The entirety of this post is available on the Gender Avenger website.
Why are women underrepresented as speakers?
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.
Market research isn’t straight forward. As much as you believe one thing, I’ll completely disagree and believe another. Which brings us to this series of posts. Over the coming weeks, a colleague and I are going to tackle an important research question. I’m going to make one argument and they’re going to argue the exact opposite. Do we each have the same opinion, do we each believe in our own argument, are we arguing against our real opinion? You figure it out. Here goes!
Keza Kyanzaire: Telephone interviewing should die
CATI or Computer Assisted Telephone Interviewing Software has been helping market research companies interrupt people during dinner since the dawn of time (the proliferation of the internet). I admit, there are advantages to this form of interviewing, especially considering that when it was originally conceived, the alternatives included going door to door with a clipboard. However, it’s past time to put telephone interviewing to pasture.
According to the CRTC’s 2016 Communications Monitoring Report, more Canadian households exclusively use mobile phones over landline phones, and the shift away from landline phone usage continues to increase. Telephone interviewers are increasingly beginning to reach respondents on mobile phones. Respondents on mobile are often younger, and age can have an adverse effect on the willingness to participate in research and the quality of the data. As well, a respondent reached on a mobile phone may not be in an environment conducive to completing an interview.
Let’s not even mention the fact that telephone interviewers are often mistaken for telemarketers, which comes with its own set of issues. Market researchers have always had to fight to distance themselves from telemarketers in order to establish trust with participants, and legitimacy in the work we do.
Web-based surveys are an effective alternative to telephone interviews. Not only are they a cost saving alternative, (staffing call centres can be extremely costly and they tend to have high turnover rates) they allow the respondent to decide where and when to participate. Particularly in the case of MROC’s or panels, respondents can choose to participate in surveys that are of interest to them and are more engaged and active. Moreover, web-based surveys allow for visual stimuli and more interactive elements that not only make for a better experience for the respondents, but also allows for a depth in the types of questions that can be asked, and data that can be gathered.
Telephone interviewing has been a great resource for the industry, but with the advantages of web-based methods, why are we still running call centres in 2017. It’s past it’s prime and it is time that they are replaced by more effective and advanced methods.
Keza has an Honors Bachelor of Psychology with a Specialization in Cognition. She also completed Humber College’s Research Analyst Postgraduate Program, where she learned how to conduct both social and market research. Keza put her education into practice as a Research Analyst Intern at Numeris, where she conducted statistical analysis and helped inform business decisions. She is now a Researcher at PATTISON Outdoor. And she’s an experienced speaker having taken the stage at #IIeX in Amsterdam.
Annie Pettit: Telephone interviewing is the best thing since sliced bread
I’m not a gambling person but I’ll bet you know about research panels. Well, you’re in the minority. Most people who are not in the market and social research industry don’t know that panels exist. Which begs the question -who are these people who know about research panels? Are they just people desperately seeking places to earn money? What causes some people to seek out incentive based activities? One thing I know for a fact is these people have internet. They can afford monthly internet charges, maybe even high speed internet charges, as well as the cost of a device to access the internet. Internet might be a right but it still is a luxury for many people. Let’s check our academic privilege at the door. So how do we gather opinions from people who aren’t aware of research panels or couldn’t participate in them even if they wanted to?
One alternative is mall or central location research. Send your best face to face interviewers to the nearest mall and have them randomly interrupt passersby. Well, first of all, not everyone lives near a location that is large enough to warrant sending an interviewer. Sorry residents of Nunavut, you’ll never be asked to participate in mall research – your town isn’t large enough for your opinion to matter. You live in Vancouver, Calgary, Saskatoon, Montreal, or Halifax? Your chances are pretty good of meeting up with a mall interviewer – unless you’re one of those people who takes a ten minute detour through the parking lot to avoid the person with a clipboard. Besides, anyone who’s taken an introduction to research class knows what accidental sampling or convenience sampling is. Goodbye ability to generalize to a larger population if mall research is your game.
Which is why I’m a huge fan of telephone research. As it has been since the invention of polls, telephone research continues to lead the fight against self-selection bias. If you’ve never heard of research panels, if you can’t afford high speed internet, if you don’t live in a major city, if you avoid people at the mall, telephone methods still value and seek out your opinions. We are currently living through a time where people don’t trust the polls. Let’s not revert to methods that make it even easier to distrust polls.
If you’re an early career researcher, data scientist, data visualizer, marketer, or similar, and would like to write a #PointCounterpoint article with me, please send a gmail to my full name anniepettit and let me know what topic interests you. Maybe I’ll pick yours!
