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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

Keynote presentation with Antoinette Benoit, SVP and CMO of McDonald’s Canada

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

  • Being a learning company
  • Only way to become a meaningful brand
  • 90% of brands could disappear because people do not care about them, that’s why you need to be a learning company
  • They understand the need to keep learning
  • People were not happy that she worked with McDonald’s, they didn’t think it was a good company
  • Why do people attack McDonald’s, why do people misunderstand the company
  • They don’t want to be junk food, they want to be a beautiful restaurant, like what they’d have at home
  • Some people didn’t like it because they didn’t recognize it without balloons and plastic decorations
  • Evolved from fry hamburger Coke trinity to a larger offering with salad, fruit, new bread
  • Try to explain that fries are real potatoes because people just didn’t believe it, just just have huge scale so they need to use factories
  • They had to learn how to talk about creating their food
  • They needed to understand the local culture of France to make all the changes work
  • They never explained their company properly so of course people misunderstood them
  • Need to use a common language with a global brand
  • From fast food to good food fast is an extension of what France initiatives in the 90s,  a vision that every country initiated in their own way
  • McBaguette was a local offering [that made me giggle]
  • Each country needed their own consumer research to see what would resonate in Austria, Germany, Sweden, and elsewhere
  • It remains an American brand affected by American politics and country specific challenges
  • Shared 20 insight tools with at least 10 companies – engagement process, corporate barometer, qualitative brand audit, price sensitivity
  • Used the tools to speak the same language and have a high tech understanding of the countries and what might work in one but not in the other
  • We relentlessly learn how to learn, not everyone has the same attitude towards learning, need people who are not afraid of change, it’s second nature for some people
  • Canada is the second lead international market after Australia (USA is the lead national market)
  • 85% of sourcing in Canada is Canadian (we can’t produce enough salad all year round), 23000 families use Ronald McDonald House in Canada
  • We can’t be seen as Canadian but we should be seen as part of the Canadian world
  • More consumer led now as opposed to operationally possible
  • They were called consumer and business insights and these groups were separate, but now they are combining them
  • We’re not always great partners but they are working on that
  • Changed their name to strategy and insights to show where they want to go and the role they want to play
  • They post test every piece of creative from Canada and globally, 3 hours focus groups that really surprise the moderators including money earned and lost
  • Need to understand totality of consumer lives not just when they’re in the restaurant
  • They go beyond product testing, to include understanding eating habits in Canada and how to adapt to those, what do they eat in home versus out of home, what the segments beyond just demographics
  • Brands have personality, consumers know what the are functionally but not who they are in their heart
  • Need to have a consistent voice about who they are
  • Nielsen nicely packages data for most thing but in McDonald’s they have a big mess of data that needs to be crafted into something that tells a story, gets to the insight that seasoned researchers and newbies can understand
  • You can’t cheat when you’ve got data in front of you

I am now a Fellow of the MRIA #MRIA16 

This is surreal.

If you look at the list of MRIA fellows on the website, it’s a group of people who created market research in Canada. People who put Canada on the map. People I’ve admired from afar for years, never quite brave enough to say hi to. Strangely now, it seems that I have joined their ranks. This is undoubtedly the biggest honour of my career and I hope I can continue to do my colleagues and friends proud.

Thank you to Chris Commins for believing in me and nominating me. Thank you to the MRIA Board of Directors who felt that my work in the marketing research industry warranted recognition. I am truly grateful and honoured.


Keynote presentation by Thierry Bransi, Director of Commercial Insights and Planning at Metro Richelieu Inc. #MRIA16 #NewMR 

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

  • There are a lot of pessimistic people in our industry, research won’t be around in fifteen years, another said researchers only end there by default, research isn’t well positioned for the future
  • Want to share my optimism
  • We start in sales, then go to marketing, then go to research to dictate products; this paradigm is disappearing, more and more people are arriving in research by choice [me!]
  • The research function is growing
  • Going from information collectors to actors within an organization
  • All the science in our industry is exciting
  • Background of Metro, the banner, the organization, Metro Inc, Metro Ontario
  • In 1947, retailers got together and changed name in 1956 to Metro, they have a long history
  • In 1992, acquired Stienberg, pivotal moment when new president arrived, Steinberg company was going bankrupt 
  • President weathered through crisis, market conditions were unfavourable, became profitable and made many acquisitions 
  • 3rd company in its sector, 65000 employees, 12.2 billion in sales
  • Possibly the best grocer in the world as stated by reviewers
  • New president reaffirmed the client focus
  • Suppliers collect information and do the analysis and they think the work ends there, but for us that is the beginning of the analysis stage
  • What do we do with so much data, consumer information is vague, ownership of information isn’t clear
  • Some data collection processes are becoming more popular, anyone can do research, there is automated research that happens without anyone asking for it, more people can do research
  • Traditionally analysis looked like investigation and telling a story, the best story became the king
  • But today, most intelligence has to do with merging information which is more democratic, everyone analyzes and generates insight
  • We’re dealing with a ten thousand piece puzzle now, so now we say our puzzle is sexier but that doesn’t always work
  • We used to wait beside the fax machine to get our sales numbers, but this old method balances thought and analysis; now we can throw piles of numbers on a page without thinking
  • Now we build 500 slides and then tell our story, maybe we need to rethink this
  • To have an impact, we need to develop a network

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