INBOX INSIGHTS, August 11, 2021: Delegation, Instagram Influencers, Bias in Market Research

INBOX INSIGHTS: Delegation, Instagram Influencers, Bias in Market Research (8/11)

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How Delegation Goes Well or Badly

How can I delegate anything when no one else does it as I do?

This might be the most common question I get about process and change management. Remember last week when I told you that you might be the problem?

Here is a common example: doing the dishes. Because of schedules, I handle doing the dishes. You can say that this is my task. On the rare days that my husband is off from work, he’ll pitch in and do them instead. You can say that the task is delegated to him on those days.

Great to have someone to help, right? Except that his process isn’t exactly the same as mine. When I load the dishes into the dishwasher my goal is to maximize every square centimeter of space. Dishes get placed in a very particular, symmetrical, and orderly way. When he does the dishes, he does not do this. He loads the dishes into the dishwasher wherever they fit. He doesn’t take the painstaking time that I do arrange and rearrange dishes.

Does that mean he’s doing it wrong? Not at all. Why? Because he gets the same result when he does it his way. The dishes get cleaned.

Why am I prattling on about how the dishes get cleaned in my house? Because it is an example of how setting expectations can either blow up your night or result in a clean kitchen. I can choose to only ever do it my way. Or, I can let it go, trust him, and be happy that it’s done.

*My husband agreed to let me share this anecdote in the newsletter

You’re saying to yourself – that’s not the same as delegating to team members. Isn’t it? At the end of the day, you’re asking someone to follow a process that you have outlined. You’re asking someone to complete a task and get the same result that you get.

Let’s say you’ve tasked someone with creating an end-of-month report. They put a report together and you’re not satisfied with the result. So you take the task back and redo it in a way that you’re happy with.

Where could this have gone wrong and what are some potential solutions?

  • Did I set correct expectations of what I wanted?
  • Did I show examples?
  • Did I offer to answer questions along the way or did I say “do it”?
  • Did I outline the steps to completion?
  • Did I show the person where I wasn’t happy with the end product and walk them through how to fix it?

Did you notice something? All the questions are about what I did or didn’t do. Not the other person. Before you assume the other person can’t do it you need to stop and ask yourself what you contributed.

Will you run into cases where someone cannot perform the task as asked? Yes. But, that’s a topic for a different newsletter. Start with your responsibilities first and work from there. Good delegation takes work from both parties.

Successful delegation leads to successful change management. As much as we want to believe we can do it all on our own, that is rarely the case. Getting your team to help can be empowering and freeing for you, and them.

Have questions about change management, delegation, or what true crime novel I’m reading this week? Find me in our free Slack group, Analytics for Marketers.

– Katie Robbert, CEO

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Binge Watch and Listen

In this episode of In-Ear Insights, Katie and Chris talk about biases when conducting market research. Whether you’re conducting surveys, 1:1 interviews, or focus groups, what you bring into your research can dramatically affect – and even invalidate – the outcome. Learn how to mitigate these problems and keep your research as free of tainted influences as possible. Tune in to find out how!

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Last week on So What? The Marketing Analytics and Insights Live Show, we looked at the fundamentals of natural language processing. What is it and how does it work?

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Data Diaries - Interesting Data We Found

In this week’s Data Diaries, we share two things. First, one of the perpetual challenges we’ve had is classifying very large datasets. Sometimes, there’s no substitute for human effort, but the challenge is whether the human effort is worth the time invested. In our case, we had a long, long list of Instagram influencers and brands – but I knew the data was dirty, that things were in the wrong buckets, and not just a few. So I had a few different choices to make:

  1. Live with the data. Not the best choice when you know you’ve got dirty data.
  2. Clean it mechanically. My first thought was to build a scraper that would pull an Instagram profile picture and use a neural network to classify whether there was a human face in the photo or not (an excellent indicator whether a profile is an influencer or brand). I estimated this would take probably 20-30 hours of coding and testing.
  3. Clean it with human labor. Amazon’s Mechanical Turk, once you get the hang of it, allows you to scale human labor at relatively low cost.

I chose option 3. I already had 14,000 Instagram profiles ready for analysis, so I commissioned a Turk to have people rate them as a brand logo, a person’s face, or something else. Here’s the funny thing: machines are good at what people aren’t, but the reverse is also true. Recognizing faces is easy for us. It’s hard for machines. So in this case, it made more sense to use option 3.

Amazon Mechanical Turk

Within an hour, the entire job was done and the error rate was around 1% – which was phenomenal – at a cost of US$246.

Now, could I have built a business case for option 2? Yes. But the cost – dozens of hours at a very high bill rate – was many multiples more expensive than option 3 for a relatively simple classification task. The way I thought about this was that my billing rate for hourly work is US$1,000, so if the task would have taken me more than 15 minutes to code, we would have effectively lost money.

The key takeaway here is consider what the various costs are to do a job, and choose what’s most efficient, even if it isn’t the most fun or most appealing. Would I like to write that code? Yes, and maybe someday I’ll do it – but not today.

Onto the data. With our newly-cleaned up dataset, I wanted to check back in on engagement rates on Instagram, particularly for influencers as the topic has come up recently.

Instagram Influencer Engagement Rates

What we see is a continued, gradual descent of influencer engagement over the past year to date, starting at nearly 1.75% engagement and descending to around 1.2%. In general, influencers have fared much better than brands have, though the engagement decline continues to make things challenging for them; in early 2019, they averaged almost 3% engagement.

The key takeaway is that influencers are showing more declines in engagement, which means that your investment in them may diminish in terms of results they generate. Be sure you’re using as many tracking mechanisms as possible to closely monitor their performance on your behalf and understand the impact they’re making – and what the return on investment is. There may come a point where your ROI drops below that of other channels and strategies; when it comes close, pivot.

Methodology: Trust Insights extracted 1,011,757 Instagram posts from 10,379 influencers using Facebook’s Crowdtangle software. Influencers were identified as individual personal accounts by Trust Insights and validated with Amazon Mechanical Turk. The timeframe of the data is January 1, 2021 – August 8, 2021. The date of study is August 10, 2021. Trust Insights is the sole sponsor of the study and neither gave nor received compensation for data used, beyond applicable service fees to software vendors, and declares no competing interests.

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