INBOX INSIGHTS, March 1, 2023: Good and Bad Advice, Conventional Metrics

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How to Tell Good Advice From Bad Advice

People are willing to tell you want to do, whether you want to hear it or not. This means we often get a lot of really bad advice. Like, really really bad.

I asked our Slack Community, Analytics for Marketers, what the worst advice was that they ever received was. As usual, the community did not disappoint. Here are some of the highlights:

  • Trust no one. If you’re going to succeed, you have to do it by yourself, or else someone else will steal everything you’ve worked for.
  • You’re too nice, you need to be meaner.
  • You need to spend more time focusing on bringing new clients into the business instead of those who are already here.
  • The only reason people respect you is because you work with me.
  • If you are a good lieutenant everything else will take care of itself.
  • You don’t move forward with anything unless you’re sure about it.
  • Stop showing emotion, it’s a weakness.
  • You need to stop working on improving your skillset and start engaging in office politics. It’s the only way you are going to ever get anywhere.
  • You need to make sure the men in charge feel good in meetings, you’re right but you need to soften your tone.

That last one is especially gross, but sadly not uncommon. Do you see the pattern with this advice? Aside from it being flat out terrible and damaging, it’s screaming “I’m insecure!” When you’re getting advice, examine the source. Do you think they are trying to set you up for success or are they sabotaging you? There are a lot of people who will say things like, “Do as I say, not as I do” but actions speak louder than words. If the person who is dolling out advice is not someone you respect or aspire to be like, it’s probably not the right advice for you.

On the flip side, I asked our community to tell me what some of the best advice was that they received. Here are some of the responses:

  • Complaining and judging reveals more about your (poor) character than the person or object receiving the judgement.
  • Praise in public, correct in private
  • I was once told to always stay two steps ahead of your manager. This way you’re always prepared for when they may ask for data, a project update, etc. Not only does it show you as being responsive and prepared but if they’re being asked for an update from their boss, it serves them well too.
  • Second bit of good advice: keep a brag bin. Anytime you get a complimentary email, call, Slack, etc public or private, put it in a brag bin folder. This does two things: when you’re having a tough day it’s a good pick me up, and when it’s time for raises and reviews, it acts as proof points for your manager.
  • Be nice to people on your way up because you’ll meet them again when you’re on your way down.
  • If you’re a manager, you’re not responsible for any of the success. All praise should go to your team members, and you should recognize them often publicly.
  • It’s nice to be important, but it’s important to be nice.
  • College only gets you your first job, the rest is who you know.

Wow, what a difference! This set of advice has a lot to do with being thoughtful, empathetic, and a good person. Just like looking for red flags when you’re getting advice, look for the green flags too.

How do you know who to get advice from? As stated above, look at their actions. Do they behave in a way that you yourself would? Do their actions make you cringe? If your gut is saying, “this isn’t the advice for me”, trust it. Say thank you and move on.

Do you want to connect with a community that is full of good advice? Join our free Slack Community, Analytics for Marketers.

– Katie Robbert, CEO

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Data Diaries: Interesting Data We Found

In this week’s Data Diaries, let’s talk about analytics assumptions. I was asked recently about a specific web analytics metric, bounce rate, and what could be done to reduce bounce rate.

On the surface, this seems like a straightforward, logical question, right? Bounce rate – defined as a site visit when a visitor comes in and leaves without taking further action – is a bad thing, isn’t it, and therefore reducing bounce rate would be a good thing, right?

The question I frequently have about many of the best practices and advice around analytics is how they’ve been tested. There are certainly some websites where a high bounce rate is bad. There are other websites where it’s immaterial. Even on a website, there are certain types of content where bounce rate is irrelevant, like blog posts, and other types of content where it’s vitally important, like landing pages for paid campaigns.

The reality is we rely on a lot of “common sense” that may not be optimal for our particular situation. So what do we do? What’s the appropriate way to determine whether or not a piece of advice is right for us or not?

We verify our assumptions with data, of course! Here’s an example. Suppose we believe that bounce rate is important. How would we know whether it was or not – how do we test that belief? We’d start by putting a bunch of metrics into a spreadsheet – sessions, users, page views, etc. We’d add in other measures like average time on page, number of pages per session, etc. And of course, bounce rate. Then we use statistics to test if there’s even a correlation between bounce rate and the outcome we care about – conversions.

Here’s what that looks like:

Image of bounce rate analysis

What we see above is a minor positive correlation between bounce rate and conversions. We see the obvious – more visits, more sessions, more users correlates almost perfectly with more conversions. We see that scroll depth – how far down a page someone got – matters to conversion. And curiously, we see a faint correlation between bounce rate and conversions – the more people who bounce off our site, the more that correlates to conversion.

To be clear, that effect is very weak. It’s barely above statistical significance. But if the conventional wisdom were true, we should see a negative correlation – the lower the bounce rate, the higher the conversion, and we don’t see that here.

The key takeaway here is that assumptions about which metrics are good or bad must be tested with your specific data to understand how they affect you. What works for one company will not work for another, so the metrics that one company makes decisions by will invariably change as well.

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