Tag Archives: conference

How do speakers see themselves? A survey of Speaker perceptions

The entirety of this post is available on the Gender Avenger website. 

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Why are women underrepresented as speakers?

Why are women underrepresented as speakers, particularly at the conferences I go to where half of the audience members are women? Does fear chase them off the stage in disproportionate numbers?

I’ve pondered this question for years but I never knew if my hypothesis was grounded in fact or in stereotype. Fortunately, or unfortunately as the case may be, the opportunity presented itself and here we are pondering real data from a survey I did of 297 male and 252 female computer or data scientists, and market researchers aged 25 to 49 — people who ought to be on their way to securing spots on the conference circuit.

One of the questions in the survey asked people to imagine speaking at an event and to choose any attributes that would describe themselves as a conference speaker. I was careful to include an equal number of both positive and negative attributes so as to avoid leading people to choose a greater percentage of positive (or negative) items.

Curious how men and women viewed thselves? I know you are. Read the entirety of this post on the Gender Avenger website. If you’re braver enough. 

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2017 Market Research Conference Speaker Gender Tracker #MRX #NewMR 

This list shows the gender ratio of speakers at marketing research and related conferences during 2017.

These data are not 100% accurate. I am not always able to identify whether a speaker is male or female based on their name. Online programs aren’t always up to date, and printed programs often change at the last minute and don’t reflect who was actually on stage. If you are able to correct my numbers, I would be grateful for the help.

Please contribute: Some conferences remove their information immediately afterwards. If you have a PDF or image of a conference program, email it to me so I can include it in this list. If you have a paper program, mail it me or do the counts and simply send me the final numbers.

  • ESOMAR Global Qual, Porto, November: 25 female, 17 male=60% female
  • MRS Driving Transformation Through Insight, London, October: 15 female, 12 male= 56% female
  • ⭐️ AMSRS, Sydney, September: 3 female keynotes, 3 male keynotes, 1 female invited, 1 male invited, 28 female speakers, 19 male speakers=53% female
  • ⭐️ MRS, Financial, London, November: 11 female, 12 male=48% female
  • ⭐️ Qual360 APAC, Singapore, October: 16 female, 17 male=48% female
  • ⭐️ TMRE, Orlando, October, 79 female, 88 male=47% female
  • ⭐️ MR and CI Exchange, St Louis, May: 13 female, 16 male speakers=45% female
  • MRIA, Toronto, May: 25 female speakers, 33 male speakers, 6 female panelists, 4 male panelists, 1 female keynote, 4 male keynotes=44% female
  • CRC, Chicago, October: 37 female, 55 male=40% female
  • Market Research Summit, London, May, 22 female, 29 male=43% female
  • ESOMAR Congress, Amsterdam, September: 62 female speakers, 83 male speakers =43% female
  • MRS, Customer Summit 2017, November, London: 6 female, 8 male=43 % female
  • MRMW Europe, Berlin, November: female, male=43% female 
  • IIEX, Amsterdam, February: 52 female, 76 male=41% female
  • MRS, Methodology in Context, London, November: 40 female, 6 male=40% female
  • Customer Experience Strategies Summit, April, Toronto: 12 female, 18 male=40% female
  • Sysomos Summit, February, North Carolina: 16 female, 25 male=39% female
  • Sysomos Summit, September , NYC: 6 female, 10 male=38% female
  • MRIA Net Gain, November, Toronto: 6 female, 10 male=38% female
  • ILC Insights Leadership Conference (Insights Association) Chicago, September, 13 female, 24 male=35% female
  • IIEX, Atlanta, June: 58 female, 108 male speakers=35% female
  • 👎🏻ESOMAR Big Data World, New York, November: 10 female, 24 male=29%female
  • 👎🏻Sentiment Analysis Symposium, New York, June, 14 female, 35 male=29% female
  • 👎🏻Omnishopper International, Spain, November, 4 female, 13 male =24% female
  • 👎🏻CX Talks, Atlanta, October: 7 female, 25 male=22 % female
  • 👎🏻Big Data & Analytics for Retail Summit, Chicago, June: 5 female, 19 male=21% female
  • 👎🏻 Sysomos Summit, June, London: 3 female, 14 male=18% female
  • 👎🏻 Insights50 (Insights Association), Chicago, October: 1 female, 7 male=13% female
  • 👎🏻 AMAART Forum, Seattle, June: 4 female, 32 male=11% female
  • 👎🏻Sentiment, Emotional & Behavioral Analytics, July, San Francisco: 4 female, 36 male=10% female
  • .
  • PMRC Speakers not available online

Gender Ratios of Years Past:

I thought we had this single-gender #speaker thing sorted out in #MRX #NewMR

Some of you might remember a long-running and regularly updated post I created last year listing the gender ratios at marketing research conferences around the world. I stopped doing it because an entire year of data is sufficient for an industry that depends on data to see what’s happening. The data showed that women were vastly underrepresented as speakers at conferences. Conference organizers could see that this was an industry issue, not a “just them” issue. The data gave us the perfect opportunity to make great progress in how we source speakers. 