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 day, and printed pograms 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 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, 18 male=66% female
- ⭐️ AMSRS, Sydney, September: 3 female keynotes, 3 male keynotes, 1 female invited, 1 male invited, 28 female speakers, 19 male speakers=53% female
- ⭐️ CRC, Chicago, October: 34 female, 34 male=50% female
- ⭐️ MRS, Customer Summit London, November: 6 female, 6 male=50% female
- ⭐️ MRS, Financial, London, November: 11 female, 12 male=48% 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
- ESOMAR, Amsterdam, September: 62 female speakers, 83 male speakers = 43% female
- IIEX, Amsterdam, February: 52 female, 76 male=41% female
- IIEX Atlanta, June: 58 female, 108 male speakers=35% female
- ESOMAR Big Data World, New York, November: 11 female, 22 male=33% female
- 👎🏻 Insights Leadership, Palm Beach, September: 8 female, 23 male=26% female
- 👎🏻 AMA ART Forum, Seattle, June: 4 female, 32 male speakers= 11% female
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.
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.There are literally hundreds of Twitter lists labeled as #WomenIn_________, insert industry category. (Some of the relevant lists are on my
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.
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.
Cognitive Analytics: Enabling assisted intelligence in human resources recruiting and hiring by Noel Webb, @CognitiveHR, Karen.ai, #BigDataTO #BigData #AI
Notes from the #BigDataTO conference in Toronto
- He realized that HR teams were spending too much time prescreening resumes before they could even meet with the best candidates
- Recruiters only spend 6 seconds reviewing a resume which means they end up accidentally discarding some of the best ones. Time crunches mean they may only be able to get through 20% of candidates. ML can solve these problems .
- 75% of candidates who apply to jobs do not hear back from the company because there are simply too many candidates and not enough time to do so. NLP and chatbots can solve this problem.
- AI will not steal all jobs but it will automate processes and allow you to engage with potential hires in a more meaningful way.
- Shortlisting is a huge challenge for HR as reducing a huge list of resumes into a screened list takes a lot of detailed attention. Technology such as direct keyword matches aren’t the best option as they eliminate people with relevant skills but not the exact words. For instance, know R is just as good as knowing SAS but a keyword search wouldn’t know that. NLP would work much better.
- Personality insights can also be collected using sentiment analysis to get a functional understanding of the Big 5 Personality traits. [Wow, I can’t imagine how valid it is to do personality assessments with resumes which are often written by third parties and without traditional grammar and style]
- Chatbots can take an applicant through hiring and onboarding processes by answering questions that would normally be asked of an operations officer. [imagine how many stupid questions the chatbot would be asked that new hires are too scared to ask people]
The ideology of data by Sasha Gryjicic, @SashaG, Dentsu Aegis Network, #BigDataTO #BigData #AI #Intelligence
Notes from the #BigDataTO conference in Toronto
- Data marketing and artificial intelligence are headed in the wrong direction
- Marketing is the pursuit of convincing someone they need something, marketing is a commercial outcome to propel the broader economy forward, marketing uses media and communications to convince, largely based on human language
- Data is a digital expression of something in the world, organized and stored in many ways. We are finally getting the external world to use a single language but we can’t read this language. Humans don’t read binary code or extrapolations of code.
- Data violates the notion of scarcity and data is almost always out of both time and space context for necessity. Data is necessarily incomplete and it is not that thing itself. Data has inherent biases, is super messy, and contradictory
- We use data to optimize things that have already happened, or we generalize what we learn from data to engineer more of those outcomes, e.g., when managing an online store, we optimize data to get optimal business outcomes but this doesn’t help us learn why or what drives the decisions
- Intelligence is the ability to gather, category, organize inputs, store, reflect, and respond to them. For humans, intelligence is innate, structured, organized, and process oriented. We have a fixed capacity of intelligence and are creative with it. It is not the result of external stimulus.
- Language is the best way for humans to get access to our intelligence. It’s the language we use when we think. We talk to ourselves more than we talk to others.
- The AI we’re building is like automated statistics. We brute force relationships and create a black box of intelligence. We don’t understand why a computer makes certain decisions because we cannot hold enough variables in our mind to understand. Are algorithms intelligence or optimization? We are drifting further from understanding what intelligence really is. It’s not AI at all.
- We’re accelerating the fatigue of positive reinforcement. We’re following bad after bad. We’re heading away from language which is the only way to understand ourselves.
- Intelligence seems to include morality, the ability to store and reflect and take decisions based on reflections.
- We need to back away from disorganized data. We need to pause and relfect on what we see in the data to understand ourselves better. We need to dive into our own intelligence better. Reflecting on something is more important that acting on something.
Future of the smart home by Emily Taylor and Manish Nargas, IDC Canada, #BigDataTO #SmartHome #ConnectedHome #AI
Notes from the #BigDataTO conference in Toronto
- By 2020, every home will have 40 connected devices – TV, appliances, health, assistance, security
- Wearables help consumers track and log their activities such as wellness goals, athletic training, weight loss monitoring, medication reminders, gamification of activities. 1 in 5 Canadians currently own a device as a wristband or a watch and 70% of those owners have no plans to upgrade or replace. 60% of consumers are not interested in wearables at all. Designs will be less obvious, have improved battery life, and use new materials like smart fabrics. Medical devices will have better reliability and validity and this will help the healthcare sector and be relevant for insurance companies
- Security devices – smoke alarms, motion sensors, doorbells, security systems, remote home monitoring. These devices offer peace of mind. It’s no longer about emergency services but monitoring to see if the kids are home, a window is opened, the jewelry box is still there, perhaps even see if it’s a friend or foe at your front door.
