing manager whether email is working and if we should continue to create content for that channel. That allows them to make budget decisions about current and future resources.

Repeat this process over and over until you have the right user stories, the right processes, and the right platforms to address your purpose. That is how determine your performance.

The only thing left to do is get started! Reply to this email and tell me about your journey or come join the conversation in our Free Slack Group, Analytics for Marketers.

– Katie Robbert, CEO

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Data Diaries: Interesting Data We Found

In this week’s Data Diaries, let’s answer a very common question about large language models, one that folks ask nearly all the time:

What are these models trained on?

When we talk about training a large language model, everything from the open source projects like LLaMa to big services like ChatGPT’s GPT-4, we’re talking about the ingestion of trillions of words from content all over the place. One of the most commonly cited sources across models is something called Common Crawl. What is it?

Common Crawl is a non-profit organization that crawls and archives the web. They’ve got 7 years worth of the web indexed and make it available to the general public for free. What’s in this archive? Well… pretty much everything that’s open on the web and permitted to be crawled and indexed.

As of the most recent crawl, there are over 88 million unique domains in the index comprising over 50 billion pages of text. It’s 6.4 petabytes of data.

Common Crawl

How large is a petabyte? If you take the average high-end laptop’s 1 TB hard drive, you’d need a thousand of them to equal 1 petabyte, so 6,400 laptops’ worth of storage. And bear in mind, this is just text. No images, no audio, no video, just cleaned text stored in machine-readable format.

Because this is a crawl of the open web, there’s a lot of stuff in the Common Crawl that you wouldn’t necessarily want to train a machine on. For example, there are prominent hate groups’ content in the Common Crawl, as well as known misinformation and disinformation sites.

Why are these sites used in machine learning model building, when they are known to be problematic? For one simple reason: cost. Companies building large models today are unwilling to invest in the cost of excluding content, even when that content is known to be problematic. Instead, everything gets tossed in the blender for the models to learn from.

Problematic content

In some contexts, this is useful; a model cannot identify hate speech if it has no idea what hate speech is, so if you’re building an application to detect hate speech, you would need that in there. However, in the big generic models like GPT-4, this can also cause them to generate hate speech. For marketers and businesses, this certainly would be a problem.

What’s the solution? We are seeing companies and organizations start to build far more curated datasets, in part by taking Common Crawl and excluding obviously problematic content as well as low-rank content. For example, not every blog post on needs to be part of the training library, and certainly known problematic content can be excluded. As time goes by, expect to see more and more refined models that have no knowledge of problematic concepts to begin with, and those models will be best suited for commercial and business applications where mistakes would be completely unacceptable.

So what? As you embark on deploying generative AI solutions, particularly those based on large language models, realize that there’s more out there than just ChatGPT – vastly more. Be on the lookout for models that not only suit your specific use cases, but are free of the problems that earlier and larger models may have.

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