Tag Archives: NETGAIN

Consumer Consulting Boards by Tom de Ruyck #NetGain8 #MRX

Live blogging from MRIA’s #NetGain8 conference in Toronto. Any errors or stupid jokes are my own.


Tom de Ruyck, Head of Research Communities, Insites, Belgium

Consumer Consulting Boards (MROCs):
Integrating the Voice-of-the-Customer across the Entire Enterprise

  • consumers have the power to make or break a brand
  • brands don’t have full control over what is being said about your brand
  • fans want to have a say in the future of their brand
  • consumers expect better, faster, stronger every single day
  • being open and agile is what consumers need to be, but most companies are not here
  • most companies think they have two way dialogues with consumers but most don’t, they are faking it or not really doing it, they are afraid of starting a dialogue
  • it can take 18 months to bring a new “ketchup” into a shop – that is not agile! There is too much passing between departments. Need to stop ad hoc projects and we need to work on teams not departments.
  • customers are the best consultants a company can hire. obviously customers know more than someone whose only been the brand manager for a year or two. The brand manager knows the marketing, consumers know the product.
  • your brand fans might be the toughest audience ever, they become angry when the brand team do something wrong
  • consumers consulting boards are closed long term communities – you can have clashes of ideas which can spark great ideas
  • it’s one piece of the puzzle to be a more open company, a community doesn’t necessarily mean you are more agile though
  • to succeed, you need the right people on board – find people who are TRULY fans of the brand who are interested and interesting, you don’t need a “rep” brand. instead of the 2 out of 8 people in your focus group who are interesting, why not just do the research with those 2 – plus more of those 2.
  • to succeed, you need the right number of people – 50 intense participants is enough, 150 or more is when they start to interact less and it’s more difficult for the moderator to probe
  • to succeed, you need engagement – tell them about the research and the incentive which is feedback and maybe a small gift, maybe a basket of products they have worked on. they like to show it off to their family and friends.
  • “i was part of a global team that redesigned the Heinz ketchup bottle” – that kind of incentive works, it doesn’t always have to be dollars
  • people tell you more if one of the moderators is joe-participant, they may not ask the research questions but they know what buttons to press
  • why would you NOT have a 16 year old girl help you analyze data from 16 year old girls [darn right!]
  • [communities sound like gamification of surveys if you do them right]
  • give people a different hat to release them from social or cultural issues – “You are now the boss”  “You must tell us all the bad things you see”

Trends for Mobility Research by Mark Michelson #NetGain8 #MRX

Live blogging from MRIA’s #NetGain8 conference in Toronto. Any errors or stupid jokes are my own.


Best Practices and Trends for Mobility Research, Mark Michelson, Executive Director and Co-Founder, Mobile Marketing Research Association, Atlanta

  •  Everyone wears many hats, including mark who also plays guitar in a band
  • mobile brings all these different lives together, converges together new companies and new opportunities, as well as people who can’t get on a plane and speak live at a conference due to being snowed in
  • smartphones are only 5 years old and they have improved speed of delivery – always on, always close, real time delivery, people participate at their convenience, passive data collection via GPS or device use, longitudinal work all around the world with instant translations, pictures of products and vehicles come instantly, talk to someone on the phone while they’re shopping, dissipated workforce, QR and scanner codes to engage
  • smartphone make participation more enjoyable, more social sharing to explore each other’s lives, self-reported ethnography, live video chats anywhere, augmented reality, gamification
  • [quite neat to have a virtual presentater about mobile methods where the researcher is virtually there]
  • quant mobile mr data – survey is Q&A, passive monitoring without interaction, field conditions like merchandising brand audits or mystery shopping, sensory such as how people perceive via galvanic skin response, temperature, etc
  • qual mobile mr data – self ethnography with video photos, what people say on bulletin boards or discussion groups, emotional feelings in diarys, projective research, video chats or audio recordings in the moment
  • challenges – designs to fit the screen, user experience and training, keeping people engaged, validity of self-report, optimization for different devices, client trust in findings, participant trust
  • ethics in mobile mr – privacy is critical for legal reasons and continued open participation
  • commitments to responders – voluntary, protect privacy
  • challenges – normative data include more positive top box scores, questionnaire length, how to calibrate tracking data, how to incorporate into existing methods
  • best practices – transparency, respect, share the intent, expectations in advance, avoid burnout, keep it short, respect time zones, be screen and device appropriate, consider right question type for device
  • best practices – micro surveys, in-context for what people are doing right now, mobile friendly tools like dials sliders, graphic interface
  • share the guidelines with your staff, clients, and participants,
  • Learn more on the association site mmra-global.org

Transformation of Market Research by Jeffrey Henning #NetGain8 #MRX

Live blogging from MRIA’s #NetGain8 conference in Toronto. Any errors or stupid jokes are my own.

