Jen asks, “How can brands find out which kind of Facebook Ads work best for them?” One way to approach this problem is with large scale data analysis. In your industry, gather up a list of Facebook Pages and use any service which can address the Facebook API like Facebook’s Crowdtangle, then filter to only…
Summary In today's episode, I explain how brands can use large-scale data analysis to figure out which Facebook ads actually work in their industry. Here's what this means for you. You can stop guessing about ad creative and instead mine competitor posts, tag them for themes, and test data-driven variations against your normal ads. You'll…
Heidi asks, “TED.com published a list of top 10 careers to stay employed through 2030 including Socially Distanced Office Designer and Virtual Events Planner. What do you make of their predictions?” The careers listed are too short term. They’re pandemic-centric, and while the pandemic will be with us for a couple of years, it won’t…
Summary In today's episode, I critique TED.com's top 10 careers list for 2030 and explain why AI-driven roles offer more durable opportunities than pandemic-focused ones. Here's what this means for you. You get a longer-term framework for spotting careers that survive well beyond the next couple of years. You'll also learn these concepts: how AI…
Wanda asks, “How do I know if my A/B test is statistically significant?” Statistical significance requires understanding two important things: first, is there a difference that’s meaningful (as opposed to random noise) in your results, and second, is your result set large enough? Watch the video for a short walkthrough. Can’t see anything? Watch it…
Summary In today's episode, I walk through how to determine whether your A/B test results are truly meaningful or simply random chance. Here's what this means for you. You gain a clear two-step framework for validating any test before making business decisions on it. You'll also learn these concepts: how a proportion test reveals whether…
Erika asks, “What are your tips and best practices for topic and keyword research in content marketing?” It depends on the size of the content and how much domain expertise you have. Scale your research efforts to the level of risk the content poses and how important it is that you get it right. Can’t…
Summary In today's episode, I walk through how to approach topic and keyword research for content marketing based on risk level, domain expertise, and content size. Here's what this means for you. You gain a framework for deciding how much research depth your content actually requires before you ever open a keyword tool. You'll also…
Hannah asks, “For attribution modeling, what model do you use on your Facebook campaigns?” Facebook offers substantially similar attribution models as Google Analytics; most of these will fail to give you an accurate picture of every digital channel. Unsurprisingly, Facebook’s models tend to paint Facebook in the best light possible; their data-driven attribution model, for…
Summary In today's episode, I walk through Facebook's attribution models, their limitations, and which model to choose based on your customer journey. Here's what this means for you. You'll understand which attribution approach fits your business and why Google Analytics often gives a more honest picture than Facebook's built-in tools. You'll also learn these concepts:…
