Some of the areas that Ian touches upon include:

1. Why Ian got interested in Analytics
2. What is Marketing Analytics
3. Examples of projects someone in this function might work on
4. Stages in a typical project
5. How analytics should drive decision making
6. Typical day in the life
7. How is success measured - timeliness, quality of analysis and ability to communicate
8. Interesting and challenging aspects of the job
9. Examples of tools he uses - R, SQL, Python, Visualization tools
10. Not so interesting aspects of this job - how if your insights are unexpected, there can be a lot of resistance, and you really need to figure out how to persuade people
11. Difference between Analytics and Data Science
12. Career progression
13. Skills/Qualities in someone who is good at this job - comfort with numbers, ability to learn new tools and languages needed for the job, communication and curiosity
14. Typical background
15. Helpful resources for interested candidates
16. 3 questions you can ask yourself to assess if you will like the job 
17. Tips for applying for similar jobs

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Detailed transcript of the discussion:

 

[Learn Educate Discover] 02:36

Thank you so much for taking the time. Can you give us a very quick summary of what Twilio does for people who might not be familiar with the company?

 

[Ian]   03:22

Sure thing. So Twilio is a communications platform for developers and companies who want to engage with their customers in different ways. So say, you get a text message from somebody, or you say you order something online, and you get it delivered to your apartment and you get the notification, you get a text message that says your item has been delivered. Twilio powers certain application developers to include those kinds of features in their apps, so that they can focus on actually being able to do the development of the service rather than worrying about the communications back end.

 

[Learn Educate Discover] 04:04

Yeah, I've heard it's very popular. So you're doing marketing analytics now at Twilio - How long has it been?

 

[Ian]   04:25

I've been here for almost a year and a half. 

 

[Learn Educate Discover] 04:31

Can you give us a very quick summary of your background and what got you interested in marketing analytics?

 

[Ian]   04:42

Sure. So it goes back to my undergraduate - I was an electrical engineer. I did hardware engineering for a couple of years. I worked for the International Space Station, which was super fun. It was my first job out of college. I I did it for just a little bit over two years. But I did miss actually working for customers, which actually was what led me to Wharton to get my MBA. And during that time, I really discovered my passion for analytics. I think, coming from that engineering background, I discovered that I already had that affinity for numbers. And this field seemed very fitting for that. And then also, the two things that interested me were tech and entertainment, which led me to actually after graduating from Wharton going to work in video gaming. And that's where I actually got my practical knowledge of analytics before, this is my first real job in the field. And I did everything from like game design, operations, analytics, to marketing analytics to Strategy Analytics. And I'm more than happy to talk through the differences between those if you have those questions. But then, as luck would have it, I was looking for opportunities in the Bay Area. And I came across Twilio, at the perfect time, they were just announced, they're going public. And they were looking to hire a marketing analytics manager. And I fit the bill for what they were looking for, and I've been doing it ever since.

 

[Learn Educate Discover] 06:14

Awesome. All right. So I, I will get to analytics. But I'm so curious, what did you do for the International Space Station?!

 

[Ian]   06:22

Sure. So there's a job in NASA, where it's called Safety and Mission Assurance, and you're responsible for essentially making sure that any hardware that goes to the space station or any procedures that the astronauts do, don't pose risk of harm to them, or doesn't, for lack of a better word, like damage the space station. And so those are, there's a team of engineers who work on different systems on the space station. And my responsibility was I was an engineer specifically focused on the computer systems and the antennas and a little bit of power generation for the International Space Station, making sure that all the stuff we sent up there, kept our astronaut safe.

 

[Learn Educate Discover] 07:03

That's very cool. All right, I would say that what you're doing now does not sound as exciting :D 

 

So what you mentioned is that when you graduated from Wharton at that time, there was a time when you got interested in tech, and entertainment. And you also were interested in analytics. So what about analytics? What is it about this field that attracts you to it?