Netgain8The Transformation of Market Research: Where to next? by Jeffrey Henning, President, Researchscape International, Boston

  • Innovation in 1994 – a bike lane on Davenport
  • 1923 – it was a railway
  • 1904 – it was horses and buggies, beginning of electrification
  • Previously, the road was a first nations pathway and that is why it doesn’t follow traditional street ways – it used to be the shoreline!  [Cool!  I live here and didn’t know that!]
  • profession of building canoes probably lasted thousands of years, building buggies was probably hundreds of years before they went obsolete
  • So will market research become obsolete? [and here, Jeffrey yells it out 🙂 ]
  • Only methods become obsolete, not professions.
  • Currency will always exist but it will look and work differently
  • Market research is eternal, or nearly
  • Pressure points on survey research – victim of its own success – overloading inboxes, bad questionnaire design, long surveys, complex designs
  • Drivers of survey research – mobile surveys. micro surveys, DIY, Enterprise Feedback Management, Voice Of Consumer systems, emotion capture, feature wars, automation, very crowded space
  • In 1987 – questionnaire design was in wordperfect, surveys were by phone mail or face to face, results analyzed in spss and lotus, results presented in wordperfect — the human provided most of the value
  • In 1997 – finally a product to help with survey design (surveysolutions or EZSurvey) and fielding and analysis, results were now presented in powerpoint – more automation now but still a lot of human work
  • In 2013 – survey templates and question libraries, automated fielding, partially automated analysis, partially automated presentations
  • More companies are doing proprietary analytics with traditional questions and choosing pictures to represent feelings
  • Weighting used to be cone using statistical systems under direction of an analyst, open ends were rarely analyzed, and crosstabs showed everything not just the meaningful differences
  • Google applies automatic weighting to its analysis though it’s not perfect, they guess who you are based on your browser data
  • WYWIWYG – what you want is what you get – the system knows exactly which statistic should be applied – you just say what variables you want to test, you don’t need to understand how to use SPSS or SAS or R – StatWing
  • Automation to come – crowd shaped surveys, better weighting, text analytics, proprietary analytics
  • 2018 – human designs the questionnaire and not much else of the process – self driving car… self driving survey research not so far fetched – maybe we should become qualitative researchers 🙂
  • We need to develop proprietary indices, pioneer new techniques, invent unique interfaces, build proprietary panels, custom programming, more qual, benchmark databases, text analytics, be creative, good infographics, internalize research
  • “The best way to predict the future is to invent it” – Theodore Hook

Surveys in a Snap: Paul McDonald #Netgain7 #MRX

Netgain 7 MRIA
… Live blogging from downtown Toronto…

Surveys in a Snap!

Paul McDonald, Product Manager, Google Consumer Surveys

Practical applications of Big Data – How Google uses data
to make better products

  • They work with anonymized data, don’t want to learn everything about individual users
  • Passively collect data to create predictive models with probabilistic outcomes
  • 30 trillion web pages in their index, 1.2 trillion search queries per year, 400 million transactions per year through google wallet
  • information becomes insight when it’s placed in context
  • Start with a business purpose
  • Bayesian statistics work with initial probabilities and then adjust the probabilities based on new data
  • They use Bayesian big data statistics to predict demographics on Google surveys
  • Center of Disease Control data for the flu matches Google searches perfectly – can predict how intense the flu season is; indeed CDC uses this data to predict flu trends
  • 15% of search queries every day have never been seen before – they use distance between words, query history, frequency of words and phrases
  • Use bayesian stats to automatically code open ends in survey forms, e.g., finish off words or phrases

Social Commerce Engines: Scott Evans #Netgain7 #MRX

Netgain 7 MRIA
… Live blogging from downtown Toronto…

Data Integration:

