Tag Archives: social media
One of the best ways to identify lots of diverse people to speak at conferences is to follow lots of diverse people on social media. But do we?
With that question in mind, I turned to https://www.proporti.onl/, a website that says…
“Estimate the gender distribution of your followers and those you follow, based on their profile descriptions or first names. Many tech leaders follow mostly men, but I want to follow a diverse group of people. Twitter Analytics doesn’t tell me the gender distribution of those I follow, and it doesn’t try to identify gender-nonbinary people. So I built this tool for myself and put it on GitHub. It’s inaccurate and it undercounts nonbinary folk, but it’s better than making no effort at all. I want you to be able to do this, too. Estimate the distribution of those you follow and see if there’s room to improve!”
I’m cool with that so I turned to this tweet by Antonio Santos as a good place to start within the market research industry. I entered each one of these accounts (excluding @MRXblogs which is a bot that follows no one but me), in order to see how we’re doing.
— Antonio Santos 💙 (@akwyz) September 28, 2017
On average, about 36% of the people these market research influencers follow are women.
Sadly, only 3 people follow roughly equal numbers of men and women, and only 2 people follow more women than men (you can guess who!). I’m one of them, but that’s only because I actively follow women and I’ve been using proporti.onl to monitor my status. Unfortunately, for about 43% of us, one third or fewer of the people we follow are women. The curve is far from expected and could use a lot of improvement.
Fortunately, it’s easy to change that proportion. Lots of people have created lists of women on Twitter who specialize in different areas including marketing research, data science, analytics, STEM, and more. I keep a nice selection of those lists on my twitter account right here. However, here are some of my favourite lists.
- Women in Data Science: I love this list. Search through the 1200 members and you’ll find tons of women who specialize in data visualization, statistics, neuroscience, RStats, business intelligence, artificial intelligence, and more.
- Women Game Developers: 100 women who know AI, storytelling, games, user experience, digital marketing, customer relationship management.
- BioInfo Women: 600 women who know about EEGs, fMRIs, neuroscience, computer science.
- STEM women: 500 women who know data, engineering, cybersecurity.
- Women in VR: So, um, these 150 experts know VR.
Sharing is nice:
#MRIA2017 Opening Keynote: The Age of Disruption by Scott Stratten, Expert in Un-Marketing and NOOOOOO [Excellent!]
Live note-taking at the #MRIA2017 conference in Toronto. Any errors or bad jokes are my own.
Scott Stratten on twitter
- [100% hipster takes the stage including jeans, sloppy shirt, tattoos, beard, and man bun]
- He is known as the creator of the NOOOOOO button which gets millions of users and views with an average 27 second view. The site does pretty much nothing but say NOOOOOOO. It is the number one site on google for any version of the word ‘no’ that contains more than one o,
- Many people feel guilted, stupid, slow about being brought into the social media, digital world. Huge pressure to stay up to date with every channel but it’s impossible.
- You do NOT have to use every platform. If you don’t like it, don’t use it even if you want to feel cool and hip.
- When we say the word millenial, we mean people younger than us and we don’t like you. [yeah, i have to agree. We’ve built a wall there.]. This happens with every generation. Every newest generation is the worst generation.
- We’ve created a bias of ageism that is allowed. But it’s not a good thing. We use it in hiring. We assume young people don’t know. We assume older people aren’t tech savvy. Our industry depends on this. We see younger people as a threat.
- We hear things like millenials hate meetings and love to travel. Well, who doesn’t? This is just a bias of interpretation. We need to give comparative numbers. Millenials are more civial minded, cause minded, want to work for non-profits.
- The shift is not an age shift. EVERYONE is making communication changes so we need to figure out what customers want to do. Don’t say old people don’t text because they do, they just do it differently. Your customer should decide what channel they want to use. If someone emails you, then email them back instead of demanding a phone call.
- People like the written record of text, DM/PMs, emails.
- Know the speed of response expected by each method and respect those.
