Tag Archives: conference

The audience doesn’t care about your company and other tactical tips for conference speakers

As a conference speaker, the best sales pitch you can offer on stage is a presentation that educates and entertains the audience. One that explicitly shows them you understand what the audience needs.

I chat with a lot of speakers who assure me they didn’t do a sales pitch and then are astonished to find out that they did. I also chat with other speakers who are so paranoid about NOT doing a sales pitch that they strip out all the good parts of their presentation. Fortunately, there are some easy things you can do to prevent both of these situations.

Ban these words

Never say the word we. Never say the word our. Never say the word us. These tiny unassuming words automatically turn the most glorious presentation into a horrid sales pitch. And your audience has no need for a sales pitch. They are sitting in front of you because they are desperate for knowledge and insights. They want to know your personal opinion, what you have discovered from your techniques. They want to engage with and listen to you as a person. They’d rather not tweet how boring and out of touch you were.

Don’t name-drop your products

Companies spend thousands of dollars trademarking brand names. While it’s helpful to have names so that your employees and your clients know that they’re all talking about the same thing, no one in the audience cares about your cutesy names. They don’t care that you use SalesForce or SurveyMonkey. They care that you understand marketing and research. So if you find yourself wanting to say the name of a tool while you’re talking, instead simply say ‘these types of tools’ or ‘these types of companies.’ I can assure you that you don’t need to use any of your brand names or trademarked names in your presentation.

Don’t describe your company

Your audience doesn’t care about your company and they certainly don’t need you to present a detailed explanation of all the products and services your company offers, even if that slide only takes 3 minutes. That slide explaining your company needs to be turned into a discussion of how your specific topic impacts the industry. Don’t tell the audience that Annie Pettit Consulting is a business that combines artificial intelligence and eye tracking. Instead, tell the audience that eye tracking has seen huge advancements with the application of artificial intelligence. Strip out the branded content and focus on the educational content.

Don’t describe your company philosophy

Don’t waste valuable presentation time talking about your company mission and philosophy. It is not important for the audience to understand your company philosophy in order to understand the research. The audience doesn’t need to know that your company believes research should be easy. The audience DOES need to know how research can be made easy. They also don’t need to know that your mission is to solve problems. Instead, explain to them how research processes can be used to solve problems.

What is your reward?

If you do a great job of educating and entertaining your audience, they will line up to ask questions, get your business card, and they will email you afterwards asking for advice and copies of your presentation. Guaranteed.

Sincerely,

Every person who’s ever sat in a conference audience

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2018 Market Research Conference Speaker Gender Tracker #MRX #NewMR

Diversity - market research speaker trackerThis list shows the gender ratio of speakers at marketing research and related conferences during 2018.

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.

And yes, there is far more to diversity than gender. Diversity of age, ethnicity, ability/disability, sexuality, and more also matter. But let’s at least measure what we can from conference programs.

Please contribute: If you have a PDF or image of a conference program, email it to me so I can include it in this list.

FYI, I put a ⭐ beside any conference between 45% and 55%  and a 👎🏻 beside any conference under 30% or over 70%.

 

  • QRCA, Arizona, January: 19 female, 7 male=73% female (Qual research has more female than male specialists)
  • Qual Worldwide, Spain, May: 20 female, 9 male = 69% female
  • Qual360, Washington, March: 17 female, 11 male speakers = 61% female
  • ESOMAR World, Amsterdam, March: 15 female, 11 male = 58% female
  • Customer Experience Strategies Summit, April: 15 female, 12 male=56% female
  • NewMR Festival, online, February: 16 female, 13 male=55% female
  • IMPACT MRS Annual, March:  45 female,  42 male = 52% female
  • ⭐ Market Research Summit, London, May, 18 female, 18 male = 50% female
  • ⭐ ConsumerXscience, The ARF, March, New York, 24 female, 25 male= 49% female
  • ⭐ Africa Forum 2018 AMRA, Nairobi, February: 19 female, 20 male=49% female
  • ⭐ MRMW APAC, June: 9 female, 10 male = 47% female
  • ⭐ MRMW NA, April: 21 female, 24 male = 47% female
  • ⭐ MRIA, Vancouver, May: 25 female, 30 male=45% female
  • Sentiment Analysis Symposium, New York March, 9 female, 10 male=45% female
  • The Insights Show, London, March: 19 female, 25 male= 43% female
  • CX Next, Boston, April:  10 female,  13 male = 43% female
  • TMRE IN FOCUS, Chicago, May: 10 female,  13 male = 43% female
  • Quirks LA, January: 45 female,  63 male=42% female
  • Insights NEXT, April, New York: 28 female, 38 male=42% female
  • Customer Experience & Digital Innovation, San Francisco, April: 5 female, 7 male = 42% female
  • ESOMAR MAIN FEST Latam, Buenos Aires, April:  23 female,  33 male = 41% female
  • Quirks Brooklyn, February: 55 female,  81 male=40% female
  • FUSE Brand & Packaging, New York, April: 19 female, 28 male = 40% female
  • SampleCon, February, Texas: 13 female, 25 male = 39% female
  • IIEX, Amsterdam, February: 50 female, 84 male=37% female
  • Qualtrics experience summit, March, Utah, 32 female, 57 male = 36% female
  • IIEX, Atlanta, June: 44 female, 85 male speakers = 34% female
  • Sysomos Summit, February, New York: 6 female, 12 male=33% female
  • Sysomos Summit, London, April: 4 female, 10 male = 29% female
  • 👎🏻 Insights CEO Summit, January, Florida: 4 female, 13 male = 24% female
  • Insights50, May 2, New York: 1 female, 4 male=20% female
  • 👎🏻 Sawtooth conference, March, Florida, 12 female, 58 male= 17% female

—————————————————————————————————————–

  • MRMW Europe, September:  female,  male = % female
  • PMRC : female, male=% female
  • AMAART Forum, June: female, male=% female
  • AMSRS, September:  female ,  male =% female
  • Big Data & Analytics for Retail Summit, June: female, male=% female
  • CRC, October: female, male=% female
  • CX Talks, October: female, male= % female
  • ESOMAR Big Data World, November: female, male=%female
  • ESOMAR Congress, Berlin, September: female speakers, male speakers =% female
  • ESOMAR Global Qual, November:  female,  male=% female
  • ILC Insights Leadership Conference (Insights Association), September, female, male=% female
  • Insights Corporate Researchers Conference, October, Florida: female, male=% female
  • Insights Leadership Conference, November, San Diego: female, male=% female
  • MRIA Net Gain, November, Toronto: female, male=% female
  • MRMW Europe, November: female, male=% female
  • MRS Driving Transformation Through Insight, October:  female,  male= % female
  • MRS, Customer Summit , November: female, male= % female
  • MRS, Financial, November: female,  male=% female
  • MRS, Methodology in Context, November: female, male=% female
  • Omnishopper International, November, female, male =% female
  • Qual360 APAC, Singapore, October: female,  male=% female
  • Sentiment, Emotional & Behavioral Analytics, July: female, male=% female
  • Sysomos Summit, September: female, male=% female
  • TMRE, October, female, male=% female

Gender Ratios of Years Past:

How do speakers see themselves? A survey of Speaker perceptions

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

.

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. 

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. 🙂 ]

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