- Home automation – these devices will help us reduce energy usage, increase safety including devices such as thermostats, light switches, outlets, appliances. IKEA has launched a smart home lighting system with wire-free lighting at a lower cost than their competitor. They will bring this technology into every piece of furniture and curtains [window blankets 🙂 ]
- Personal health devices – These devices will result in increased awareness of monitoring. Health monitoring will take place from the home not a hospital and will result in fewer trips to the doctor and hospital. Connected clothing will help with this. Gym equipment brands now sync with health monitoring devices so you can monitor treadmill and walking together and get more consistent results.
- Intelligent assistants/bots – more natural way to interact with machines, removes the complexity of interconnections, vocalizes thought and activity, uses real time machine learning. Low adoption rates in Canada but many bots aren’t available in Canada. Connecting a speaker to the internet isn’t revolutionary but it can improve personalization. 60% of Canadians don’t care about bots but bots are here to stay. It is Alexa and soon will be your butler. It will be ubiquitous.
- There are gaps. Many devices are siloed right now. They have limited conversations with other smart home devices. The market is too focused on DIY right now as people want to solve specific problems not do the entire home in one shot. There is little support across the solutions.
- Do you need a smart-fork that monitors how quickly you eat? Do you need this fork to connect to your lights and smoke alarm?
When will we drive autonomous vehicles, by Kashmir Zahid, Ericsson Digital Services (Great talk) #BigDataTO #BigData #AI #Automation
Notes from the #BigDataTO conference in Toronto
- 1996 GM introduced Onstar. It had a weak interface, few features, and was mainly designed to offer roadside assistance.
- 2010 saw in-car navigation but it still wasn’t user friendly nor easy to operate while you were driving.
- 2012 Tesla built an all electric car and people could finally see the possibilities of vehicles with electricity and connectivity. Now that vehicles had so much digital, manufacturers could no longer stay in the shadows and let dealerships handle all the consumer interactions.
- 2014 Apple CarPlay and android auto were introduced. Connectivity was embedded in the car from the time it was installed in the factory as opposed to being added by the consumer after the fact.
- 2015 remote diagnostics are now available, repairs can now be recommended by the vehicle rather than going to the dealership or following the manual.
- 2015 Tesla creates autopilot, a self guiding car but the user is still expected to take physical control when needed.
- 2017, the Google car is no longer a science project, it is a reality.
At CES, three trends were noted
1. cars will be integrated into your life and communicate with your personal device, e.g., your home will be ready to receive you when you arrive, the temperature is set appropriately, the lights are turned on, the garage door is opened, and the turkey is ready to be taken out of the oven
2. Automation will create a natural experience of talking to your car, Alexa is winning here [although it just accidentally bought Whole Foods so I don’t know about the quality at this point]
3. Car to car communication – this will allow vehicles to see and talk to each other, so they can maintain speed and safety among other cars on the road
- Now that everything talks to everything, our user experiences will be completely transformed.
- By 2020, 90% of cars will be connected
- 4 trends in the industry
1. Cars must be connected, software defined car
2. Electrification, ITS, infrastructure
3. Automation, connected automated mobility
4. New business models, multi industry ecosystems
- This is the largest change in transportation since Ford’s model T
- Soon, we will have everything we need to travel but we won’t own the car. [Think of music, we no longer own the music we buy and we could lose it instantly if Apple decides to shut something down]
- Insurance will depend on how you drive, your telemetrics. And later on, insurance won’t be necessary as human drivers won’t be responsible for safety.
- Emergency assistance providers will be affected as cars will have embedded systems that alert first responders instantly to ask if you are safe.
- Government providers will need to reconsider what legislation is needed to take care of drivers and roads.
- 13 out of 14 of the big vehicle manufactures plan to make an autonomous vehicle in the next couple of years
- Google, Apple, Intel, Microsoft and Amazon have focus and investment in self driving car projects. Telecom operators like AT&T, verizon, Vodafone see the potential of new revenue in self driving car. Uber, Lyft, DIDI and many other startups are trying to disrupt the traditional car ownership model.
- The passenger economy will be worth $7 trillion by 2050.
- We are about to see consumer mobility as a service – one stop shop for transportation for everyone who doesn’t own a car [this is amazing for people who don’t know how to drive, are too old to drive, too young to drive, not well enough to drive]
- This will save over half a million lives due to safety from fewer accidents. And, it will free up your time since you don’t have to physically drive.
- We are two years away from letting people sleep in a Tesla on long road trips where the car has not made the trip before – Elon Musk
1. Public safety – people need to trust the machine to work while they sleep.
2. Data privacy and security – who has, uses, and sells my data. It’s not transparent right now.
3. Rules and Regulations – Who is liable for an accident? Who owns the vehicle that caused the accident?
- Connected cars will open multiple innovative services.
- They will improve the efficiency and security of new value added services for both consumers and enterprises.
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.
- 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.