 

[Ian]   07:30

I think it's really its ability to explain things. Generally speaking, there's a whole lot of concept you learn in a lot of schools, and a lot of theory that you learn in especially getting an MBA, and analytics actually allows to kind of get through, like weave the thread from the theory to the practical world of like, Yes, this is what we believe should happen based off of anecdotal evidence throughout the history of mankind. But analytics actually, has been able to tell us like, specifically for our given situation, what does the world look like? To me, it's kind of shining light in the darkness. And as a person who likes to ask a lot of questions, that kind of works for me. And then the other great thing about it is that it's useful for so many different areas. As I've seen through my short career, a lot of teams can use it, and a lot of teams need it, and they don't even know they need it. So the opportunity to actually help them develop analytical mindsets, is exciting to me.

 

[Learn Educate Discover] 08:32

Okay, so how would you describe marketing analytics then?

 

[Ian]   08:37

So marketing analytics could be a variety of things. But the general idea is that you're targeting towards two main things. One is actually acquiring customers. So that could be assessing the the effectiveness of email campaigns and determining new opportunities for that. It could be social media advertising and determining what's the right dollar spend to get the best return on investment for different social media advertising. It could be if it was worth it to host the event. So there's kind of that element of getting people to use or buy your product. But then there's the next step of actually not only getting people to do that initial engagement, but actually increase their engagement with you. You don't want somebody to just come to you one time, especially in most tech companies, you want them to stay with you. You want them to want you want them to want to grow with you. And so you want to also assess the effectiveness of marketing efforts in the area of retaining engaging and like growing with your customers to

 

[Learn Educate Discover] 09:39

Is this a marketing role? Do you report into marketing?

 

[Ian]   10:18

Yeah, so I report into marketing. And that's the way we structure it. I will say that I've seen it where there's a central analytics team, and different people have specialties. But the reason why this with the functions that I've described specifically are called Marketing Analytics, is because it's generally what most companies, especially in the tech space, I should say, consider to be marketing, you usually hire campaign managers, product marketing managers, and those kinds of jobs with the idea of their goal is to get people to use the product, get people to engage with the product and get people to stay and grow with the product. So it's just kind of like that's what idea of marketing is. And in the analytics, part just kind of fits in the marketing analytics kind of fits in right there.

 

[Learn Educate Discover] 11:04

Do you find this role in other industries apart from tech ?

 

[Ian]   11:26

I haven't seen it as prevalent as I see it in tech. I think that's just the nature of analytics in general, that it's a little bit more prevalent in tech. But there are other companies who are in industries that do it, for example, CPG invest heavily in marketing analytics. And that's consumer packaged goods, right, just in case for people need to know. But they invest heavily in marketing analytics, because marketing is a key driver for revenue for them. So in fact, some of the first studies that I was recommended to do when I got out of business school was actually your read up on the work that the CPG companies did.

 

[Learn Educate Discover] 12:35

Can you share an example of some projects you might have worked on in the realm of marketing analytics?

 

[Ian]   12:49

Sure. So let's see. So one of the things I'm heavily focused on right now is actually doing a lot of analysis of our website. For a lot of companies, especially in the tech space. The website is a very important piece of the engagement with your customers, especially that initial engagement, it's where people first really get an impression of who you are and what you say you are. And so a lot of work I do right now is actually doing analysis into, you know, where are people coming to our website from? Are they coming from the places that we invest in? Are they coming from places that that are just kind of the natural organic nature of the world? Are they typing our name into their browser and just going to twilio.com? And then taking that next step of, you know, where do they land in our website? And then what is that journey for them to either get on our website and deciding that it’s not for me and leaving or determining that Twilio is exactly what I need, and I need to sign up now. So like doing those journey mappings of our customers is actually a very big project that I'm working on now. And the idea is that that is our first, that is our first step to getting, you know, getting a lot of customers.

 

[Learn Educate Discover] 14:02

So let's say you're doing this analysis of the traffic to the website, and you find out that, oh, it looks like most people are coming to twilio.com through, let's say, Reddit. If you provide that insight, what would be the outcome of this insight?