- Brands hop onto trends, often the surface of the trend. Put quotes on pictures, use influencers, newsjacking. But you must do it right. You CAN’T capitalize on death, terror, even if it’s ‘just a joke.’ Offer condolences, help not jokes. Consumers have the power to react, to choose where they open their wallet.
- Viral isn’t about a million views. It’s about 100 views with the exact right audience. Newsjack with originality.
- Ethics are not a renewable resource. What is the first thing that comes to mind when people think about your brand? Your horrid, distasteful ad?
- The problem with live video – most people are not filmable, don’t want to be on video, they’re modest or humble. Most people aren’t that interesting, particularly when it comes to streaming live.
- Contextual content – does the content match the sharing method – concerts, sports, backstage at awards ceremonies. Most other things do not. Interviews with your VP – NO! We want to do it to look hip because we can. But should we? Does it help your brand?
- Branding is no long real time. It’s NOW time. A response in 3 minutes vs 3 hours can make all the difference. What if an airline responded to your complaint 3 days later – you’d be even angrier. Authentic and transparent are important but speed is paramount. Great responses are disarming because most other responses are terrible.
- When people complain, they want validation and to be heard. They want the attention that they weren’t getting otherwise. At least recognize the issue immediately.
- Vanity metrics make you feel great and amount to nothing, Metrics must move the needle for your client.
- Don’t write books to sell them, write books to share knowledge.
- [Scott is a very entertaining speaker. Lots of fun stories. Look for his Unpodcast with Alison Kramer]
Like that? Read these!
- How can you create the future of retail?
- Should market researchers measure the conscious or unconscious mind?
- How to make your conference talk a sales pitch without making it a sales pitch
Sharing is nice:
Live note-taking at #IIeX in Atlanta. Any errors or bad jokes are my own.
Chaired by Seth Grimes
Automated text coding: humans and machines learning together by Stu Shulman
- It is a 2500 year old problem, Plato argued it would be frustrating and it still is.
- Coders are expensive, it’s difficult at scale, some models are easier to validation than others, don’t replace humans, no one right way to do it, validation of humans and machines is essential
- Want to efficiently code, annotate coding with shared memoirs, manage coding permissions, have unlimited collaborators, easily measure inter-rather reliability, adjudicate validity decisions
- Wanted to take the mouse out of the process, so items load efficiently for coding
- Computer science and HSF influence measure everything
- Measure how fast each annotator works, measure interacter reliability, reliability can change drastically by topic
- Adjudication – sometimes it’s clear when an error has been made, allows you to create a gold standard training set, and give feedback to coders; can identify which coders are weak at even the simplest task, there is human aptitude and not everyone has it, there is a distribution of competencies
- 25% of codes are wrong so you need to train machines to trust the people who do a better job at coding
- Pillars of text analytics – search, filtering, deduplication and clustering and works well with surveys as well, human coding or labelling or tagging which is where most of their work goes, machine learning – this gives a high quality training set
- If humans can’t do the labelling, then the machines can’t either
- Always good to keep humans in the loop
- Word sense dis ambiguities – relevant – is bridge a game or a road, it smoking a cigarette or being awesome
Automated classification interesting, at scale and depth by Ian McCarty
- Active data collection is specific and granular, as well as standardized; but it’s slow and difficult to scale, there is uncertainty, may be observer bias via social desirability, demand characteristics, Hawthorne effect [EVERY method has strength and weaknesses]
- Declared vs demonstrated interests – you can give 5 stars to a great movie and then watch Paul Blart Mall Cop 5 times a 6 months [Paul Blart is a great movie! Loved it 🙂 ]
- They replicate the experience of a specific URL to generate more specific data
- Closed network use case – examined search queries from members to recruit them into studies, segmentation was manual and company needed to automate and scale; lowered per person costs and increased accuracy, found more panelists in more specific clusters, normalized surveys if declared behaviors conflicted with demonstrated behaviors
- Open network use case: home improvement brand needed a modern shared meaning with customers, wanted to automate a manual process; distinguished brand follower end compared to competitive followers, identified where brand values and consumer values aligned, delivered map for future content creation and path to audience connection
Text analytics or social media insights by Michalis Michael
- Next gen research is here now, listening, asking questions, tracking behavior, insights experts
- Revenues don’t reflect expectations, yet.
