INBOX INSIGHTS: Data Before Decisions, Recession Indicator (1/11) :: View in browser
Review Your Data Before You Make Decisions
I love data. That’s a given. But more than the data itself, I love the story and the action. I’m a problem solver, a fixer. If I can’t figure out what to do, I dig deeper.
Over the weekend, I was trying to determine if I was taking care of my indoor plants properly. I have dreams of creating a jungle inside of my home. I have an app that allows me to take a picture of the plant and the app will diagnose the issues. Unfortunately, the best advice it gives is that I’m either watering too much, or not enough. This is hugely unhelpful.
It got me thinking about how we do the same thing with out marketing. If you’ve been playing around with tools like ChatGPT you know that you have to ask it for something before you get an output. If you’re delegating to a team member, you need to tell them what you need before they can do it.
Last week on the Livestream, we covered prompt engineering. You can watch that episode here.
As we talked about it, we were able to draw the parallel between creating a prompt for a machine and delegating a task for a human. You need to be as specific as possible.
To get specific, you need to know what you’re working with. That’s where the data comes in. If you’re not familiar, this is the marketing maturity model.
Most of us, most companies, are stuck at the first step, the “What Happened?” stage of data collection. Even with that, many of us don’t collect enough data to help us get specific enough. We want to jump ahead from data collection straight into action. We want to skip the “boring” stuff like requirements and analysis and do something. I get it. We want to start playing with the AI tools and ignore the rest of our marketing tactics that need our attention.
But what if the action you’re taking will make things worse? What if it’s an expensive decision? What if people lose their jobs because you were impulsive?
Now, in my case, I’m trying to make sure I keep my house plants alive. Lives and jobs are not at stake if I can’t keep my philodendron alive. However, if I start making decisions for Trust Insights without understanding the data, I could very well be costing us the health of the company.
So what do we do? Start with user stories. Start with requirements.
As a reminder, a User Story is a simple statement that helps you understand the problem you’re trying to solve. It help you focus and points you in a direction.
As a [persona], I [want to], so [that].
In my case, “As a plant owner, I want to water my plant properly, so that it will grow larger”. To make sure that I’m doing that, I have data to collect. I need to get a set of requirements for the idea situation for the plant’s environment. What is the right amount of sunlight, water, fertilizer? I need the recipe. I also need to understand what I’m currently doing so I know what to course correct.
If we bring the example closer to what you’re doing with your marketing, you need to understand your data before you can take action on it. You need to understand the problem you’re trying to solve so that you explain the correct recipe. Now that the new year is underway, ask yourself the last time you audited your data collection processes.
Do you feel confident that you’re collecting the right data to answer the question you’re asked?
You don’t have to wait for a new year to audit your data. Why don’t you build it into your roadmap to review on a regular basis? Like watering your plants on a schedule, you should be reviewing your data regularly. Getting yourself into a routine will cut down your odds of getting it wrong and/or the problem coming unfixable. By performing tasks on a schedule, you can find issues and fix them before they get out of hand.
For what it’s worth, I was overwatering my plants but I have been able to rectify the situation and they already look healthier.
Want to tell me how you’re auditing your data (or taking care of your plants)? Reply to this email or join our Free Slack Group, Analytics for Marketers.
– Katie Robbert, CEO
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In this week’s In-Ear Insights, Katie and Chris discuss the differences between data science and data analytics. They delve into how data science focuses on exploring why things happen, while data analytics focuses on what happened. They also touch on the importance of requirements gathering and the role of data engineering. Tune in to the full episode to learn more about these topics and the overlap between data science, data analytics, and data engineering.
Last week on So What? The Marketing Analytics and Insights Livestream, we showcased how to do prompt engineering to best work with systems like ChatGPT and Stable Diffusion. Catch the episode replay here!
This Thursday at 1 PM Eastern on our weekly livestream, So What?, we’ll be looking at how to use predictive analytics to build a marketing budget. Are you following our YouTube channel? If not, click/tap here to follow us!
Here’s some of our content from recent days that you might have missed. If you read something and enjoy it, please share it with a friend or colleague!
