I see the writing on the wall and it says data science. As more and more devices join the internet of things, as more shoes and fridges and chairs and hairbrushes upload data about frequency, duration, latency and more to the interweebs, it becomes more and more clear to me that manipulating ridiculous volumes of data is the future of marketing research. No more will we ask people how often they buy and wear shoes, or which shoes they wear in which weather. We will simply read the writing in the cloud. Marketing researchers cannot rely on their old standbys while everyone else learns the always evolving tools of the research trade.
I see the writing on the wall and it says Python. A few days after internalizing that writing, I made a purchase of two paper and ink products that will never break upon being dropped on concrete. These two things, ancient learning tools called ‘books’ will be my friends for a while this year.
And interestingly, shortly after these ‘books’ came into my possession, I came across this post by Amy. I’m good with SQL and good with Excel but what about the two items in between? Well, R and Python, here we go!
After a long hard day of surveying, dataing, coding, and focus grouping, everyone needs a little inspiration to keep them going. This is for you.
- Leading Through Transformation, Anne Mulcahy #TMRE #MRX (lovestats.wordpress.com)
- Chocolate, Vultures, and Glowsticks oh my #eso3d #mrx (lovestats.wordpress.com)
- My Fight with DIY #MRX (lovestats.wordpress.com)
[tweetmeme source=”lovestats” only_single=false]I’ve been asked on a number of occasions how I found a job in market research after completing a Phd in psychology. You’d think the opportunities are endless but, like any career path, there are always obstacles.
Interviewers have told me to my face that they refuse to hire or they don’t like Phds. They’ve even given me strange tests to determine whether I’m human or robot, like “What does this abstract painting on my wall mean to you?” (Honestly, that piece of crap is major ugly.)
So here is my advice. It’s free advice so the confidence intervals are wide. Please do ask questions and I promise I’ll answer all of them.
- The fact that you have a Phd means you know research and statistics. Don’t waste your cover letter proving this.
- The fact that you have a Phd means employers think you don’t know the real world and that you can’t speak casual english. Prove this wrong in your cover letter. Write in business dialect not in dissertation dialect. This is one case where fancy words do NOT impress.
- Forget the stats speak. When they ask you what a t-test is, don’t tell them it’s an analysis of mean scores and confidence intervals of a quantitative variable for a second qualitative variable. Speak english. Say it’s a way to determine if two groups of people differ on a measure like height or weight.
- Join a few online survey panels so you can get a lay of the land. What questions get asked? How are they asked? Do you want to shoot yourself during the survey because it’s so horrid? This will give you insight about the business you think you want to get into and…
- … something to talk about during interviews. In which you will speak like a human being not a professor.
- Go to a used bookstore and buy a Market Research 101 textbook. Learn it.
- If your field isn’t psychology, you would do well to take a course in social psychology or personality psychology. It will give you great insight into survey question design.
- Learn either SAS or SQL. Not the menu driven kind, the syntax programming type. Even if you don’t end up using it on the job, you will be better able to talk to the statisticians and get what you need in the time you need.
- Accept that in the business world, projects take 14 days not 14 months, with sample sizes of 200 not 2000, and conclusions that are final not proposals for 8 more years of research. Now is not the time to try to convince your gracious hosts otherwise. They aren’t stupid.
You are already qualified. You just need the right vocabulary and the right perspective on research for business. Any questions?
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