I’ll admit it can be difficult to see the problem but Twitter and Facebook make the job of spotting single gender panels much easier. Now, I truly don’t care about single gender, or single race, or all young, all old, all differently abled, or whatever the panel bias is. I DO care when the only type of biased panel I ever see is middle-aged, white, male panels. Has any #MRX conference ever had an all black panel not talking about black issues in #MRX ? Or an all woman panel not talking about women’s issues in #MRX? That’s the problem. That’s the statistically improbable problem

So that’s why posts like this are so disappointing. 

There are literally hundreds of Twitter lists labeled as #WomenIn_________, insert industry category. (Some of the relevant lists are on my twitter account.) There are hundreds of websites listing #WomenIn_________.  There are Facebook groups, google plus groups (yeah, tons of techies there!), Reddit groups, you name the digital channel, they have #WomenIn___________ groups. There’s #WomenAlsoKnow. There are thousands of lists of women experts if you look for them. You don’t even need to ask a woman/black person/differently abled person if they know another woman/black person/differently abled person who is an expert on a topic. You just need to know how to use the google. Or the internet explorer if you work for a company that still operates in the dark ages. 

But with that tweet, and the emails that came my way to complain, I guess I ought to do the counts again. I hope that while we may have not reached peak equality, e.g., at least 45% of one gender, we at least have shown improvement. I hope that instead of 35% of speakers being women, that at least 40% of speakers are women. 

Please do send me PDFs of any market research conference agendas you have saved. I’d appreciate the help. So would your friends and colleagues. My gmail address is anniepettit. 

Fingers crossed!

Changing the game: Sports Tech with the Toronto Argonauts and the Blue Jays, #BigDataTO #BigData #AI

Notes from the #BigDataTO conference inToronto 

Panel: Mark Silver, @silveratola, Stadium Digital; Michael Copeland, @Mike_G_copeland, Toronto Argonauts; Jonathan Carrigan, @J_carrigan, MLSE; Andrew Miller, @BlueJays, Toronto Blue Jays

  • There is a diverse fan base across all Toronto teams, and their preferences and values are diverse in terms of who are they and what drives them to watch and attend games. There are many segments of people not just ‘fans.’
  • Fandom takes many shapes and sizes and you always need to grow and rebuild the fan base. You can’t appeal to only avid fans. You must also appear to casual fans. You need to go beyond the narrow focus of superfans.
  • The strategy of loyalty programs is that they are an engagement tool to gather data for mining, generate in-game activation, let people win prizes by participating, help partners better understand the fans, and this creates wins across the board – for the team, the partners, and the fans. 
  • The teams want to learn what people are doing during the game as opposed to guessing. Which benefits do they use their points for, what do they choose at the concession stand, are they watching road games. And this is not just for season ticket holders but people across North America watching games. We need to use the data to learn how to scale beyond ticket holders.
  • People want more meaningful and personal relationships with their sports teams. We need to learn what food they want, what environment they want in the venue, what relationships they want outside of the game. And we need to filter out the noise and deliver.
  • We’ve all done the analogue research. It’s been done for 100 years and it’s not unique to sport. How do we use technology to do it better now. WHO – we need to stop guessing and start using more efficient research. This massive data we have will tell you many things like WHAT do they want. They might want NEW THINGS that you didn’t offer before, an app, an emoji. The data will also ASSIST your team with player recruitment and roster management. We’ve been doing all this for ages and now we want to do it better, more efficiently, most cost effectively.
  • Big data is not free though. Not all stadiums have wifi to do wifi research and it’s expensive to invest in putting wifi in a stadium. We need to spread the cost among multiple agencies.
  • This isn’t a technology project. Rather it’s a people project. For instance, a chef can do vastly more with ten ingredients than I can. We need to change the way we engage with fans and leverage partner relationships. Yes, we’re investing in technology but the focus is people. We need to translate tools for each part of the business, reimagine how we engage with fans, and how we make a profit. You can buy a beautiful car but you need to learn to drive to take full advantage of that new car.

