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Should You Quit Marketing and Become a Data Scientist Instead? (Thinks Out Loud Episode 261) — Headlines and Show Notes
Given the rise of data science in marketing, whether for personalization, AI, predictive analytics, or whatever comes down the pike next, you wouldn't be criticized for asking whether you should quit marketing and become a data scientist. But is that really a good idea? Is the future of marketing nothing more than writing algorithms? Or is there a future for creative people who focus on the customer in total.
The latest episode of Thinks Out Loud looks at whether you should quit marketing and instead focus on becoming a data scientist – and how you can best succeed no matter what the future of marketing, or data, holds.
Want to learn more? Here are the show notes for you:
- Data is the Crown Jewels: What That Means for Marketers Today (Thinks Out Loud Episode 239)
- Why are marketers afraid of data? – Biznology
- AI and … Pizza? – Biznology
- “How To Measure Anything: Finding the Value of Intangibles in Business” by Douglas Hubbard
- Will Digital Turn Every Business Into a Service? (Thinks Out Loud Episode 235)
- Who Owns the Customer? Marketing or Digital? (Thinks Out Loud Episode 226)
- How Badly Has Facebook Effed Up? (Thinks Out Loud Episode 234)
- How Marketers Can Put Data to Work Next Year – Biznology
- Maximizing Data to Put Personalization to Work for Your Property
- Maybe Facebook's Data Problem Is Your Data Problem (Thinks Out Loud Episode 203)
- Why Mobile and Data Go Hand-in-Hand for Marketers (Thinks Out Loud Episode 253)
- Why Data Is Overrated (It's Not What You Think) (Thinks Out Loud Episode 240)
- The History – and Future – of Trust in Digital Marketing (Thinks Out Loud Episode 241)
- The Lessons Marketers Must Learn From GDPR (Thinks Out Loud Episode 219)
- SearchChat: Can You Personalize Without Creepy Data? – Biznology
- 6 Stellar Insights into Personalization for Hotel Marketing: Hospitality Marketing Link Digest
- What the 2019 Mary Meeker Internet Trends Report Means for Digital Marketers (Thinks Out Loud Episode 248)
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Past Insights from Tim Peter Thinks
You might also want to check out these slides I had the pleasure of presenting recently about the key trends shaping marketing in the next year. Here are the slides for your reference:
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Running time: 13m 33s
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Should You Quit Marketing and Become a Data Scientist? (Thinks Out Loud Episode 261) – Transcript
Well, hello again everyone and welcome back to Thinks Out Loud, your source for all the digital marketing expertise your business needs. My name is Tim Peter and this is episode 261 of the big show. I think we've got a really cool show for you today, a lot of interesting stuff to talk about.
I want to start with a conversation I've been having with a number of marketing professionals lately around data, and what you as a marketer really need to know about data and how much you need to care about data, and I want to be really clear about this. Data is incredibly important when we talk about marketing today, you know, personalization and artificial intelligence and all of the many things that are going to make marketing more effective in 2020 depend on data.
But I think when we have that dialogue, a lot of marketers think they need to be data scientists, and I don't want to suggest that data isn't important. But what I do want to suggest is that it's not your job to be the data scientist. Instead, it's your job to ask the right questions of the data scientists.
I mean, if you want to be a data scientist, by all means you should. It's a great field. It's really interesting. You will be endlessly employable for the next, oh I don't know, decade or so. But your job as a marketer is to think about the business implications, to think about the implications for customers, to think about the customer experience.
I think of data, I think of the way companies should look at data almost like a two-by-two matrix, kind of like a BCG group matrix where the axes are on the, on the X-axis, do we have the data and on the Y-axis, do we know what questions to ask, right? And I sort of presuppose these are yes-no questions, but it clearly, it's going to be more of a spectrum.
You could do a two-by-two, you could do a three-by-three, but fundamentally it comes down to one of four positions, which are:
- Yes we have the data and yes, we know what questions to ask;
- Yes we have the data and no, we don't know what questions to ask;
- No, we don't have the data and yes, we know what questions to ask; and of course
- No, we don't have the data and no, we don't know what questions to ask.
Now I think too many companies spend their time worried about "no we don't have the data, but we know what questions to ask" and "no, we don't have the data and we don't know what questions to ask." And I think that's kind of a mistake. Now obviously if you don't have data and you know what questions to ask, so number three on my list, right? No, we don't have the data, and yes, we know what questions to ask then you need to go get the data.
But if you think about Douglas Hubbard's essential book, "How to Measure Anything: Finding the Value of Intangibles in Business," which I've talked about before, Hubbard points out a series of rules for data that include 1) you have more data than you think, 2) you need less data than you think, and 3) new data is more readily available than you think.