- We’re not doing a great job of integrating insights yet, social media listening analytics is not completely integrated in our industry yet
- Homonyms are major noise, eliminating them needs humans and machines
- Machine learning is language agnostic, create a taxonomy with it, a dictionary of the product category using the words that people use in social media not marketing words
- It is possible to have 80% agreement with text analytics and the human [I believe this when the language is reasonably simple and known]
- Becks means beer and David beckham but you need training algorithms to do this, Beck Hanson is a singer, you need hundreds of clarifications to identify the exact Becks that is beer
- Beer is related to appearance and occasions, break down occasions into in home or out of home, then at a BBQ or club
- What do you say about a beer when they do a commercial that has nothing to do with the beer
- English has s a lot of sarcasm, more than a lot of other languages [yeah right, sure, I believe you]
- Break down sentiment into emotions – anger, desire, disgust, hate, joy, love, sadness – can benchmark brands in these categories as well
- Can benchmark NPS with social media
- Brand tracking questions can be matched to topics in a social media taxonomy, and there can be even more in the social media version than the survey version
Sharing is nice:
Ask first, listen later by Lori Reiser
- Traditional research often starts with the business, product iterations, product marketing, product fine tuning
- We should put people first so you hear about unmet needs and pain points
- Case studies
- Health insurance firm – young adults getting their first insurance plan, and new retirees moving away from employer benefits; had predefined assumptions but those were based on the six people in the room, fears of these people were financial and health and being bored not really insurance, how did they define good health and how could that be protected, did focus groups and bulletin boards, retirees weren’t worried about getting sick but rather that their parents would get sick, younger people were more worried about stress as their health issue
- Meat company – what did consumers need in terms of communication needs, saved qualitative for the end of the research, started with a survey of staff members and inspection organizations, realized they needed to formalize their email address to clients so that it didn’t come from Annie Pettit but rather from the company, realized that Mennonite members didn’t have email addresses [pay attention to that anyone who says they do probability sampling via RDD]
- Pharmacist – surveyed pharmacists as well as focus groups and the focus groups were after the fact, what does patient centered care mean, many barriers in terms of how pharmacists communicate with people given what doctors and other people want them to be able to say
- You can’t ask broad questions unless you go qualitative, open ends on a survey arne’t going to cut it; give your users permission to participat in the idea making, find the trendsetters and listen to them, use skilled moderation
Classified: Research that integrates to innovate by Mark Wood
- Technology has given us an identify crisis, people challenge are traditional beliefs, anyone can do DIY research, other people are jumping into our sandbox with new types of data
- Tactics to get our mojo back
- Understand consumer dynamics in a more connected world
- Leverage expertise in a data curation to improve SML capabilities, Think both/and between traditional survey and SM, Help companies navigate path to purchase, use data to help action
- Have to work with clients better and bring in other pieces of data so we can inform them better
- Bringing MR expertise into social media listening
- We need to take SM data to the full extent just not report facts and figures
- Must get access to this data first and then we can sample and structure and clean it
- SM provides an authentic voice of the consumer, has rich detail; surveys enhance social media structure to categorize themes and related back to real initiatives
- Use social and survey together to inform each other
- Need to identify lots of buzz with lots of impact, not just lots of buzz
- Use buzz impact matrix to understand which conversations have high buzz, high impact, and then dive deeper into those issues
- Identify all the touch points that need to be assessed, identify which have the highest reach, but there are big differences in claimed versus reality
- Need to move from measurement to action [the problem of the century!]
- Opportunities are greater than ever for MR to have an impact
Sharing is nice:
Live blogged at MRS in London. Any errors or bad jokes are my own.