Highlights from Day 1 of the #BigDataTO Conference #BigData #AI

I’ll admit I didn’t have high hopes. How good could a free conference about big data and artificial intelligence be? Especially if the upgrade tickets, which I so frugally declined, were only $75? Well, I was pleasantly surprised.

Let’s deal with the negatives first. The morning registration line was long and it took some people 30 minutes o get through it. The exhibition hall was small with not nearly as many vendors as I am used to seeing at conferences. There was no free wifi in the main hall (um, admission was free so why do we deserve free wifi too?). Sometimes the sessions were so packed, there wasn’t even room to stand. And, some speakers didn’t even show up because, well, airplanes.

However, those negatives were completely washed aside with the positives. Some of the talks were quite good. Some of the speakers were quite good. The topics were quite good. They gave out free conference programs. And did I say free? Some free things are worth what you paid for them. This one was worth a lot more. I highly advise you to go and it’s definitely on my 2018 conference schedule as time well spent.

So, here are a few tidbits of knowledge from a bunch of different speakers that intrigued me today.

  • Data science is often handled at the tail-end of a project. We only take the time to learn what happened after the fact and when it’s too late to do anything about the current situation. We need to do a better job of using our data for the future – for segmentation, targeting, to understand what our customers want, to uncover blind spots.
  • Good data scientists care where the data came from, who created it, what it represents. They don’t just take the data and run it through stats programs and spit out reports. It’s not just about statistics and reporting. Data quality must come first.
  • The real money is not in having the data but rather in knowing what questions to ask. Literally everyone has data but only the companies that hire the smart brains to ask the right questions will succeed with big data.
  • You might think using artificial intelligence is very impersonal. On the contrary. It’s impossible for a human being to be personal with hundreds and thousands of people but AI allows you to be far MORE personal  with thousands or millions of people.
  • Computers and artificial intelligence need to learn the senses – for instance, they need to learn to see the types of moles on skin that will become cancer, learn to hear which wheels on a train are cracked and about to cause a train wreck.
  • Algorithms are what make computer see and listen and as such algorithms are the future. Soon, companies will brag about their algorithms not their data.
  • We need to let computers do the pattern recognition so that humans can do the strategizing and reasoning
  • If you want to work with big data but can’t afford it, have no fears. So much software is free and open source. You can do anything you want with free tools so don’t let dollars hold you back from doing or learning.
  • The danger with artificial intelligence is training it with bad, untrustworthy, biased data. We’ve all seen the reports of AI perpetuating racism because the training data contained racist data. You must choose good datasets that are clean and genuinely unbiased and only then will you find success.

Science Faction: Tomorrowland is here now by Ari Popper #IIeX 

Live note-taking at #IIeX in Atlanta. Any errors or bad jokes are my own.

  • We live in the exponential age, how do we take advantage of this
  • Amazon Alexa, Airbnb, uber, these are causing disruption
  • Driven by emerging technologies; Internet of things, virtual reality, 3D printing, robotics, emerging tech
  • Access imagination of fiction writers, got hundreds of business people to write fiction stories about the future, what executives anticipate the future will be
  • Smart homes, personal assistants, disinter mediated markets, deleting disabilities
  • Smart homes – starts with a sensor, industrial grade technology is making it’s way to consumers, anticipate our needs
  • Personal assistants – will we fall in love with our OS, algorithms will know us, insights will be better if we trust them with our data, SIRI Amazon Alexa 
  • AUtonomous commuting – it’s no longer 20 years away, only thing stopping it is regulation
  • Video of grocery store with intelligence, products talk to you based on what you need and want
  • VR is very immersive and improving fast, profound technology, it hacks the brain and people run into walls [my colleagues tried google cardboard which is a poor mans version and they were screaming and falling 🙂 ]
  • Amazon Alexa – voice portal to Amazon, 
  • We bold and outrageous in predictions about the future, so bold that people won’t believe us, then we will predict the future
  • Storytelling and creativity is the way to get to the future, imagination is more open to anything
  • People are more likely to change their beliefs systems if you tell them a story
  • People plus technology humanizes the future
  • City of the future – anthology of the future – book they put together
  • Lowe’s is doing incredible book in VR, simulation, home improvement simulation 
  • Selling more Alexas now than kindles, there are unintended consequences of having Alexa in the home, kids are treating it like a slave, want people to think about it is a digital Mary Poppins, Teach Alexa to request please and thank you or say things like “what’s the magic work”

Keynote presentation by Ray Poynter (Excellent!) #MRIA16 #NewMR @raypoynter

Live note taking at the #MRIA16 in Montreal. Any errors or bad jokes are my own.

