Almost Timely News: 🗞️ How To Do Feature Engineering with AI (2026-07-12)

Almost Timely News: 🗞️ How To Do Feature Engineering with AI (2026-07-12) :: View in Browser

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What’s On My Mind: How To Do Feature Engineering with AI

This week, if you haven’t taken it already, please take my Reader Survey. I’ll show you how I use the data and what you could be using with your own survey data using feature engineering.

Part 1: What is Feature Engineering?

If you’ve never heard the term, feature engineering is all about deriving new data from the data we have. I mentioned it in passing in the June 28th newsletter, but it’s worth reviewing again.

Broadly, in any given data set, there are two kinds of features, continuous and categorical, which is fancy for numbers and not-numbers. If you’ve used tools like Adobe Analytics or Google Analytics, these are metrics and dimensions.

We broadly define a number as anything you can do basic arithmetic on; website sessions is a number. You can add, subtract, multiply, and divide that number. A not-number is the opposite; a company’s industry is a piece of information you can’t do math on.

This is important because, as with everything, there’s nuance. There are things that look like numbers but don’t function like numbers. For example, in North America, there’s a system known as NAICS codes, the North American Industry Classification System. A company like Trust Insights has NAICS code 541611, management consulting. But this is a label - doing math on that number changes its value and meaning in unhelpful ways. It looks like a number, but it doesn’t behave like one.

Likewise, there are things that don’t look like numbers but function like them, like the day of the week. We say Tuesday, and if your week starts on a Sunday, a Tuesday is the third day of the week. Time in particular is rife with messy number-like things that don’t behave consistently. You can add and subtract days from a date like 2026-07-12 but you don’t really multiply or divide them often.

We need to understand this to understand that non-numbers in particular are packed full of information that we can extract and turn into more information. And once we have that new information, we can do more with the data we already have.

Now, I’ll borrow from the June 28th newsletter to give you a concrete example fo feature engineering, of deriving new data from existing data. Take this datestamp: 2026-06-28. We see this as a date, as a single piece of data. But we can decompose it into:

• Year

• Month

• Day

• Week of year

• Day of year

• Weekend or weekday

• Day of week

• Day of month

• Week of month

• Day of quarter

• Week of quarter

• Month of quarter

• Quarter of year

Depending on what you’re doing, each of these new data points might be important and relevant, especially if, in the case of my reader survey, I’m looking for insights about who my readers are, how they behave, how I can serve them better, and if I’m blunt, how I can sell them more things. Knowing whether day of week or day of month or they of quarter changes their behavior might be important.

For example, if you work in B2B, month of year is really important. December is a month when a lot of folks don’t have a ton of sales activity going on in the Northern Hemisphere. People are winding down for the holidays; if I’m doing data analysis and I have datestamps, I can decompose my click rates with these additional data points to see if those points show any patterns I can’t observe from the datestamp alone.

In turn, this means that if I’m offering sponsorships to a sponsoring company, I might have to increase or decrease what I charge them based on the patterns I see in the data, but I won’t know that if I don’t engineer those features from the data.

Part 2: Why Feature Engineering Matters for Surveys

One of the most important reasons you need to know feature engineering for surveys is because every question you ask someone in a survey reduces the completion rate. The more questions you ask, the less participation you get. Thus, if we want high completion rates AND good quality data, we have to think about what we can feature engineer from the data we have. Never ask questions for information you can extract on your own.

The first step in the process is to inventory what data you do have. For the Almost Timely newsletter, what information do I already have that I don’t need to ask? I have email addresses, obviously. I have opens and clicks for each issue. I have dates and times for subscriptions and unsubscriptions. That’s already quite a lot of data I don’t need to ask for, which means the survey can be shorter.

In terms of what I do need to ask for, let’s start with the first and second questions of the survey - your email address, and then your title and company domain name. That seems like a weird combination, right? Why did I do this? Well, email is important because if you win the drawing next week, I need some way of contacting you. But isn’t email enough to tell me a lot? Yes, but a lot of folks subscribe to this newsletter with their personal email accounts (as you should!) and so cspenn@gmail.com doesn’t lend a whole lot of insights.

So I ask for job title and company domain name. Why this? Look at how much you can unpack from these two fields. First, job title tells us about seniority in an organization - a marketing coordinator typically has less purchasing power and authority than a marketing director. It also tells us about the role a person plays in their industry. An HR administrator or a financial analyst do ver