INBOX INSIGHTS, March 31, 2021: In The Headlights is now INBOX INSIGHTS, the Trust Insights Newsletter - Trust Insights Marketing Analytics Consulting

In The Headlights is now INBOX INSIGHTS, the Trust Insights Newsletter

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In the Headlights is now INBOX INSIGHTS

It’s time for some spring cleaning, and we’re putting away the newsletter we’ve been writing for the past three years. In the Headlights is now INBOX INSIGHTS. Why the change? In the early days of the company, there was a lot of driving-themed stuff on the too-clever-for-our-own-good premise that we’d focus on data-driven marketing. A funny thing happened along the way: all marketing became data-driven by necessity. From pandemics forcing everyone to get creative to technology advancements that left our heads spinning, all marketing has had to become data-driven to some extent, rendering the phrase somewhat redundant. It’s the same as “Internet marketing” – completely redundant, because if you’re not using the Internet for some of your marketing, you’re not really doing an effective job of marketing.

We’ve also made a few obvious changes, like naming section headers to be less clever and more blatant, because obvious is in fashion again. Now, onto something useful.

On Proper Surveying

Over the past week, I’ve been reviewing submissions to an awards event I’m participating in as a judge. I’m specifically judging the data and analytics category, and several of the submissions have been campaigns built around surveying. When it comes to surveys, there are many more ways to do them wrong than right, and market research is a profession unto itself.

In these awards submissions, I’ve seen three glaring types of errors that we should all work to prevent in our own surveying and research: incomplete methodologies, improper survey design, and incorrect results interpretation. What goes wrong?

Incomplete methodology. When we publish surveys, we also need to publish our methodologies, which are summaries of the 6Ws – who did we survey, what did we ask, where did we get our sample population from, why are we publishing the survey (including disclosing competing interests), when did the survey run, and how did we conduct the survey.

Too many organizations omit all these details, and without them, we cannot judge how credible the survey results are. The gold standard for survey methodologies and disclosures is the AAPOR Code of Ethics, which I strongly recommend every marketer who conducts surveys to read and adhere to strictly.

Improper survey design. Writing good survey questions is an art unto itself as well. Many surveys designed by marketers fail to encompass the range of responses that survey participants could give, and some are even so bad as to be leading questions that provide invalid data.

For example, suppose we were asking you a question about analytics, and the survey question was, “How important is an analytics consulting agency like Trust Insights?” You can see there’s already a bias, a presumed emphasis here, when a better survey question would be, “What methods, if any, do you use to manage your analytics?” of which one of the responses could be an analytics consulting agency. If you want to improve your question design skills, be sure to read this very thorough methodology from Pew Research.

Incorrect results interpretation. This is where many surveys hit the rocks and come apart. Too many people who run surveys don’t understand how to interpret the results, especially things like confidence intervals, and as such, draw incorrect or invalid conclusions from the results.

Let’s say you are surveying the United States population about a topic and you sample 1,200 people. What’s your margin of error, your confidence interval? At a 95% confidence level, it’s about 2.83%, which means that any answer which is within 2.83% of another answer is statistically identical. So if you asked, “Which color is your favorite, red or blue?” and 51% of people said red, and 49% of people said blue, it’s just as likely, from a statistical perspective, that up to 51.83% of people said blue and as few as 48.17% of people said red – so you can’t tell the difference.

Suppose you wanted to them break that down by gender and you had a 50/50 split. Your margin of error ballons to 4%. Suppose you wanted to focus on teenage females. If that were 200 of the 1200 responses, your margin of error expands even more to 6.93%. Even if your results were 55% saying red and 45% saying blue, there’s a statistical possibility that 48.07% said red and 51.93% said blue, so you couldn’t declare a winner.

Getting surveys right requires lots of study and experience. They’re useful, powerful tools when used correctly, but like Uncle Ben in Spider-Man said, with great power comes great responsibility. Make sure to survey properly, and if you don’t know how, bring in an outside research firm to help you.