- In-Ear Insights: Data Science vs. Data Analytics: What’s the Difference?
- So What? How to do prompt engineering
- INBOX INSIGHTS: Agents of Change, 12 Days of Data (1/4)
- In-Ear Insights: Secrets of the C-Suite
- INBOX INSIGHTS, December 21, 2022: Happy Holidays, Holiday Gift Book
- Almost Timely News, January 8, 2023: Best Practices and Recipes
Take your skills to the next level with our premium courses.
Get skilled up with an assortment of our free, on-demand classes.
- Empower Your Marketing with Private Social Media Communities
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- What, Why, How: Foundations of B2B Marketing Analytics
In this week’s Data Diaries, let’s check in on a data point we haven’t looked at since the middle of last quarter: the best recession indicator available to us, the 10-year to 3-month US Treasury maturity spread.
You may recall that this indicator has successfully signaled impending recessions in the USA ever since 1970, and more critically, has not sent up false positives for recessions that didn’t happen.
As a reminder, this indicator is the difference, the spread between the maturity rate of 10-year US Treasury securities and 3-month US Treasury securities. What it tells us, briefly, is whether investors feel confident in the economy. When they do, they put their money into long-term investment vehicles like a 10-year Treasury. When they do not feel confident in the economy, they prefer to buy short-term investment vehicles so that they can get their money back faster (albeit with lower returns).
So, where are things?
Above, we see in the red portion of the line chart the dotcom bust in the early 2000s, the Great Recession of the mid-2000s, the early days of the pandemic, and… today. The indicator of a recession is now very, very heavily in negative territory, which presages a recession period. In fact, this indicator is deeper into negative territory than any of the previously mentioned events. Why, we can’t tell from the data, though we can hazard a few guesses – global political instability, substantial global climate change disasters, ongoing supply chain issues, ongoing pandemics. There’s no shortage of reasons why investors might be more than a little spooked.
So what? What do you do with this information? If you manage a company, now is the perfect time to dust off your scenario planning. As Katie likes to say, at a minimum, you need three scenarios planned out: best case, worst case, and business as usual. If a recession hits hard, what’s your plan? If the recession turns out to be just investor nervousness and the broader economy is just fine, what’s your plan? If the major forces of instability vanish and the world finds itself on solid footing again, what’s your plan?
Regardless of your management status, now is the time to play it a little safer when it comes to your money. Hedge your bets, maybe put off that big investment if it’s not necessary. If you’ve got planned expenditures in your budget, now’s the time to start looking for deals; the power balance is now more in favor of the buyer than the seller, generally speaking. (this is obviously subject to industry and product specifics)
Methodology statement: Trust Insights downloaded data from the St. Louis Federal Reserve Bank’s FRED API. The timeframe of the data is January 1, 2000 – January 11, 2023. The date of study is January 11, 2023. 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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Here’s a roundup of who’s hiring, based on positions shared in the Analytics for Marketers Slack group and other communities.
- Analytics Manager at Omnicom TRKKN
- Data Analyst at Migros Online
- Manager, Analytics at Shopstyle
- Senior Data Engineer / Analyst at iTrinity
- Senior Data Scientist at Microsoft
- Social Media Coordinator (Contract) at Podglomerate
- Vp Of Marketing at Yottaa
- Web Analytics Engineer at Brack
Are you a member of our free Slack group, Analytics for Marketers? Join 2900+ like-minded marketers who care about data and measuring their success. Membership is free – join today. Members also receive sneak peeks of upcoming data, credible third-party studies we find and like, and much more. Join today!
Are you ready for the new year? With “The 12 Days of Data 2022”, you’ll get an in-depth look into the year that was 2022 in data analytics and what trends might carry over into 2023.
We’ll explore the latest trends in social networks like Tiktok and Instagram, give you economic data to help you decide whether a recession is looming, and more – all for free!
Plus, it’s available in PDF or ePub formats, plus machine-read MP3 audio so you can enjoy the content in any way you choose. Get the insights you need now with “The 12 Days of Data 2022”!
Where can you find Trust Insights face-to-face?
- Martechopia, March 2023, London
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