…by sending a yet another email.
…by sending a yet another email.
n. Marketing Hacker – Increase Revenue, Optimize Efficiency, and cut costs using Automation, Data, and Process.
I decided to coin this term, because as a Marketing Automation dude who dabbles in everything from statistical analysis to computer programming to data warehousing and analysis, I need fairly strong and disperse set of technology proficiencies that are “hacky” in nature in order to achieve the Marketing Automation Manager’s goals of increasing revenue by increasing the efficiency of marketing and the processes we use to follow up with prospects.
To be fair, everyone hacks something – we are all hackers of our domain expertise. Social Networking, psychology, writing, whatever. I chose this term because it encompasses both the technology-oriented nature of what I do, and what has traditionally been considered a “creative” endeavor. Juxtaposing these concepts next to each other is a better decryption of my talents than “Marketing Automation”.
What do you hack? What tools do you use? How does that creative value for your business?
What is a Marketing Hacker?
Partially, it is just a novelty term to describe another marketing geek. In many circles, it is descriptive of someone who comes from a hacker type of background/role who finds themselves in the world of using computing automation, data, some general statistics, and an insatiable curiosity to discover how things work and put that knowledge to good use.
Lets break down the two words a little further…
First is “Hacker”:
Long gone are the days where a “hacker” is a blanket term for individuals in dark basements doing legally or ethically questionable activities on their computer with information that belongs to other people.
Instead today’s hackers are people genuinely curious people who want to discover how things work in order to use that knowledge to extend the intended functionality of that device beyond its original purpose. Give a hacker a shiny widget and this is what the internal thought process looks like:
Got a widget that takes input “A” and input “B” and gives you output “C”? Cool! What happens if you put “C” back into the widget with input “A”?
You can see here that the hacker thinks of novel ways to utilize the device above and beyond its original purpose. There has been no greater platform since the wheel that has given rise to a hacker culture than the computer. Want innovation and creativity in your business? Find a real life hacker.
Next is “Marketing”:
Traditional Marketing has been a MadMen style creative environment. Flashy advertising in new and exciting mediums of sexy people doing sexy or otherwise entertaining things in order to position a product in the fore-front of your mind. The Marketing today is about what you can learn from the data, and what that tells you about your widget (ads, clicks, email opens, cart purchases, etc). Well guess who knows data better than anybody?
Hackers. They know how to get it, how to manipulate it, and how to feed that data back into the widget to produce an outcome that yields something new and unexpected.
So you stick a Hacker with a passion for data and a marketing who uses it to drive business growth and BAM! Marketing Hacker.
While this term could be used to describe a variety of jobs and replace existing titles, I think that it is mostly going to apply to people of a hacker and deep technology background who’ve done their fair share of grey-hat type hacking, that have got their hands dirty down in the digital dungeons and aren’t afraid to mix and match technologies and exploit a widgets potential to achieve a certain business goal.
What do you think a marketing hacker is?
Just what is Machine Learning (ML)? How is it different than statistics? Why do I need it?
In depth and more objective answers can be found on wikipedia, but you came to IkeCube for an answer:
Now – I’m certainly not an expert, but my take on ML is that it is basically a computer program that does the trial-and-error process of developing statistical models for you. This could be different kinds of regressions, clustering, etc.
So while it doesn’t do anything that a human couldn’t do, it does take a lot of the pain out of the guesswork involved in regular old statistics. An over-simplified comparison might be using an excel function to do a series of calculations on a whole column of data instead of computing them all by hand.
That isn’t to say that you could just jump right in to ML software and begin predicting when the next stock market boom/crash is going to happen. You still need to have a solid grasp of statistical concepts, and know how to evaluate the results of a ML process for appropriateness. I’m not a professional statistician; I’m a professional Analyst looking to solve real problems.
For example, Netflix awarded a team of joe-shmoes $1 Million to come up with a better way to calculate recommended movies to users, and the team used ML to do it. Think about that for a second: If Netflix was willing to pay $1 Million to improve the recommendation model, then they must expect to earn above-and-beyond $1 Million as a result of implementing the improved recommendation system.
The problems that I am attempting to solve aren’t as ground breaking, and are an order of magnitude simpler than the above example. I simply want to predict who is likely to respond a certain way given a series of events in a population with a certain set of demographics.
The software I have used in the past for statistical modeling has been limited to (gasp!) Excel, Minitab, and on the TI-86. So in the process of learning about ML and how to use it, I have tried out several pieces of software. While none has given me a sufficient answer to my problem thus far, they each have merits worth considering:
I’ll most likely continue working in R because it is fast and efficient and allows me to get very granular with my analysis, but I’ve recently found much success using Rattle for R, which puts a GUI (graphic user interface) on top that makes it easy to conduct common tests, explore the data, and do some basic visualization.
Continuing in the thought line of understanding what you know you know vs. what you know you don’t know and finally what you don’t know that you don’t know, I’ve noticed that corporate meetings tend to revolve around the first two.
For example, if you receive a meeting invite that has the word “discuss” in the subject line or it is in the first line of the invite body, you can be pretty sure that the requester has little to no idea exactly what they are coming to the meeting to do. That is, they know what they know and that is about it.
I find this meetings particularly offensive, because the requestor is merely offloading the mental heavy-lifting required to materialize the proposition into a tangible ask of their meeting participants. It also provides insufficient context in how to prepare for the meeting. Finally, and perhaps most offensively, meeting invites with this word usually neglect to specify a desired outcome of the meeting.
Q: Why does the requester do this?
A: Because they know that they don’t know what they want/need, and are expecting you to figure it out for them. On occasion, it is possible that the lack of information is intentionally ambiguous thus indicating a power dynamic and political player, which is just as annoying if you are in a lower position of authority.
However, if they had put in the effort to discover and know what they didn’t know, then they could put it in the email and specify a desired outcome of the meeting (unless they are one of the aforementioned power players). These meetings start with a clear goal, with little to no time wasted on explaining the ask. They also tend to finish with a set of tangible action items.
Of course, someone who doesn’t know what it is that they don’t know probably wouldn’t even send a meeting invite. So your time is safe from being stolen by inefficient meetings.
A previous manager of mine said they would decline to attend any meeting with an ambiguous subject or body that also contained the word discuss". If you calculated out the average hourly rate for everybody in a meeting, and added it together and multiplied it by the duration of the meeting, you’d find that inefficient meetings are a colossal waste of money in your organization.
Waste is a thief.