  • [Ray makes a lovely introduction in French. Love it!  ]
  • The large agencies and inside departments will be conducting a smaller percentage of research over time, they are being niched
  • Research WILL become faster and cheaper and in some cases it will become better; this process is accelerating
  • Research will be less about error reduction and more about impact 
  • First driver is customer centricity – do retailers REALLY want to do the right thing for customers?  Sure, but they really want to do better business is this is how to do it
  • The last competitive advantage is your customers, we have to develop ownership and possession
  • Brand loyalty is when people buy your brand against all logic
  • The Panama Canal did not cause people to stop buying bananas because the bananas didn’t take the usual long way around [more giggles 🙂 ]
  • Change is not good for everybody 
  • Big data is a big driver, it’s stealing a lot of budget and delivering relatively little
  • Market research has always been good automation – printing, scanning, auto dialling; we lost a lot of phone interviewers and people typing questionnaires 
  • Artificial intelligence will attack the creative, imaginative part of our work
  • Newspapers are using bots to write copy, journalists just tweak it
  • Democratization of insights – customers are expressing views and want to be heard and involved
  • We are a skill not an industry, “able to use the calculator, I can type” Used to be proud you couldn’t type because it expressed your status
  • Bifurcation of skill and automation – people use automation to become better workers themselves 
  • Big money is in the automated part and big fun is in the small business
  • When you bring money in, you’re no longer a cost center
  • SurveyMonkey is the biggest survey company out there, it is the democratization of insight, bypassing the ‘researcher’ to do things yourself
  • Separation of the skilled and the automated 
  • Do you need a print room? Fax room anymore? No, you can form a brand new company without any formal business needs we used to have.
  • How do we thrive on change
  • Get closer to customers – ethnographer so, anthropologists always did this
  • Quant researchers need to do this, we need to personally hang out in online communities, with real people to see what brands and products are all about
  • Integrate with the rest of the business – volunteer to work with other reas of the company [NEVER say no one asked me to]
  • Understand the language in finance and human resources, don’t improve our language on them, don’t impose our use of the word “significance” on everyone else
  • Be an automation winner – try to be the person who implements automation, the person who pilots it, there is an ongoing role for being an expert
  • If you’re in a company that doesn’t want to automate its processes, move companies
  • Be an improvement enabler – if you aren’t the best, do whatever you can to help the top 1% people be the best
  • Use market research as your edge
  • Rays insight for people joining the work force – don’t do want you love. Thousands of people will be better than you at it. Join a different industry and then you WILL be the best in that industry when people need that skill.
  • Learn a new skill every year – Ray is learning Japanese [really impressive!], it will push you to where you are uncomfortable and that’s not a bad thing, it doesn’t even matter what, but may it a class on how to be a CATI interviewer [chuckle 🙂 ]
  • Automation will affect professionals – doctors, lawyers, researchers, and it won’t be one change, Uber was disruptive but soon when there are automated cars, Uber will be out of business too
  • People don’t always want cheaper or better, templated surveys that do NOT change is very liberating and cheaper to maintain, more cost and customized surveys isn’t always what people want
  • [ray is a great speaker, every time, guaranteed. 🙂 ]

Neuroscience and growing employee engagement with research #MRIA16 

Touch to sell: neuromarketing’s full toolkit to captivate the senses by Diana Lucaci

  • We need to bring more science into the boardroom
  • If I’d asked people what they wanted they would have said faster horses – we need to eliminate bias, eliminate response bias and social desirability
  • System 1 is when you slam on the brakes without thinking
  • We can measure using biometrics or neuroscience – facial expression, eye tracking, heart rate, skin response
  • There are consumer and medical versions of tools, like how a Fitbit is not a medical device
  • Biometrics are unidirectional – it could happen for any reason whether happy sad disgust or fear; this is why you combine with neuroscience
  • You can test physical media like postal boxes and also emails and scent and sound
  • What happens when you add scent to physical media and digital media
  • When you like what you’re looking at there’s more action in the frontal lobe
  • Cognitive load is lower for physical rather than digital
  • Unaided brand recall is better for physical
  • Physical is more persuasive and motivating 
  • Digital captures more attention based on time looking at things, but only because they’re trying to make sense of it which means it’s not as motivating or persuasive
  • Nothing compares to the instore experience, interacting with an item makes you more likely to purchase it
  • Need to make sure your storefront is noticed, eyes are drawn to faces particularly if the face is directly pointed to you, turn the face and people will look at other parts of the ad [how cool is that!]
  • Look at the CBC marketplace episode on retail tricks – how stores make you spend more
  • Decision fatigue is real
  • Sell to your tribe not to everyone
  • visual attention is automatic and quick
  • Humanize your customers and create mobile experiences that delight and add value to their lives


