Category: Marketing Data Science

  • You Ask, I Answer: Vetting Marketing AI Vendors for Bias?

    Tracy asks, “What are some questions you should ask vendors to better understand what data they use in their algorithms to make sure it’s not biased?” It’s not just questions we need to ask. Consider checking for bias to be like any other audit or due diligence. We will want to investigate the 6 main…

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  • You Ask, I Answer: Difference Between Fair and Unfair Bias?

    Gianna asks, “What’s the difference between fair and unfair bias? What’s the fine line?” Fair and unfair comes down to two simple things: laws and values. Statistical bias is when your sample deviates from the population you’re sampling from. Bias isn’t inherently bad unless it crosses one of those two lines. Can’t see anything? Watch…

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  • You Ask, I Answer: Third Party Data and Trustworthiness?

    Ashley asks, “If you choose to use public datasets for your ML models, like from Amazon or Google, can you trust that those are free of bias?” Can you trust a nutrition label on a food product? The analogy is the same. What’s in the box is important, but what went into the box is…

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  • You Ask, I Answer: Most Common Biases in Marketing AI?

    Elizabeth asks, “What’s the most common type of bias you see that we as marketers should be aware of?” There are so many to choose from, but I’ll start with two: confirmation bias, and selection bias. Confirmation bias corrupts the entire process by looking for a result that fits a predetermined conclusion. Selection bias corrupts…

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  • You Ask, I Answer: Data Democratization and AI?

    Jim asks, “I am skeptical of data democratization because the average decision maker does not understand data collection, transformation, integration etc. Doesn’t AI make this an even bigger problem?” It depends on how abstracted the decision-maker is. Certainly the pandemic has shown us the general population is completely incapable of parsing even basic scientific data,…

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  • You Ask, I Answer: Bottom of Funnel Content for Conversion?

    Summary In today's episode, I break down how to craft bottom-of-the-funnel content that matches the emotional drivers behind purchase decisions and backs them up with rational proof. Here's what this means for you. You'll align your offers with what buyers actually feel at the moment of purchase, lifting conversion rates without changing your product. You'll…

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  • You Ask, I Answer: Determining Facebook Ads Effectiveness?

    Jen asks, “How can brands find out which kind of Facebook Ads work best for them?” You’ll need to do a content assessment in 4 layers: – Audience content – Your own content – Competitive content – Landscape content Once you’ve done all 4, you’ll have an understanding of what the different concepts and media…

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  • You Ask, I Answer: What Grade Level for Website Readability?

    Tiff asks, “At what reading level should website copy be written? Is it the same as print?” The answer to this question depends on two things: your audience, and what readability score you’re using. There are 5 major readability scores: – Flesch-Kincaid grade level – words/sentences – syllables/words – Gunning-Fog index – words/sentences – complex…

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  • You Ask, I Answer: Measuring Content in the Customer Experience?

    Stephanie asks, “How can marketers measure if their content is improving the customer experience?” In the buyer’s journey portion of the customer experience, measure by pipeline acceleration; what content is moving people towards conversion? In the owner’s journey, look to your marketing automation data. Can’t see anything? Watch it on YouTube here. Listen to the…

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  • You Ask, I Answer: Measuring Content in the Customer Experience?

    Summary In today's episode, I break down how marketers can measure whether their content is improving the customer experience by analyzing both halves of the journey. Here's what this means for you. You gain a dual-framework measurement approach that helps you pinpoint which content drives new conversions and which content keeps existing customers loyal and…

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