INBOX INSIGHTS, April 27, 2022: Will AI Take Your Job, Brand Awareness, NPS Scoring - Trust Insights Marketing Analytics Consulting

INBOX INSIGHTS: Will AI Take Your Job, Brand Awareness, NPS Scoring (4/27) :: View in browser

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How to Know if AI Will Take Your Job

This is a common question with a variety of answers. To help simplify, I’ve created a 2×2 matrix to categorize the work that you do so you. This way you can better determine if you should worry about what AI can do.

Tada! The Trust Insights “Will AI take my job?” matrix!

Ok, the name needs work. That’s not important right now.

To plot your tasks and understand if AI can do it versus a human, here is how you read the matrix. In the top-left quadrant, you have tasks that are highly repetitive and low on creative thinking. Those are the tasks that AI will be able to do and honestly, you should be happy to part with them. In the bottom-left quadrant are the tasks that are not repetitive but also not creative. AI will be able to handle these tasks as well, with some programming for the variations. In the top-right quadrant are your tasks that are highly repetitive but also highly creative. The more creative thinking that you need, the more that AI will struggle to replicate it, even if it’s repetitive. The bottom-right quadrant is where you hope a lot of your tasks will fall if you’re concerned. These are the less repetitive and highly creative tasks. These are the tasks that are most safe from AI.

Here is how you use this matrix to examine your own work. First, write down all the things you’re responsible for. This can be as simple as “check the dashboard for data anomalies” or as complex as “create a strategic plan for marketing”. For this exercise, start with one week’s worth of tasks.

My list might look something like this:

Project manage weekly client deliverables
Record Podcast
Write newsletter open
Update editorial calendar for next week
Update marketing tracking sheet
Interview manager candidates
Write a sales pitch email
Create a 2×2 matrix for newsletter post

My next step is to plot these tasks on the “Will AI take my job?” matrix. You can label each task as “High” and “Low” for Repeatability and Creativity. The next version of my list looks like this:

High – Repetitive / Low – Creative

Update marketing tracking sheet

Low – Repetitive / Low – Creative

Project manager weekly client deliverables

High – Repetitive / High – Creative

Update editorial calendar for next week
Record Podcast
Write newsletter open
Write a sales pitch email

Low – Repetitive / High – Creative

Create a 2×2 matrix for the newsletter
Interview manager candidates

When I look at my to-do list this way I can breathe easily. My job is safe from AI this week. There are a few tasks that AI could do, but most of the items need human intervention and creative thinking. Could I have AI write the newsletter? Sure. If I didn’t care too much about the quality. The same is true of a sales pitch. AI can write that for me, but it won’t necessarily be my tone, my humor. These are choices you can make about your own work and what you feel is best for your work output.

The next time you’re wondering if your job is safe from AI, or if there is an opportunity to introduce AI into your job, use this matrix to see where your tasks fall. You may be grateful to give up some items on your list so that you can refocus your energy on bigger value items.

Do you have questions about what tasks AI can do?

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In this week’s In-Ear Insights, Katie and Chris answer a listener question about proving the value of brand awareness.

Sophie asks, “Our marketing strategy as a young startup is to drive sales, to try and make money. But how does brand awareness affect that? I could just run lead generation ads to try and get people to get bookings, but I know part of our marketing needs to be brand awareness and getting people to the website. If your focus is on sales, how do you communicate or justify brand awareness campaigns, if they won’t directly justify sales?”

We review what a brand is, where brand awareness fits in the customer journey, and how to think about measuring it. Tune in to find out!

Watch/listen to this episode of In-Ear Insights here »

Last week on So What? The Marketing Analytics and Insights Live show, we did a show just for our agency friends on how to pivot your clients to Google Analytics 4. Catch the replay here »

This Thursday at 1 PM Eastern, we’ll be looking at exploratory data analysis and how it applies to marketing data.

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Data Diaries - Interesting Data We Found

In this week’s Data Diaries, we’re going to take a look at last week’s newsletter. We asked you last week the following question:

How likely are you to recommend Trust Insights as a consulting firm to a colleague in the next 90 days?

This is a generic clone of what’s known as the Net Promoter Score™, a system devised by Fred Reichheld and the management consulting firm Bain & Company. The scoring method is usually done on a scale of 0-10, and the scoring breakout is done like this:

Net Promoter Graphic Graphic by Satmetrix

The calculation of a Net Promoter Score is percentage of promoters minus the percentage of detractors.

So why didn’t our poll follow the same format? It mainly comes down to philosophy. Zero to ten – even zero to five – are a lot of choices, especially for a fast poll. It requires a lot of cognitive processing on your part as the reader. How much would you recommend a company? The more choice you have, the more it taxes your brain and the less likely you will be to engage with the poll.

We also live in a world now with people willing to make fewer nuanced choices in general. People have become much more absolute in their thinking; shades of grey are largely lost. Things are great or awful, a product is five stars or none, a person gets a swipe left or right. This degradation of nuance means that a 0-10 poll is likely to yield a large pile of 0s and a large pile of 10s – and not much in between.

So we simplified the poll into three choices – likely, neither, and unlikely. That gives us the ability to preserve the categories of the Net Promoter Score while allowing you, the reader, to make a fast, simple decision.

The trade-off of using this method is that you lose some granularity. There’s a very small window of promoters in the original scoring mechanism – only 18% of the possible responses are promoters, while 63% of the responses are detractors. In our version, it’s 33% and 33%, so there’s a difference. Does that difference matter? Not in our use of it.

So how did ours turn out? The good news is that this is a super easy computation. Let’s say you have 100 responses – 10 promoters, 8 detractors, and 82 passives. The math is 10% – 8% – or a score of 2. The range is -100 to +100; if you had 1 promoter, 1 passive, and 98 detractors, you’d have 1% – 98%, or a score of -97.

What’s a good score? Various folks who have made use of the NPS score over the years have said +50 or above is excellent. Above zero is okay. Below zero isn’t good, and below 50 is really, really bad.

How did we do? 39.7% promoters, 36.6% passives, and 23.7% detractors, leaving us with a score of 16. We’re above zero, but we’re not real close to 50.

Here’s the big question, a question that neither the original nor our modified version answer well:

So what?

Whether you got a score of -97 or +25 or +89, the score itself is a single data point. It’s a snapshot of a number with no context. From this score alone, we can’t make a decision – it doesn’t tell us what to do differently, if anything. This is also one of the major criticisms of the Net Promoter Score™ methodology. It’s a barometer of sorts, but it doesn’t tell you anything actionable by itself.

There’s some nuance; if your passives are a huge pool and you have very few detractors or promoters, then that tells you people don’t feel very strongly about you one way or another. And obviously, if either promoters or detractors is a disproportionately large number, you know something’s either very right or very wrong, but beyond that, there’s not much else to conclude. Over time, this data could show trends; a score that increases over time means you’re providing more value to your audience.

Ultimately, NPS is a lagging indicator of the value you provide. If you choose to use it in your overall marketing analytics mix, just know that it doesn’t appear to have much predictive power, not much capability to tell you what to do better. It lacks any granular details or qualitative data to explain whatever score you achieve – that has to be filled in by separate market research efforts.

Going forward, we’ll be using it on a more regular basis in the newsletter, so expect to see this poll every so often throughout the year – a data point isn’t all that helpful, but a data series where we can compare and contrast over time is.

This is a roundup of the best content you and others have written and shared in the last week.

SEO, Google, and Paid Media

Content Marketing

Data Science and AI

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