Almost Timely News: The Data-Driven Marketer (2022-11-20) :: View in Browser

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Almost Timely News: The Data-Driven Marketer (2022-11-20)

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What’s On My Mind: The Data-Driven Marketer

Let’s unpack what I think is the biggest misperception about the data-driven marketer:

You don’t have to be good at data to be a data-driven marketer.

This is 100% true. Why? Because it’s what you do with the data that matters.

You don’t have to be good at artificial intelligence or spatial mathematics to use Google Maps, do you? No. You fire up the app, get behind the wheel, and you drive the car to your destination using the guidance of the app. You are literally data-driven, but you didn’t do any of the data part.

Why would your marketing be any different?

What you have to be good at is using data to make decisions. What you have to be good at is putting experience and emotion and intuition and all those other factors that go into decision-making to the side for a bit so you can focus on making decisions using the data you have.

Someone else – a team member, an agency, a partner – can provide you with the data. As long as it’s good, as long as it’s correct, you can and should use it to make decisions – if you know what the data is telling you, what it means.

For example, take this data set from the Federal Reserve Bank.

FRED T10Y3M chart

This is the 10-Year Treasury Constant Maturity Minus 3-Month Treasury Constant Maturity. What this shows is the average yield of 10-year Treasury securities versus the average yield of 3-month Treasury securities. (A Treasury security is something like a savings bond)

What do you have to know about this data to make decisions from it? What it means, to be sure. Where the data comes from, whether it’s correct and quality, definitely. But do you need to be able to perform mathematics on it? No.

What it says is straightforward: people buy Treasury securities from the US Treasury department at varying yields and durations. It’s a way to save money and earn a little interest on it. When the economy is good, people will buy long-term Treasury securities – like 10-year bonds – because they feel confident they won’t need access to that money before the security matures in a decade. When the economy is bad, people will buy short-term Treasury securities – like 3-month bonds – because they feel they might need that money again sooner rather than later.

When people buy up Treasury securities, the government pays them back when the security matures. In effect, when we buy Treasuries, we are lending the government money at an interest rate – what’s called the yield. Treasuries like these are sold at auctions by the government; the government sells a 1,000 security for, say,950. Companies bid on securities at that price until the government says, okay, we’re all done with the 950 lot, next up for auction is the960 lot, and so on and so forth. The yield is the difference between what you bought it at ($960) versus what you can redeem it for in 3 months or 10 years.

The maturity spread, then, shows the difference between the short-term and long-term auctions. When the economy is good, the long-term rates will outpace the short-term rates because people want bigger returns on their investment and they can afford to have their money locked up for longer periods of time. The government will sell out of those auctions faster, but they tend to have larger starting yields to compensate people for locking up their money for longer.

This means the difference between short and long term will be positive.

When the economy isn’t good, investors will buy the short-term Treasuries much more than the long-term ones – and this means that the difference between short and long term will be negative.

That’s what the data says. We know where it comes from – the US government. And this data is quite reliable and open, so we don’t have concerns there. And now we come to the final part of being data-driven: understanding what the data means. This is the part where most data-driven efforts fall apart – not because we don’t have the data, but because we don’t know what to do with it. We don’t know what decisions to make from it.

The 10-year/3-month maturity spread is a leading economic indicator. Over the last 50 years, it has been one of the best predictors of a recession among publicly available data. When the spread is positive, confidence in the economy is high, things are good. When the spread is negative, confidence in the economy is lower, and a recession is on the way.

What the data tells us right now, at the tail end of 2022, is that a recession is underway. The rate is in the red, negative, and that means we need to make some decisions. What sorts of decisions? Decisions around budgets – like how much to spend on marketing. Decisions around strategies and tactics, especially if we have reduced staffing to contend with and no prospects for hiring more folks in the near term due to things like hiring freezes. Decisions around market research, to see how our customers are being impacted, and what we might need to do to retain existing ones and win new ones. After all, people buy for different reasons, and the reasons change in changing economic circumstances.

But the critical part of this entire example is that you had to do no mathematics at all, did you? You didn’t have to do anything other than look at the data, as long as you know what it said and more important, what it meant. You are now in a place to make decisions with your data: how to run your marketing and your business in a very probable recessionary environment.

Conduct this exercise whenever you’re dealing with any metric or KPI, with any data that’s important to you. What does it say? What does it mean? What will you do about it? That’s what it means to be data-driven – and everyone can be data-driven, not just the math aficionados. As long as you’re making decisions based on data, you are data-driven.

Got a Question? Hit Reply

I do actually read the replies.

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ICYMI: In Case You Missed it

Besides the new Google Analytics 4 course I’m relentlessly promoting (sorry not sorry), I would recommend the piece on the cookieless future.

Skill Up With Classes

These are just a few of the classes I have available over at the Trust Insights website that you can take.

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Get Back to Work!

Folks who post jobs in the free Analytics for Marketers Slack community may have those jobs shared here, too. If you’re looking for work, check out these five most recent open positions, and check out the Slack group for the comprehensive list.

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What I’m Reading: Your Stuff

Let’s look at the most interesting content from around the web on topics you care about, some of which you might have even written.

Social Media Marketing

Media and Content

SEO, Google, and Paid Media

Advertisement: Google Analytics 4 for Marketers

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Tools, Machine Learning, and AI

Analytics, Stats, and Data Science

All Things IBM

Dealer’s Choice : Random Stuff

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How to Stay in Touch

Let’s make sure we’re connected in the places it suits you best. Here’s where you can find different content:

Required Disclosures

Events with links have purchased sponsorships in this newsletter and as a result, I receive direct financial compensation for promoting them.

Advertisements in this newsletter have paid to be promoted, and as a result, I receive direct financial compensation for promoting them.

My company, Trust Insights, maintains business partnerships with companies including, but not limited to, IBM, Cisco Systems, Amazon, Talkwalker, MarketingProfs, MarketMuse, Agorapulse, Hubspot, Informa, Demandbase, The Marketing AI Institute, and others. While links shared from partners are not explicit endorsements, nor do they directly financially benefit Trust Insights, a commercial relationship exists for which Trust Insights may receive indirect financial benefit, and thus I may receive indirect financial benefit from them as well.

Thank You!

Thanks for subscribing and reading this far. I appreciate it. As always, thank you for your support, your attention, and your kindness.

See you next week,

Christopher S. Penn


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