Scott Evans, Senior Product Strategist, Bazaarvoice, USA

Why Social Commerce Engines will Power Next-Gen Market Research

  • They are a white label firm
  • ZMOT – zero moment of truth
  • You used to go to the shelf and pick something. Now you access information everywhere before going to get the exact one you’ve decided on
  • Omnichannel – People look at things in the store then buy online, People will go to the store and read online reviews on their phone, People read reviews on their mobile while they’re driving
  • Social commerce engines are all about conversion – gives people the option to communicate about their products
  • What is the ROI of traditional market research? It’s hard to answer that question. But with social commerce, you can calculate ROI.
  • Check out the opentable website to find a good restaurant. It’s becoming easier to use these kinds of things without being  a serious techie
  • Many product page websites are actually BazaarVoice – both display and collection of data. review, comments, Q&A, stories; Likes go to your facebook page, your reviews go to your social networks
  • Traditionally it was hard to get SKU level information [exactly what size of peanut butter did you buy?]
  • Great opportunity to leverage our insight skills
  • Social media monitoring is now a commodity. Canada leads in this area.  Can’t measure SKU level, can’t measure non-public CGD, can’t measure behavioural data
  • We need to decide what to zero in on. How do we map customer journeys? How to evaluate consumer segments?
  • 3M dish wand: hundreds of people write reviews about this stuff, people are passionate about different things 🙂  ; First, ask for reviews, then identify issues, form a team, respond publically, fix the problem.  They were able to respond to each person who wrote a review and give them a fixed product.
  • 3M Scissors:  Created the best scissors on the market. Noticed a lot of women noticed they were good for scrapbooking so they marketed them for scrapbookers. Ditto left handed users.
  • Cabela’s Pant Design: Noticed “hunters” loved the new pants. The older men didn’t like the pants. It came down to the young folks liking the pockets of the new pants.
  • Nexxus Product Testing:  Recruited advocates to test new products.
  • In one month, BV had 350 million visitors with 16 billion impressions. 70 million products covered, and 65 million contactable reviewers.
  • Good way to talk to people who actually want to talk about the product
  • Instead of sampling, you have the census of reviewers available to you [that’s not really census though]
  • Next gen mr – broaden reach, richer more natural product experience, x-market comparison, SKU level analysis, detailed behavioural patterns, generalization( weighting), mr assets (segmentations), consumer modeling

Data Mining and Predictive Modeling: Andrew Grenville, Kevin Dang #Netgain7 #MRX

Netgain 7 MRIA
… Live blogging from downtown Toronto…

Predictive Modeling

Andrew Grenville, Chief Research Officer and Kevin Dang, Senior Research Manager, Vision Critical

Data Mining and Predictive Modeling to Drive Panel Management Strategies

  • Analytical panel management has key factors
    • data set-up
    • what business challenge are we trying to solve – e.g., valuable panelists, churn
    • methodology and analysis – which model, which variables, how to fine-tune, which statistic works better
    • evaluation and performance – is the model stable, can you move it through time
    • application – if we know who will churn, how can we act in time
  • static reports, drill down, ad hoc reports, forecasting, predictive modeling, optimization – researchers tend to stop before predictive modeling
  • Want fast turn around time, waiting a couple weeks for analysis isn’t going to work
  • Needs to incorporate longitudinal data, learn from the past
  • Needs to be flexible to incorporate both survey data and panel data
  • Needs to have as little ongoing IT support as possible
  • Identified all the interaction points along the panelist life cycle – disqualifies, over quota, incentives paid, other touch points
  • First cleaned out poor data, e.g., 115 year old people
  • Churn model – urban area or high income were more likely to churn, 18 to 30 more likely to churn, low response rates more likely to churn
  • Conducted exploratory and confirmatory analysis – predictive accuracy was 76% [awesome!]
  • Challenges with model include the quality of the data, lots of time was spent harmonizing the data for modeling
  • Challenge was making model useful and actionable – it had to be as simple as possible so that it was actionable

Mileage from your Dashboard: Adam Froman #Netgain7 #MRX

Netgain 7 MRIA
… Live blogging from downtown Toronto…

Dashboarding with Large Data

Adam Froman, CEO Delvinia and Asking Canadians (Share your Ikea monkey photo!)

Getting Mileage out of Your Dashboard

  • How do we listen to consumers when they have the choice of how to communication?
  • Experienced researchers need to lead us to avoid the crash that the young researchers would create
  • There is a huge opportunity for researchers if we just stand up and accept the job
  • People used to wonder why you would want to know the effect of a commercial immediately and not next quarter
  • Always look at things through the eyes of the consumer
  • Seek out data driven insights, use visual storytelling
  • User of a dashboard is an executive, the CEO – the dashboard needs to help them make better decisions
  • When clients can’t commit, see if your local university can you help you out
  • Wanted answers available within three clicks, narrow down to result in the data to the person who can deal with the issue
  • How to ensure a successful digital dashboard
    • Clearly define your user audience: data person, researcher, CEO
    • Gather and analyze existing data and reporting tools
    • Ensure user interface is a collaborative process
    • Don’t let technology lead, try before you buy
    • Realize that it’s just the beginning, same data different algorithms
  • Adopt an R&D mentality to stay ahead of the game

