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These data prove I am worth 80% of what my male research colleagues are worth #MRX #NewMR

As a freelance market research writer (my service sheet is here), I regularly check my public profiles to make sure they’re up to date. This time, I checked my profile on Savio, a marketplace connecting market research buyers with research experts, which is maintained by GreenBook.

Thumb print unlockAfter clicking around the website for a bit, I realized that every researcher’s hourly rate was completely and easily transparent, not hidden behind multiple clicks and privacy walls. My brain gears sped up….

People don’t talk about salaries which is a problem. Freelancers don’t know if they’re being paid what they deserve. Women don’t know if they’re being paid less than their equivalent male counterparts. So in that regard, I have to thank Savio and Greenbook for opening the black box and helping researchers see a piece of reality.

I had planned to go see the new Solo movie but this lovely little dataset just presented itself to me. Plus, I hear there isn’t any ukulele in this movie so let’s do this.

I downloaded the data and removed the business profiles. That left me with 191 individuals who provided an hourly rate, country, and the types of work they do. I manually gendered all the profiles. Obviously, I may have gotten a few wrong as I am not the gender police (much better said by Effin Birds) – 83 women, 107 men, and 1 unknown.

Hourly rates by genderAs all good data people do, I started with a frequency distribution. A few things were immediately apparent. 1) 3% of men and 4% of women VASTLY undervalued themselves. If you have listed an hourly rate under $50 per hour, go to your profile right now and FIX IT. I never want to see an hourly rate less than $50. From any freelancer. For anything. 2) 6% of men and 2% of women listed stunningly high rates, a couple over $1000 per hour. These rates might be for bragging, for negotiating, or for real but if you can command them, more power to you. 3) Women were far more likely to undervalue themselves while men were far more likely to overvalue themselves. [“THAT was a pencil in the neck moment!” -Luke Sklar]

Hourly rates by country and gender Maybe group averages would paint a different picture but nope. Across all 191 rates, women asked for 81% of what men did – $168 versus $207 per hour.I tried excluding outliers from 12 people whose rates were below $50 or above $500. Women still asked for 81% of what men asked – $153 versus $189 per hour. I then focused on the three countries with at least 8 researchers. In Canada, two women and myself listed rates that were a paltry 40% of what five men listed. Among 123 US researchers, women asked for a somewhat better 80% of what men asked for. I am thrilled, though, to offer a huge hurray to the 14 researchers in the UK where hourly rates listed by men and women were equal. (Okay, women can increase their hourly rates by $5 in the UK.)

Maybe it’s because women do “less valuable” work so I tried grouping by the 25 different type of work people specified they did. Major caveat though – these data do not account for the fact that someone might charge different rates for different types of work.

I’ll pick out two examples from the chart since it’s a little bit complicated and uses two axes. At the left of the graph, among people who offer legal research services, women specified an hourly rate of $169 compared to men at $136. Thus, women listed a rate that was 124% of what men listed. There exist four categories of work where women listed a higher hourly rate than men – Legal Research, Field Services, Recruiting, and Support Services.

Second, at the right of the chart, among researchers who conduct Mystery Shopping, women listed an hourly rate of $138 compared to men at $225. Women listed a rate that was 61% of what men listed. There exist 21 categories of work where men listed a higher hourly rate than women.Hourly rates by industry and gender

I don’t know if these differences are because women undervalue themselves or because men overvalue themselves. I don’t know how much of these differences exist for bargaining or bragging purposes.

But I do know this. As much as I love statistics, t-tests and chi-squares aren’t necessary to determine the likelihood that these results are due to chance. Correlations and Cohen’s D aren’t necessary to determine whether the effect sizes are meaningful.

Women ask for less financial compensation than do men.

Cindy Gallop Highest Number Without Laughing Gender Diversity Salary Income

Women, my advice to you is simple. Give yourself a raise. Give yourself a giant fucking raise. (I’m channeling my inner Cindy Gallop and I urge you to follow this amazing woman on Twitter or LinkedIn and personally talk about your salary with her here.)

Cindy Gallop Shit Ton Money Gender Diversity Salary IncomeIf you’re currently in the $50 to $99 bucket, up your rate to land in the $100 to $149 bucket. If you’re in the $200 to $249 bucket, give yourself a raise into the $250 to $299 bucket. Don’t think twice, it’s all right.

If you’re curious, I may have started my day claiming my worth to be 80% of what my male research colleagues felt they were worth.

It sure didn’t end that way.

You might wish to look at:

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Happy International Women’s Day! Let’s meet some #MRX women who are flying under the radar #IWD #WIRe #NewMR #WIREheroes

International Women's DayHappy International Women’s Day!

The market research industry is lucky to benefit from a diverse range of people. Indeed, unlike some industries that are vastly male or vastly female, about half of us are women.

I know many women within our industry who regularly take the stage or sit on association boards or have roles on leadership teams. You probably know them too. Don’t you think it’s time to get to know some other fabulous women who keep the cogs or our industry turning? Let me start with two of those fabulous women!