The art of noise by Tom Ewing
- #IPAsocialworksinitiative – to guide effective use of social media data
- It’s all about measuring not counting, Ray Poynter wrote this part
- The insight guide is being written by Tom Ewing and he is interviewing people for it
- Five basic questions
- What questions do you need to answer, what decisions will betaken because of it
- What do you know already, existing research, behavioural
- What social behaviora are you looking for, what data sources, do you need aggregate data or granular level
- What is the relevant timescale, ad hoc or continuous, how do you know yyou’re finished
- Do you need quant metrics, what are they, it’s more than likes follows
- We have reached the peak of social tech
- Seth Grimes says the state of social insight is “confused,” it’s not really a mature technology, widely adopted, but not fully integrated
- It’s the early majority now, tactical, silos, unstructured text, the future is here but it is unevenly distributed
- We need to clear out the cliches, integrate with methodologies, integrate with insights and business and people
- Different poll results would arise if you use Twitter vs Facebook [i see this as a sampling problem, you don’t just take any church or school or company and say it is representative]
- UK Facebook data is closer to a census than a sample [but it is still very much a sample!]
- You can join insights with other methodologies [data fusion is ALWAYS wise, every method reveals different insights]
- Provide more file and predictive tracking systems and an ability to get granular and specific
- Who owns social insight? Is it marketing? Analytics? Insights? PR? Sales? It’s more about collaborating [why must one group OWN it, everyone can benefit hugely from it]
- Finding common ground – we’re all working toward the same goal, emotional understanding of people
- Limits of real time means it’s more tactical but there is indeed strategic value
- How do you get insight about a topic when you know nothing about it [oh goodness, I could go on about this. I was asked to draw insights about cars and trucks. I know NOTHING about them and couldn’t understand anything people were talking about. Expertise in the topic is a MUST! Put me on the food/outdoors/music research.]
- AI is already doing so much work that people would have done – collecting data, sorting and coding, analyzing, prepping, reporting
- Social insight begins with social practice, use new platforms like Whatsapp and snapchat, look beyond the brand level to the topic level, consider pictures and video, look for patterns at the network level
- Different things come out via text and images but you wno’t know unless you plan and look for differences in different types of content
- We are working towards general use, stratégie, cooperation, seamless integration among people, rich media as well as unstructured text
- Tom asks people to submit their social media case studies to him for publication in the book
Finding insight in 140 characters by Jake Steadman
- Jake [not from Statefarm] speaks
- Story about Star Wars, in 1977, need to tap into mainstream media, tap into core group of fans, went on TV and publications. FOr second movie he went to com icon. Fans went in the hundreds to see them in person for Q and A.
- Today, mobile is eating other media time.everyone has a mobile phone. December of last year, new Star Wars came out. Still need media and interest and fan base. THey launched on Twitter. That tweet drove all the other media. Drove coverage in print. Access to the stars still applies but they did it more directly and personal on Twitter. Stars did this on the red carpet, backstage, White House visits, all on Twitter. 1.2 billion tweets on opening weekend.
- All social data can drive insight
- There are culture issues and there are tools.
- Rise of the machines, machines allow you to become storytellers and consultants, it allows you to be a leader not a methodological policeman
- Let’s you do same day insights and be agility, we need to get over our obsession with precision, forget statistical precision [agreed, don’t create precision with decimal places, know when your data is directional and don’t make it more than it is ]
- Social is the democratization of data, client side is more like to be using social, agencies are more likely to be using big data, agencies need to be more for less and quicker
- Look for your soggy fries – customers were starting be less loyal with a restaurant, you could run a large research project, or you could look at social data immediately, found many uses of the word ‘soggy fries’, it didn’t need precision but rather it needed recognition
Panel with Jenny Burns, Christopher Wellbelove, Jess Owens, James Devon
- Some people jump at social media because it’s cheaper than things like focus groups [be cautious, cheaper means you need to spend more time thinking and analyzing it]
- Lots of people are analyzing social media without ever using it, you need to use the tools to really understand them [the speaker has now gone through three pairs of google glasses, and might be tweeting as we speak 🙂 ]
- Social media is still treated as a slightly dangerous cousin
- 67% of people using social media puts it really into late adoption, it is not new nor early adopter territory
- It says more about the market research industry being years behind to be so late in using this technology
- Privacy and ethics still matter, some companies get individual permission to review Twitter accounts
- People need to understand how their data is being used, whether it’s aggregated, Facebook data is fully anonymized and aggregated and great for ethics and privacy
[I kind of like have a couple presentations followed by a separate panel]
Sharing is nice:
Keynote: Why Social Media “Likes” Say More Than You Might Think by Dr. Jennifer Golbeck, Human-Computer Interaction Lab, University of Maryland #ISC2015 #MRX
- how much do two strangers online trust each other? you need to know about the people themselves first.
