Tag Archives: tracking

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:

Holiday Shopping with All Screens 24/7 by Maria Domoslawska, and Roddy Knowles #FOCI14 #MRX

Live blogging from the #FOCI14 conference in Universal City. Any errors or bad jokes are my own.foci14

Holiday Shopping with All Screens 24/7
Maria Domoslawska and Roddy Knowles, RESEARCH NOW

  • multimode starting with online, going to metered PCs, mobile phones, and then mobile diary GPS tracking which adds more engagement and gets us closer to the consumer
  • sample sizes of thousands, conducted around Thanksgiving
  • partnered with Experian for Hitwise data, tracked the top 20 retailers
  • 3% of people used mobile for purchasing but we will see mobile did play a central role
  • saw spikes for black friday and cyber monday on PC traffic
  • even though people aren’t necessarily shopping on their phone, the phone is with them at all times, they use their phones while they shop a lot more than they used to
  • 40% of consumers say they LIKE the huge crowds in the UK, and plan to do this
  • 31% of people made only unplanned purchases, impulse
  • 26% made planned and unplanned purchases
  • purchase planning by store – more unplanned at CJP, Macy’s, Kohl’s – clothing stores
  • in the UK, they plan to buy after Christmas, they give gifts on boxing day because they know they can get a better deal
  • more planned purchases for bigger ticket items like appliances and electronics
  • flowers and small gifts were mostly unplanned
  • ask people to take photos of their receipt – would people actually do it? and are panelists buying what they said they bought – did they forget something?
  • used the mobile diary to determine if people could actually find what they were looking for
  • were able to capture basket spend via diary data and determine average expenditure by store, matched well with responder data so people were able to recall quite accurately
  • 74% say gift giving makes them feel good
  • Key takeaways:
    • multi-country tracking help find subtle changes in your market which can become a big creative idea for your next program
    • mobile is in shopper’s lives and will not go away. it is an essential device for shopping even if they aren’t actually purchasing on their device
    • getting close to the point of experience yields rich and accurate data on shopping behaviours
    • not every datapoint is going to add value but layering survey data with new variables of passive behavioural data can activate your hypothesis

Other Posts

Building a Social Spine in Tracking Research by Larry Friedman #FOCI14 #MRX

Live blogging from the #FOCI14 conference in Universal City. Any errors or bad jokes are my own.foci14

Building a Social Spine in Tracking Research
Larry Friedman, TNS

  • turn tracking research from a rear-view mirror into something that is windshield oriented, looks to the future
  • historically tracking is survey research
  • lots of information is observable, passive, that we didn’t have available before – social data
  • we need to use obtrusive (surveys) and unobtrusive research together, MR rarely uses unobtrusive methods
  • The Trouble With Tracking – insights not actionable, flat-line metrics, far from real time, long vague surveys
  • is social media data a real alternative? real time, sensitive to events real consumers talking to each other in their own words at their own pace
  • has social worked? hasn’t made the case yet, mostly because there is a lot of discussion and it’s not always what we’re interested in, hard to find the signal over all the noise. hard to understand what the metrics really mean. people want a one-to-one metric with external measures as in how we compare survey metrics
  • what does the number of negative mentions in social really mean? how does it compare and to what?
  • some worry that social isn’t representative because not everyone is online, not everyone uses these tools [and everyone participate in surveys??]
  • the real issue however is predictability. even if it’s not representative, if it predicts it’s got value [heck yeah! first time I’ve ever heard someone other than myself shout this out]
  • should social data look the same as survey data? why should it? shouldn’t it really be different?
  • great success in predicting brand health by putting social and search data in the model
  • data cleaning is extremely essential, discussion must come from the correct geography – avoid english conversations around the world, avoid the coupon and sales bots, ensure you are getting apple computers not apple pie
  • social lets your track your brand equity today instead of 3 weeks from now when something major could have happened and been dealt with
  • make the social equity score a leading indicator, plan for several weeks ahead, prevent problems sooner
  • most social analyses are mass market analyses, we can’t throw away surveys just yet. surveys are needed for deep dives
  • models need to be updated more frequently – pinterest and vine are brand new and suddenly huge, social sites will come and go very quickly
  • google modeled trends in flu reporting, people search for flu symptoms online – model could anticipate trends in flu that CDC was reporting, model worked well for a couple years. model is no longer active perhaps because google changed how it does it’s search terms, changing their own data changed their own model
  • old tracking model was a project on it’s own, you discussed not changing the tracker so as not to change the trend.
  • new tracking model is an integrated program from a variety of sources to give a forward look into the market place
  • need to consider what consumers say (survey), think (social), and do (POS)
  • able to predict car registrations by using all forms of data together
  • difficult part – we see a trend that is decreasing and so take action to improve it… and then the trend increases. but was your prediction accurate? did you fix the problem or did the trend just increase as it was going to anyways?
  • why do you use tracking surveys if you make no changes to affect those ratings? why bother tracking numbers that never change [fabulous point]
  • we need to learn more about which and when spikes matter

Other Posts

I love social media research because: You can track results daily #3 #MRX


I recently wrote a blog post citing ten of the things I love about social media research. Today I address love #3.

Social media research lets you track data on a daily basis.

Tracking results on a daily basis isn’t really a big deal. Let’s face it. It’s easy as pie to to launch an online survey to a thousand people every single day, and then hope you get enough responses every single day, in order to complete your data analysis every single day. You see, while it is indeed POSSIBLE, it’s just not convenient and the cost can be ridiculously prohibitive. For these reasons, online tracking survey research is often conducted on a weekly or monthly basis.

When it comes to social media research however, as long as you are researching a brand (or person or category or topic) that generates sufficient data every day, you can very easily collect, clean, score, and code data every day. Most automated systems automatically gather and process data within 24 hours which means if you just launched a TV commercial or a marketing campaign, you can measure consumer response to it as it launches, as it fails, as it succeeds, and make appropriate tweaks along the way.

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