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In this week’s In-Ear Insights, Katie and Chris discuss awards. Do awards matter? Should they be something we invest time and treasure in acquiring, or are they just pointless vanity? Tune in to hear our conclusions!

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

Last week on So What? The Marketing Analytics and Insights Live Show, we discussed the off-the-shelf attribution models built into software like Google Analytics, Google Ads, and Facebook Ads. Catch the replay on what the different models mean and how they fit into your marketing.

Watch/listen to this episode of So What? here »

This Thursday at 1 PM Eastern, we’re going to tackle dark traffic in your Google Analytics – what it is, where to find it, and how to start teasing out what it might be. Join us on our YouTube channel!

Are you subscribed to our YouTube channel? If not, click/tap here to subscribe!

Data Diaries - Interesting Data We Found

The column formerly known as Rear View Mirror is now Data Diaries.

This week, let’s do a bit of macro data examination and look at a key pandemic indicator: air travel. The best leading indicators aren’t speculative in nature; they have something that has a real cost, a real burden so that you know the indicator is based in reality. For example, the price of shipping containers isn’t speculative – companies only buy space on a cargo ship when they actually have something to move. Likewise, people don’t go through airports just for the sake of going through an airport. They go through airports because they’ve bought a plane ticket and have somewhere to go.

That’s what makes passenger air travel so valuable as an indicator – there’s a real, tangible cost to getting on a plane and going somewhere. With that, let’s take a look at the United States Transportation Security Agency (TSA) Passenger Throughput numbers, the number of people who have gone through TSA security checkpoints at airports in the last 2 years:

We see the impact of the pandemic in stark relief in March and April of 2020, when the world literally almost came to a stop, and then over time, people began traveling again. We’re still about a million passengers per day fewer than the same time two years ago, but it’s clear that more people feel comfortable traveling now than even just 3 months ago.

What’s the key takeaway? As the pandemic slowly draws to a close over the coming months, economic indicators like air travel, cargo shipping containers, and other data series will help us predict and understand how quickly the economy is following suit. For some industries, these indicators will be KPIs unto themselves; anyone in travel and tourism is or should be watching them like a hawk. For others, like us, they’re a part of the big picture, part of understanding the ripple effects that lead to less or more business, faster or slower business decisions.

Methodology: Trust Insights exported and normalized data from the TSA Passenger Throughput monitor. The timeframe of the data is March 30, 2019 – March 30, 2021. The date of study is March 31, 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.

Do you want to understand data science better as a marketer? Would you like to learn whether it’s the right choice for your career? Do you need to know how to manage data science employees and vendors? Take the Data Science 101 workshop from Trust Insights.

In this 90-minute on-demand workshop, learn what data science is, why it matters to marketers, and how to embark on your marketing data science journey. You’ll learn:

How to build a KPI map
How to analyze and explore Google Analytics data
How to construct a valid hypothesis
Basics of centrality, distribution, regression, and clustering
Essential soft skills
How to hire data science professionals or agencies

The course comes with the video, audio recording, PDF of the slides, automated transcript, example KPI map, and sample workbook with data.

Get your copy by clicking here or visiting TrustInsights.ai/datascience101 »

Interested in sponsoring In The Headlights? Contact us for sponsorship options to reach over 16,000 analytically-minded marketers and business professionals every week.

The column formerly known as Shiny Objects is now Weekly Wrapup. This is a roundup of the best content you and others have written and shared in the last week.

Data Science and AI

SEO, Google, and Paid Media

Content Marketing

Are you a member of our free Slack group, Analytics for Marketers? Join 1600+ like-minded marketers who care about data and measuring their success. Membership is free – join today. We also post hundreds of job openings sourced from around the Internet every Wednesday, so if you’re looking for work, join the Slack group!

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Read our disclosures statement for more details, but we’re also compensated by our partners if you buy something through us.

Where can you find us in person?

MarketingProfs B2B Forum, April 2021, virtual
HighEdWeb Analytics Summit, April 2021, virtual
ContentTech Summit, April 2021, virtual

Going to a conference we should know about? Reach out!

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