 

[Ian]   14:32

It raises the question of whether Reddit is worth investing in. We might previously have not even put Reddit on our radar, hypothetically speaking. But then if we say that 20% of our audience hypothetically is coming from Reddit, then what we then can do is say, Well, should we invest in actually having somebody staffed with creating Reddit comments or maybe we should not? Or maybe Reddit is kind of tapped out, we're just going to get what we get from them. But it creates the initial discussion and actually might make room for further analysis down the line of what are the next things we should be doing as a company?

 

[Learn Educate Discover] 15:18

So let's go one level deeper into this project - Can you walk us through the stages in the project just to give an idea about the kind of activities you might engage in when you're working on something like this?

 

[Ian]   15:31

Sure. So yeah, so it starts with usually starts with a very general question. How good is our website working? Could be something that general and in this case was a little bit more specific than that. But I've gotten projects where it's very much that and

 

[Learn Educate Discover] 15:46

just a quick clarification there who asked this question.

 

[Ian]   15:51

So it could be a variety of people, it could be your chief marketing officer, it could be the person in charge of the website, in this case, it was a collaboration between the two, there was a unified agreement, that deeper analysis needed to be done in the website, we were doing great maintenance analysis, but it was time to do like the State of the Union for the website kind of analysis, making sure that we were capturing all the right opportunities. And so through their engagement with each other, it was decided that we needed to do this analysis. And so they approached my team of saying, Do we have the bandwidth? Did we agree with the prioritization of it? And which, of course we did, hence why I'm working on it. 

 

So then the question really was, are people finding what they need on our website? And that was actually two things. One is, are we getting people to sign up to be customers, or Twilio on our website? Like, you know, what percent of people are actually signing up? And then the next question is, we want to make sure that we're sending the right people to the right channels, so that they can actually get use out of us. 

 

And so there's those kinds of questions of all we said, is the website sending the right people to the right places? And those are the initial questions. And so then, because then the conversation became well, what do we then I responded back with? Well, what do you want the website to do? Because, and this is a trapping of analytics, you can give tons of information, there's always data about websites, because every day, there's somebody new visiting your website. But what you really want to do is be able to give insight that drives decision making, you really want to make sure your insights are actionable. And so for that, you want to make sure that you know, what people are asking, like, why they're asking, the questions are asked, and so that, through that conversation, I got a list of things that people want our website to do. And then I can then test against that with the data that we have with our tools to see if the websites actually doing those things. And if not, then start doing deep dive analysis of why that's not happening, or is it exceeding expectations? 

 

[Learn Educate Discover] 19:44

Gotcha. Yeah, that makes sense. So can you share an example of, going back to the point that you made that how Analytics has to be insightful analytics otherwise does not really of any use. Can you give an example of a good versus a bad question which should help you arrive at that insightful metric.

 

[Ian]   20:04

Sure thing, and it's usually not the question itself. It's the “what can be done with that question”. So you can ask the same thing, how many people are coming to our website? And if the question itself is just how many people are coming to our website, and you just get a number? That's not really that insightful, there's no plan of action. Versus somebody going how many people are coming to our website, I want to assess Is that sufficient to drive the number of signups for the service that we need? And if it's not, then we need to look into creating new methods of acquiring customers. That's an actual question that has a decision tree that follows it, which allows people to take action. So then when you present the number, you have like a benchmark, and you have a Benchmark Number, you can say higher lower, how much higher how much lower, and then somebody can actually do something, because now you know that there's a like, there's, you know, what the next step is.

 

[Learn Educate Discover] 22:46

How are the teams structured? Who are the stakeholders that you're typically working with? 