From Social to Business: Chris Gruber #Netgain7 #MRX

Netgain 7 MRIA
… Live blogging from downtown Toronto…

Business Social Analytics

Chris Gruber, IBM/Cognos Consumer Insights Solution Architect

From Social Insights to Business Advantage

  • Art plus science and then it’s iterative
  • Buying pattern used to be consecutive, one at a time, one after the otherNew model focuses more on generating loyalty, creating enjoyment, advocacy, and bonding
  • Buy is not the end
  • 90% of consumers trust peer recommendations
  • Social media improves traditional predictive analytics, adds to attitudinal data
  • Creating a running word cloud helps you see top words and phrases over time; use these to write a better survey and learn about what you don’t know
  • Influencer charts help you identify the websites that are generating the most traffic or emotion
  • Segmentation tells you that young folks will respond better to text messaging whereas moms would prefer emails

Socializing Research: Mike Rodenburgh #Netgain7 #MRX

Netgain 7 MRIA
… Live blogging from downtown Toronto…

Mike Rodenburgh, Vice President, Ipsos, Vancouver

Socializing Research: It’s the end of marketing research as we know it, but we feel just fine!

  • Old methods aren’t engaging in today’s world, your eyes are crossed at minute 25
  • Over emphasis on survey data, respondents are lab rats, incentives are not motivating or relevant, surveys ask wrong questions, focus on science ignoring immersive online experience
  • “socialized research” engaging with consumers in ways that mimic the way they engage with their friends in social networks and other types of data
  • It’s not sample and surveys and reporting. It’s people and experience and stories.
  • It means blending methodologies, active surveys, passive data, and interactivity
  • Facebook can be a real representation of people’s lives – family, brand likes, celebrity likes, photos
  • Apps already use Facebook’s social graph data to inform product decisions – why can’t we?
  • “Login to the survey with Facebook” generated 274 people out of 600 agreeing
  • Is the data reliable? Are those who share different from people who don’t share?
  • Demographics of agreers and refusers was nearly identical, no differences in tech ownership, stated demographics match, but stated likes don’t always match actual likes – only 34% of people correctly recalled their facebook likes
  • Average respondent had 149 likes but 8 people had more than 1000 likes
  • People’s likes overindexed on intuitively correct things – African Americans liked african american icons, etc
  • There is a positive correlation between TV show reach and social engagement, the successes were where the host had a dynamic personality as opposed to generic game shows
  • Facebook data is JSON strings which means it’s not compatible with many standard data tabulation systems
  • How do you visualize 50 million data points?
  • Learn things you didn’t think about asking on a survey, eliminate demo questions from surveys, add contextual phots and friend info to surveys
  • The best way to predict the future is to create it

Small vs Big Data: Fabien Rolland, Michel Girard #Netgain7 #MRX

Netgain 7 MRIA
… Live blogging from downtown Toronto…

Fabien Rolland, Director of Marketing Research, Aeroplan
Michel Girard, Director, Analytics at Aeroplan

Small vs. Big Data: Which one rules the boardroom?

  • Aeroplan has 5 million members, issue 1 reward every 14 seconds
  • Big data is volume, velocity, variety – petabytes, real time, multiple sources of structured and unstructured data
  • Customer data includes attitudes, touchpoints, demographics, transactions, lifestyle, share of wallet, neighbourhood data, model scores
  • Need to create self-serve access to the data for routine questions, to reduce pressure on analytics team
  • Need to centralize all the member touchpoints – online contact center, mobile, transactional
  • Don’t be reactive order takers, free up time to focus on business questions

  • Little data is getting better, faster, cheaper, better
  • Most of change is happening on the technology side, text analysis, reporting, event triggered, location based, web intercepts, mobile qr
  • Little data can answer most questions about most parts of a problem
  • Big data can be precise in measuring specific aspects but it often doesn’t understand broad problems
  • For aeroplan, they only know about transactions that use the card number, don’t measure competitive activity, can only measure webdata when members opt in
  • Sometimes big data is too big, sometimes you just need a simple tool like a survey
  • Big data can’t tell you what motivates a specific segment or how to improve your products
  • Little and big complement each other and are not substitutes
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