INBOX INSIGHTS: Monthly Reporting Part 4, Common Crawl in AI (6/28) :: View in browser
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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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the limitations of data science skills. They explore the various aspects of data science and what it truly means to be a data scientist. They touch upon the importance of understanding the scientific method and how it applies to data science. The conversation also delves into the misconception that data science is the sole focus of a data scientist’s work, highlighting the significant role of data engineering, data analysis, and programming skills. They emphasize that data science cannot exist in isolation and requires a strong foundation in other disciplines. The episode concludes with advice for individuals considering a career in data science, encouraging them to focus on their interests and strengths. Overall, the discussion sheds light on the complexity and interdisciplinary nature of data science, challenging common misconceptions about the field.
Last week on So What? The Marketing Analytics and Insights Livestream, we discussed podcast launch tactics. Catch the episode replay here!
This Thursday at 1 PM Eastern on our weekly livestream, So What?, we’ll be looking at the tactics and techniques for launching a podcast. Are you following our YouTube channel? If not, click/tap here to follow us!
Here’s some of our content from recent days that you might have missed. If you read something and enjoy it, please share it with a friend or colleague!
- In-Ear Insights: Limitations of Data Science Skills
- Analytics data as a rearview mirror
- Mailbag Monday: Where would you recommend getting started for learning better prompt generation?
- So What? Launching a podcast – podcast marketing tactics
- Mental Health Resources – Mental Health Awareness Month
- INBOX INSIGHTS, June 21, 2023: Monthly Reporting Part 3, Instagram for Influencers
- In-Ear Insights: How to Market Boring Products
- Almost Timely News, June 25, 2023: When Should You Use Generative AI?
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Get skilled up with an assortment of our free, on-demand classes.
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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.
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.
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 blogspot.com 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.
- Case Study: Exploratory Data Analysis and Natural Language Processing
- Case Study: Google Analytics Audit and Attribution
- Case Study: Natural Language Processing
- Case Study: SEO Audit and Competitive Strategy
Here’s a roundup of who’s hiring, based on positions shared in the Analytics for Marketers Slack group and other communities.
- Digital Measurement Solutions Team Lead at Seer Interactive
- Senior Analyst, Wired at Conde Nast
- Senior Digital Analyst at Save the Children
- Senior Manager, Digital Analytics at Harnham
- Senior Web Application Developer at NextAfter
- Strategy & Analytics Consulting Manager at Seer Interactive
- Web Analyst at veikkaus oy
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Tick-tock, tick-tock! Time’s winding down to secure your spot at MAICON, The Marketing AI Conference, this July 27-28 in Cleveland, Ohio. Not convinced? Picture this: an enlightening pow-wow with the smartest in marketing and AI, including a can’t-miss deep dive into generative AI and large language models by Trust Insights Chief Data Scientist, Christopher Penn. It’s your backstage pass to all things AI and marketing. Need more? Here’s a nifty $150 off the ticket price with the code TRUST150. So, ask yourself, “When did I last turbo-charge my marketing with AI insights?” If that’s a head-scratcher, it’s MAICON o’clock for you! Secure your ticket to the future here: https://trustinsights.news/maicon23. Remember, the early bird catches the AI insights!
Where can you find Trust Insights face-to-face?
- MautiCon, Virtual, June 2023
- MarketingProfs AI for Content Creators, Virtual, June 2023
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- Content Jam, September 2023
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- Content Marketing World, DC, September 2023
- Marketing Analytics Data Science, DC, September 2023
- MarketingProfs B2B Forum, Boston, October 2023
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