 

[Ian]   23:10

So there's going to be the team that owns the results from the metrics that you're polling, they're always going to be involved. So if this is a website, question, in this particular case, this actually specifically targeted towards people signing up, then the person who is responsible for driving sign up, is going to be involved, as well as the person who's just been responsible for designing the website, they may not mean the same to people. But this immediately impacts their business. They're elements of the business. So they're both involved. But also is not just the two people who own the final result. But it's also the people who are going to be acting on those results. You don't just present the finding, you also have to then sometimes help coordinate what the next steps are. And so for example, in this case, we have a person who manages our website design team, who's involved, because any changes that we recommend have to be vetted to, are we able to do them and like how soon can we do them. So you need somebody who's a little bit more operationally minded. Usually, for the bigger projects that are going to have big actions taken, you're going to have somebody like that involved. And then from there, it just depends on the scale. Usually, you're almost always going to have those people. But then you might also have executive leadership, if it's a state of the company kind of analysis or you're going to have finance involved. If it the recommendation is investing a large sum of money in say, Google AdWords or something like that.

 

[Learn Educate Discover] 24:50

Based on what you described, it sounds like it's a lot of people from product, whether it's a product manager or designer, those sort of functions?

 

[Ian]   25:03

Yes. And I think that's just kind of a nature of like the industry we're in is it's very, that we're very, it's very product driven. And so yes, there's that element. And the idea is that you could have some brand people sometimes from the customer advocacy, it just depends on the  nature of the project. 

 

[Learn Educate Discover] 25:23

So can you describe a typical day for us? If I were to run into you? What would you be doing?

 

[Ian]   25:30

Yeah, sure. So it really depends on the day, but I will say that there is a good enough weekly cadence that I can kind of answer that. So generally speaking, most analytics teams, across the board and marketing analytics is no exception, have a set of usually weekly metrics that they're reporting on. So you're usually going to have a day or so of either putting that information together, reviewing that information with the people of the responsible parties, or actually verifying information, or taking next step actions based off of just those, that basic weekly metrics pack. There's a state of the marketing metrics pack that we send out that kind of summarizes the big initiatives that we had in the previous weekend, are they effective? Are they not effective? You know, those kinds of things. So there's usually some time doing those things, then the next step is usually in a big chunk of the time is usually being involved in actually reading through data for a large project. And actually, what I mean by reading through is not like reading line by line. But creating different views of information so that you can actually make sure that the recommendation and the analysis that you're doing is an informed one. So that you're not presenting a false answer, like, it's, this is 40%, likely, but then you realize you're looking at it wrong and your models wrong, and maybe it's 30%, more likely. And so there's those kinds of things that you're doing. And then the third major bucket that you're spending your time doing is usually involving meeting and coordinating for the next projects that you have. So while you're doing your analysis, and you're very focused on these large projects that you're working on, you know that the business doesn't stop. So you also have to be keeping your pulse on, what are the needs that are coming up for the coming up for you. And so you need to be coordinating with the right people to make sure that during you're ready to catch the next analysis.
 

[Learn Educate Discover] 31:06

What do you think are the most interesting aspects of this job?

 

[Ian]   32:50

So the most interesting thing to me, like the absolute most interesting thing, is that the possibility that you can change a previously core held belief of the company, I think that is what excites me the most about it. And what I mean by that is, you know, if you're pulling data at somebody, you know, somebody can say that, I'll just use a loose example from my time here at Twilio, that all developers go to a certain page on the website. And that can be a core held belief. And what we can find is no, developers are a little bit more varied. And that, you know, you know, a lot of them do go say 70%. But there's 30%, who go somewhere else who don't go to that page, but they still succeed as customers with us. And so you can change the conversation to to where it's net, where you can take sweeping generalizations and actually refine them to be like, No, there's a big pool of people who do something different. And we need to think about them differently to treat them according to what they want to do. So you get the the ability to shift. That's the word I was looking for paradigms. of, you know, your organization, I think that's the most that's the most interesting to me, thing to me. And then the second most interesting thing to me is just the continued learning, the expectation is that you're always you're always learning. And you're always learning how to use something. It's frustrating, but it's great that there are so many tools being built for analytics. And there's so many different query, there's so many different data storage systems, there's mem sequel, seek MySQL, no sequel, I don't even know what those people really means. Because it doesn't make sense to me. But it's a thing. So there's always opportunity. There's always opportunities to learn because people are always trying to optimize analytics like nobody's business.