I first met Kim Wong when she interviewed for a researcher position at Conversition, a social media research company. It was quickly apparent that she was a perfect choice. She figured out our business super fast, even though it was a strange concept at the time. She soon became a wizard at sentiment analysis, content analysis, and data quality of social media data. We could trust her to turn any set of random data into exactly what we needed. You know how amazing it is to find a colleague who can take a task and run with it independently? Yup, that’s Kim. Kim, cheers to you, your awesome contribution to our research team, and to the market research industry. 👏🏽👏🏽👏🏽👏🏽👏🏽👏🏽👏🏽👏🏽

Meredith Morino is another quietly awesome researcher who deserves a big round of applause. I love your dedication to and passion for qualitative research. I love your openness to try new things even when they seem outside your usual way of doing things. I love that you’re a team player who works hard to ensure that you and your colleagues at Sklar Wilton & Associates do well. Meredith, I look forward to many more intriguing blog posts from you, and even seeing you present on stage. You’ll be awesome, I know it. 👏🏽👏🏽👏🏽👏🏽👏🏽👏🏽👏🏽👏🏽

Do you know Kim or Meredith? It would be awesome if quiet people could stand on stage and get the huge applause that thought leaders/speakers get all the time so let’s do it here. If you appreciate the work that Kim or Meredith do, leave a note for them here. Even better, send them an email and tell them just how much you appreciate their work.

What do Kim and Meredith need to do now? Recognize another woman in research who is making a difference! Tell us which women researchers are your unsung heroes! You could leave their names in the comments below, tweet their name and why they are awesome, mention why they’re awesome on LinkedIn or, even better, email them and let them know why you think they’re awesome. Don’t forget to tag it #WIREheroes so we can clap for all these awesome people!

What should YOU do? If you’ve been named, and even if you’ve not, it’s your turn to name a woman in research who is flying under the radar. Let’s see how many unsung heroes we have! Don’t forget to tag it #WIREheroes!

Voxpopme Perspectives: Video posts… in writing

Along with a group of market researchers from around the world, I was asked to participate in Voxpopme Perspectives – an initiative wherein insights industry experts share ideas about a variety of topics via video. You can read more about it here or watch the videos here. Viewers can then reach out over Twitter or upload their own video response.

Except the video blogging thing wasn’t working for me. I do my best thinking in writing and I’m pretty sure you don’t want to watch me read a post. So instead, I’ll be sharing my thoughts in written posts. Feel free to write back if you’re so included. Stay tuned!

Voxpopme

What’s the big deal with big data? #casro #MRX 

I recently debated big data with a worthy opponent in Marc Alley at the Corporate Research Conference.  He stood firm in his belief that big data is the best type of data whereas I stood firm in my position that traditional research is the only way to go. You can read a summary of the debate written by Jeffrey Henning here.

The interesting thing is that, outside of the debate, Marc and I seemed to agree on most points. Neither of us think that big data is the be all and end all. Neither of us think that market research answers every problem. But both of us were determined to present our side as if it was the only side.  

In reality, the best type of data is ALL data. If you can access survey data and big data, you will be better off and have an improved understanding of thoughts, opinions, emotions, attitudes AND validated actions.   If you can also access eye tracking data or focus group data or behavioural data, you will be far better off and have data that can speak to reliability or validity.  Each data type will present you with a different view and a different perspective on reality. You might even see what looks like completely different results.  

Different is not wrong.  It’s not misleading. It’s not frustrating.  Different results are enlightening, and they are indeed valid.  Why do people do different than what they say? Why do people present contradictory data? That’s what is so fascinating about people. There is no one reality. People are complex and have many contradictory motivations.  No single dataset can describe the reality of people.  

There is no debate about whether big data has anything to offer.  Though Marc and I did our best to bring you to our dark side, we must remember that every dataset, regardless of the source, has fascinating insights ready for you to discover. Grab as much data as you can. 

Fad or Foible: MR Trends by Bernie Malinoff #NetGain8 #MRX

Live blogging from MRIA’s #NetGain8 conference in Toronto. Any errors or stupid jokes are my own.

Netgain8

“Fad or Foible” MR Trends Affecting the Industry, and Skill Set Needs To Delight Your Client, Bernie Malinoff, CMRP, President, Element54, Montreal

  • remember “second life”   – just because it’s a shiny new toy doesn’t mean it’s relevant
  • researchers tend to be conservative, risk avoiders, it’s a strength to some degree, people trust that we will be disciplined about our work, but this can also hold us back
  • we used to be data poor, now the problem is data obesity – Hal Varian, Google
  • [Bernie has written out tweets on our slides that we can write into twitter, now isn’t that thoughtful 🙂 ]
  • don’t be concerned about the person sitting next to you, worry about people who’ve never been to a market research conference and possibly never consider themselves market researchers
  • the dirty dozen – are you afraid of gamification, online communities, social media, crowdsourcing, facial analysis
  • many emerging technologies are now mainstream
  • you can now capture emotions of 43 facial muscles and vocally detected intonations – add that to your basic film plus sound – now you have what i said and HOW i said it. these are off the shelf products you can buy now
  • supplier selection is often based on more creative and energetic modes
  • researcher of the future is a strategist, synthesizer, method agnostic, story tellers – now it’s use the right method for the research objective, not the tool you’re most familiar with
  • blend technology with rigour, find a fit for purpose technology when it’s appropriate
  • 3 cases do not make a norm, a new method will not and cannot replace all other methods
  • replace fear of the unknown with curiosity
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