- social media data can give you accurate data, even when used in a simple way
- can predict introversion, political leaning, procrastination, personal health habits from your posts
- likes on facebook are always public, can use this to predict many things
- top four likes most indicative of high intelligence include science, thunderstorms, colbert report, and liking the page for curly fries. For low intelligence, the like was for the page “i love being a mom” But these don’t truly relate to intelligence.
- homopholy – we are friends with people who are like us, our traits are more common among friends than among random people.
- you just need one smart person to like a page and then it spreads like a virus to all your friends
- we’re really good at predicting sexual orientation, even in places where being gay could get you executed
- how does a store like Target figure out you’re pregnant, not from pregnancy tests, – an extra bottle of vitamins, a bigger purse, brightly coloured rugs together are a statistical connection
- how can you know what these statistical combinations are?
- can your social media network figure out which friend is your spouse? you know which friends are friends with each other and nothing else. Often it is the person with the most friends in common. But look at social dispersion instead, eg., your sports friends, your school friends, your family friends, your work friends. Dispersion is who is connected to the most of your groups – that is the spouse about 75% of the time. the other 25% is a good indicator that youre going to break up your relationship
- can predict postpartum depression from social media content.
- how easy is it to get data?
- takethislollipop.com – makes a video out of your social media accounts, try it if you think all your settings are private
- we leak a lot of data, we don’t realize how much data apps are pulling
- it’s not all bad – apps recommend products you might like, google uses it to make the web easier to interact with
- we don’t have control over it – don’t know who has data or what they’re doing with it
- even though the algorithms are smart, we still don’t buy whatever the algorithm says we might like. but they tell us things we would never think about but we actually want
- treat your business algorithms as useful but realize some are wrong or shouldn’t be acted on, it’s one more piece of advice
- [jen needs no slides, she changes every 5 or 10 minutes just for the heck of it, impressive]
- how do you figure out which people like the targeting or don’t like the targeting
- you don’t want to be the creepy facebook stalker guy, it needs to be handled with care
- Hillary or Rand: Trusting the crowd with your vote
- 69% of US adults say that Cost Of Living is an important issue
- 52% of US adults say that Crime is an important issue
- 58% of US adults say that Terrorism is an important issue
- Writing the Lyrics of Qualitative Transformation
Sharing is nice:
I came across this interesting infographic on the e-strategy trends site. It made me think about the types of selfies that I take. Most are at conferences with other attendees and I rarely have a product in hand. But, our clothes are visible, our smiles are visible, and someone’s logo is likely to be in the background whether it’s a vendor’s logo or the conference’s logo. Social listening researchers have been waiting for a tool like this for a long time and it looks like we’re finally inching forward just a little bit quicker now.
— Annie Pettit (@LoveStats) September 19, 2014
— Annie Pettit (@LoveStats) September 19, 2014
— Annie Pettit (@LoveStats) September 19, 2014
— Annie Pettit (@LoveStats) September 19, 2014
- What’s Gonna Kill You? An Infographic That Actually Works #MRX (lovestats.wordpress.com)
- In Honor of Infographics. #MRX (lovestats.wordpress.com)
- Frame this Gorgeous Mercedes Benz ad on your living room wall #MRX (lovestats.wordpress.com)