 

[Learn Educate Discover] 34:46

Yeah, these are great points. And it sort of goes back to what you said in the beginning that what attracted you to analytics is this notion of how you can use data to really shine a light on things which people might not be thinking about. And that's exactly what you're saying. What is your favorite tool? What tools are you spending most time in?

 

[Ian]   35:06

So it's a script. scripting language R is my favorite thing. And to be honest with you, I didn't I never used it before coming to Twilio. And it was one of those things that I was, you know, you go to work and you learn how to use Excel. And then I found out that R is so much better than Excel in so many ways, except for graphing. And so it became my favorite thing. I don't use pivot tables in Excel anymore because I can do I'm so are so much more flexible, once they once I learned how to use it. So it's easily the best thing I've learned and is the probably the thing that I absolutely will transfer with me to the next job I have.

 

[Learn Educate Discover] 36:12

Are there things that you do not like about this job?

 

[Ian]   36:36

Yes. And it goes to what you were talking about, about the convincing people and I don't mind the convincing people part. But there's elements of it. There's an element. And this is not just in marketing analytics, this is seen every form of analytics. And it's really the nature of, of if somebody has a belief that they've been acting on, and they've worked on it long enough. It's it's they it's sometimes the conversation isn't about the facts. It's about this is my baby, how dare you tell me that my baby is ugly? Yeah. And as a new parent, I tried to not use that metaphor, but it works. But it's very much the element of there's all there's sometimes a lot of resistance, especially when you're recommending serious change, or you're saying something's not working. And so it's the most frustrating part is when you know, what you're recommending is correct. And you know, that the person on the other end of the conversation is coming from a personal standpoint, rather than a logical or reasonable standpoint. reasonable, but reasoning standpoint, I should say. That makes it can be frustrating at times, that would be my least favorite thing. But fortunately, most of the time people are open to at least you know, thinking through the points that you're making, if you're able to convince them that you've thought about it correctly.

 

[Learn Educate Discover] 38:08

Have you found any strategies particularly helpful when you run into situations like that?

 

[Ian]   38:14

Yeah, so I have a, I have a term called a win, and I use it. People get tired of me using it. But it's my way of defining success before we even do the analysis. And so what I mean by that is I get buy in from the stakeholders on what they think what they hypothesize is correct. And I say, so what are our wins? How will we know we’re winning? What should it look like? So that at the end of the day, we're all committing to numbers. And so when those numbers don't show up, or they or they show up, more than we were expecting? It's not an argument of it's not like we all agreed on, we all agreed on these things going into it. So it makes it easier to have conversation afterwards, because we already had the pre conversation of what are theories and what our beliefs and what our feelings

 

[Learn Educate Discover] 43:21

Do you think there are any common misconceptions that people tend to have about Analytics? 

 

[Ian]   43:38

Well, there's two that immediately come to mind. One is analytics and data science are in fact two different things. Most people see analytics, and they hear big data. And they just assume that analytics and data science are exactly the same thing. And they're not necessarily the same thing. Data science is a more specialized form of actually understanding how to to do more complicated math and do more complicated stuff like clustering and groupings and doing these doing advanced predictions, and doing text analysis, like these very advanced thing, techniques that you can use to approach data that, you know, are, scale in and of itself that I do not have. Versus analytics is actually more of an application of findings. So it's like getting findings, but it's more about being able to apply findings to whatever is relevant to you. Be that marketing be that operations, whatever it is. So that's the first one and then the second misconception, which I think is just as prevalent, is some people think of analytics like they think of consultants In the sense that you kind of get in, you make your recommendations and you're out. And most good analytics teams that I've seen, have some stake in actually, you know, making sure that their recommendations deliver. So that could be actually coordinating the next steps. Or it could just be something as simple as you know, I'm going to create the metrics that we're going to track to make sure that whatever you do, based off of my previous analysis, actually yields results. So that we continually have some skin in the game in terms of actually keeping, keeping the ship afloat.

 

[Learn Educate Discover] 45:44

And in terms of the just sort growth in this role, what does that career progression look like?