So if you know the questions to ask and you don't have the data, getting the data itself is not the hard part. You know, it's become very popular to say data is the new oil and I don't think that's true. Data is not oil because oil is hard to come by. Insights are the new oil. Insights require the mining and the digging and the prospecting that you would expect to do if you were actually in the business of, I don't know, going out and exploring an oil field. But you have a ton of data and typically getting meaningful answers is easier than you think it is from the data that you have, excuse me, getting new data is easier than you think it is.
I saw a really interesting thing the other day that Google will let you automatically purge data. You can go into your settings in gmail or YouTube or Google Search, and it will automatically purge data after three months or after 18 months. And what's interesting about that to me is how specific those periods are. You know, why doesn't Google let you purge data after one month? Why doesn't it let you purge it after six months? Why doesn't it let your purge it after 12 months? It's either three or it's 18. Maybe that's just easier to program, but I would bet that the recency of the data that they want suggests that the data Google collects, they've probably found gets less useful as it gets old. It loses its predictive power. So they want to keep it for at least three months so they can learn something from it, and ideally 18 months or longer, but I bet after 18 months they don't really care because it probably doesn't tell them much.
So what's more interesting is yes, we have the data, but no, we don't know what questions to ask. And for you as a marketer, that's not a data science problem. That's an insight problem. That's being able to think about how you want to help your customer, and the kinds of products and services you want to offer, and the way you want to promote that, and the channels in which you want to sell it and the way you want to price it. That's where the really fascinating parts come in. Not that data science is not fascinating, but it's the kind of thing that you as a marketer can do with a better understanding of your customers.
Look at examples of companies where success seemed obvious in hindsight. We all know tons of these questions of these examples, but why were they successful and why did it seem obvious in hindsight? Because they knew how to ask the right questions and how to formulate the right thesis about what it was they were trying to do.
If you think about Uber, they had a fundamental insight about the quality of taxi services and the utilization of black car service. You know, there were lots of cars sitting idle. Why don't we connect the driver and the rider? Yes, it took data, but the fundamental insight was, man, there's a lot of cars sitting around and man, a lot of passengers who were unhappy with the quality of the service they're getting. Obviously they followed that up with peer-to-peer. What we think of as Uber today, UberX, actually didn't come around until two years after the company started and Sidecar and Lyft really started the peer-to-peer concept, but again it came down to asking can we make the drivers more useful and can we make the passengers more happy, right? I mean that's, that's fundamentally the really cool thing.
And I want to be fair, I'm definitely for purposes of this discussion, ignoring Uber's less than savory behaviors, but the point isn't to lob at the company for its ethics or behaviors, only to note their early insight and those of other folks in understanding, hey, we've got the opportunity for a two-sided market here. How do we put those together?
If you think of Airbnb, very similar concept, this time just for hospitality. If you think of a Stitch Fix, great company understood changes in shopping behavior. People have less time, people need a little bit more support, and so just went to a very simple model that has made them profitable since 2014.
And if you go much further back, you know, look at Amazon. My favorite story about Amazon is Amazon didn't set out to be a bookstore because Jeff Bezos had some abiding love of books. But instead, according to an article on entrepreneur.com, he drew up a list. Jeff Bezos drew up a list of 20 potential products he thought might sell well via the internet, including software, CDs, and books. Now I'm reading directly from this writeup. "After reviewing the list, books were the obvious choice, primarily because of the sheer number of titles in existence. Bezos realized that while even the largest superstores could stock only a few hundred thousand books, a mere fraction of what is available, a virtual bookstore could offer millions of titles."
Notice what they said. Books were the obvious choice. Were they? I mean clearly selection played a role and clearly so did the ease of shipping books. But if it was so obvious, why didn't Barnes & Noble or B. Dalton or Borders get there first? And the answer is not because they didn't have data that would tell them this would work, but because they didn't have the insight. They didn't ask the key question that might have made, I don't know, Borders be the Amazon of today. They missed the mark and it wasn't because they didn't have the data. It's because they didn't ask the right question.
So when you're thinking about how can I as a marketer use data to personalize, or use data to empower artificial intelligence and the like, think in terms of do we have the data, and more importantly, do we know what questions to ask? Because if you can do that well, you're going to do great regardless of what happens five years down the road, 10 years down the road, and that's a much better place to be.
Now looking at the clock on the wall, we are out of time for this week, but I want to thank you again so much for tuning in. I genuinely appreciate it and I want to remind you that you can find the show notes for today's episode as well as an archive of our past episodes by going to timpeter.com/podcast. Again, that's timpeter.com/podcast. Just look for episode 261.
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With that, I want to say thanks so much for tuning in. I appreciate it as always. I hope you have a great rest of the week, a wonderful weekend ahead, and I'll look forward to speaking with you here on Thinks Out Loud next time. Until then, please be well, be safe, and as ever, take care everybody.