From survey to engagement – a journey of research and organization evaluation by Claude Andres and Amy Charles

  • Regularly get Canada’s top employer awards
  • Rely on data from employee survey to do this
  • Old program was “father knows best”, HR would tell everyone what to be happy about
  • Established a sample survey in 2006 and then redid a census survey in 2007 to include every ministry, 2009 added signifciant demographic data
  • How do you measure firefighters, swimming instructors, and policy analysts who are all employees
  • They need a common language but they need to talk to completely different kinds of people
  • Needed to work on data collection AND reporting
  • Reports used to show lots of numbers and metrics and they were boring [DATA IS NEVER BORING!  🙂 ]
  • Reports evolved into guidebooks supported by data portals
  • Broken window theory – if you break one window, lots of kids will keep doing it. Must stop it before it grows
  • Don’t make assumptions too quickly – surveys kept asking about fairness of hiring and people always said no. We think they don’t understand how boring works so let’s teach them what we do. But it turns out the more they knew the unhappier they got. But even people who got the job didn’t like the process.
  • Happy employees do not equal engaged employees
  • When the metric is the measure, you’re on a slippery slope. If you watch your speedometer so you don’t speed, you will get into an accident.
  • Can’t change compensation without getting input and informing ahead of time, people need to learn ahead of time and be given time to understand

Panel: People as Proxy #MRIA16 

Live note taking at #MRIA16 in Montreal. Any errors or bad jokes are my own.

Panel with Sean Copeland, Evan Lyons, Anil Saral, Ariel Charnin, Melanie Kaplan

  • Timelines are very compressed now, instead of two or three months people are asking for hours to get answers
  • It’s no longer 20 minute questions but quick questions
  • Market research is often separate from data science and analytics but this team has put them together
  • They don’t have to answer questions with surveys because they have the raw data and they know the surveys probably won’t be able to answer them accurately; they know when to use market research so that it is most effective
  • When is MR the right solution and when do they partner with data scientists 
  • There is a divide between MR and data science which is strange because our goal of understanding consumers is the same
  • We can see all th transactional data but without MR you miss the why, the motivator, one method doesn’t answer the entire question
  • We need to train and mentor younger researchers [please join http://researchspeakersclub.com ]
  • Some mistrust of quantitative data, are panels rep, why do the numbers change month to month, reexploring Qual to understand the needs and wants, clients remember specific comments from specific focus groups which helps the time to see the issues
  • A doctor is still a doctor even when they use a robot, the same is true for consumer insights with surveys and data science
  • Don’t be protective of your little world, if a project comes to you and is better answered by another method then you are wise to pass it to those people
  • You need to appreciate what MR offers and what analytics offers, both have strengths and weaknesses you need to understand
  • A new language may be morphing out of the combination of MR and data science
  • Everyone believes they are providing insight, of course both sides can do this whether it’s projects and models and understanding the why, insights need to be both of these
  • Still need to be an advocate for MR, can’t just go to data science very time even if it’s the new great toy
  • Live Flow Data – is this a reality, it will happen, can already see 5 day forecast of weather and know about upcoming conferences and how many tickets were sold for a week from now; monthly assumptions from data could happen
  • They can see the effects of ads immediately in live data
  • They don’t want to hear what happened yesterday, need to know what’s happening now
  • Future of our business is understanding people and solving problems, you always need more information to do this; if you learn new things, you can do more things and solve more problems
  • Need more skills in strategy and merging with insights, don’t just hand off reports, help clients take insights and turn them into the next initiative 
  • Is it one story or multiple stories after you’ve got all the data put together
  • Don’t just deliver a product and then leave it, our results are only as accurate as the people who interpret it; research can say a hamburger should look exactly like this but when the end product designers change all the tiny little things to be more convenient then you wine up with a completely wrong hamburger in the end

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