 

[Ian]   45:58

Sure, so it could look like a variety of things. You start out as an analyst. And from an analyst you can grow to be an ultimate individual contributor, where you become very specialized. So you can grow along the individual contributor route that you really, like, I'll just say you really like revenue expansion. And you really like understanding how customers how to grow revenue with a specific group of customers. And then you can so you can create a path for yourself and a lot of companies where you become the go to expert for revenue, expansion analytics, and like, that's your that's your field and you become a that's your yes, your arena. So you become a senior analyst, with a very specific focus. versus the alternative is there is, you know, there's the management element of it, there's the progression to where you're not just focused on the individual analyses, but the actual coordination planning of planning of the analysis. And what's next. And then from there, it's actually then determining resources for analysis, like, what tools do we use? Planning out the staffing, and all those kinds of things that come along with it. So there's usually two routes, and then there is in terms of if you want to stay in analytics, and then there's the third route, which is people who be through analytics determine their what their passion is, that's not analytics. And they go do that. So we have a lot of you have a lot of people who work in analytics, eventually go into product management, or eventually go into growth, because they did a lot of analysis on specific projects. And then they found that they really like those things that they worked on, and they want to go do that full time.

 

[Learn Educate Discover] 48:26

Do you think there are three to five skills or qualities that really stand out amongst people who are good at this job?

 

[Ian]   48:50

Sure, yeah. So the first is comfort with numbers. It's the biggest part of the job, especially at the entry level. So you don't necessarily have to immediately be the best mathematician. But while you're studying to get there, you should be comfortable and interested in actually getting to that stage. And then I'd say being open to learning languages in terms of scripting and programming is important. So either as having a baseline of previous programming experience or being willing to teach yourself to actually be able to do those scripting languages are very important. And then communication is absolutely important and both visual and visual. Being able to write effective emails and summarize your findings and summarize your recommendations. Knowing how to present data is incredibly important. I know a lot of people don't know this. But there's a lot of, there's a lot of things that you should not be showing on a pie chart that a lot of people show on. And so like no, like, actually understanding what visualization actually is best for communicating your information is incredibly important. Because that's a lot of people aren't good with numbers. But a lot of people are good with pictures. And if you show them a picture that can you know, that, that they'll tell, they'll analyze a picture in a heartbeat. And if you show them the wrong picture, they will analyze incorrectly. And then the verbal side of actually being able to convince and persuade, and get agreement with people. Those are the three main ones. Another is curiosity. A big opportunity for a lot of analysts and where a lot of analysts that I've seen, even where I actually had a lot of opportunity myself, is actually when you find something that's a little odd, when you have some free time that you're willing to go like look into it and see if there's a see if there's something there, that's an opportunity that might not necessarily pan out, but might have some value that you can then use later or present later to people to help them do their job better.

 

[Learn Educate Discover] 55:26

What is the typical background for this role then?

 

[Ian]  55:30

So the typical background is either somebody who has a data science degree, a statistics degree, something like that. But there are programers sometimes if they don't want to certainly go into development. But I will say, I know a lot of people who didn't do that. I'm not I, Lord knows it. I wasn't a programmer in college. I barely passed my programming classes. And I know, I work with somebody who's a biology major. And one of my close friends who did Marketing Analytics was an economics major. So it's really less about the schooling and is more about just, do you have those skills? Have you learned Python? Have you learned R? Have you learned SQL? Like, do you know the tools needed to do the job less than, like, necessarily what your professional experience has been?

 

[Learn Educate Discover] 57:55

So is there a way for someone to figure out Is this a good fit for me? 

 

[Ian]   58:18

Yes. So there's a couple of things that you can ask yourself, that'll really help start. Are you comfortable with people seeing your work? That's the first question. And it sounds like a silly question. But the role of somebody in analytics is to show their work. And if they show their work, right, it will be spread past them. So you have to be comfortable with people seeing the work that you've done, and not just seeing the end results, but actually seeing the work itself so that they can be comfortable with what you're showing them. The next question is, you know, do you like convincing people to do things and not manipulating them? But do you like the art of persuasion in terms of your job? And then do you like numbers? I can't stress that enough. Yeah. So those are like the those are like the checklist of questions to ask yourself. But then I'd say the next best thing to see if you would really like to work is to do to create scenarios for yourself. So like say, there's a lot of information that's publicly available Facebook and Twitter both have this requires you don't know Python, sorry. But if you can access a lot of their data, like about, like, how many people are tweeting about this, or how many people are, are, you know, posting stuff like are writing on this about this TV show or something like that? And you can do a whole lot of analyses on your own to kind of like see, you know, is there a correlation between people who watch it justified on FX, who also watch western movies who tweet about Western movies and like you can create questions for yourself and go on on Facebook and Twitter, do a whole bunch of analysis to see if there's something there. And you know, they're silly, but they actually do develop the skills that you need to be an analyst.

 

[Learn Educate Discover] 1:00:09

That's interesting. Have you have you done this sort of analysis by yourself?

 

[Ian]   1:00:14

I did it a couple times. I'm a movie fan. And I have a bit I do a lot of analysis on movies because I religiously watch movies.

 

[Learn Educate Discover] 1:00:22

What's one crazy or counterintuitive thing that you found?

 

[Ian]   1:00:29

The longer a movie is, the higher likelihood it has to have a good rating on IMDb. It's not much higher, likely, but it's slightly higher likely to have a good rating on IMDb!

 

[Learn Educate Discover] 1:01:05

Okay. All right. So if let's say I want to apply for a marketing analyst position. Is there a way for me to assess whether the role and the function is good at that company? While I'm recruiting?

 

[Ian]   1:01:28

That's a great question, actually, you know, yes, and no. So it's hard to say it's, it's really tough to say for sure. Just because, you know, when people create postings, they there's a lot of puffery in the postings for a lot of companies about you know, they use a lot of the same terminology that's really makes it really hard to assess details, but you can look for keywords that might describe what they're specifically going to want you to analyze. They might say, you know, our conversion funnel, which would imply that they want you to assess how people are coming to the service, and how people are actually becoming customers on the surface, they could be saying they want, they might say the word NPS, which is made from net promoter score, which is assessing how people feel about how you how likely someone is to recommend a company. So you can look up for keywords about specifically what they want to analyze, and then do some research on what those things are. And see if those are things that interest you.

 

[Learn Educate Discover] 1:05:22

I know that data scientists, they regularly take part in contests and such, is there something like that for analytics related?

 

[Ian]   1:05:47

I'm not sure if there's anyone specifically marketing analytics related. But what you will find is that there are people who are in marketing analytics who participate in those other ones. And so yes, participation in those projects is phenomenal. A lot of companies would be excited that you participated and or placed in those kinds of competitions for sure. Well,

 

[Learn Educate Discover] 1:06:06

Maybe you have a blog, where you've listed insights you’re uncovering. I guess that probably demonstrates your interest in the field.

 

[Ian]   1:06:18

Exactly. And that's actually a growing thing that's going on in the field is that there are more and more people actually doing blogging. So it's almost like one of those things that if you are looking, if you know that you're going to be looking for an opportunity in the next year, I'd definitely recommend you start blogging, I wouldn't recommend you start you create a blog and then apply to something tomorrow, just because of the fact that you won't necessarily like if they go to your blog, you won't necessarily look the best. Yeah. But if you've got time, I would definitely recommend doing

 

[Learn Educate Discover] 1:06:48

And how important is the cover letter?

 

[Ian]  1:06:52

Cover Letter, not super important. There are certain places that are going to be excited to see it. But I'd say 99% of the time, not needed.

 

[Learn Educate Discover] 1:07:05

Yeah. All right. Well, this was great. And thank you so much. Is there any other advice you'd like to share with people who are interested in this field? 

 

[Ian]  1:07:22

Sure, I just have one last thought which is that the best way to tell if you are good at presenting logical persuasive arguments is to actually read your own presentation, or writing. And if you look at your look at your presentation and don't find any holes, then you're probably not going to be good at empathizing with somebody on the other end of the table, and probably then not going to be good at persuading.

 

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