2016 Net Gain – Facial Biometric Ad Testing

My notes on the Net Gain presentation by Bernie Malinoff and Norman Chang on Facial Biometric in Advertising Research (without asking a single question). There has been limited editing and hence there will be typos.

Over 90% of human behaviour is driven by emotions.

Emotions in consumer research.

Real Eyes Facial Coding: using webcam can code emotions based on your face – can calibrate to within 1%.

Works across cultures, if subject has a beard of not, or even if they wear glasses.

People never express anger in advertising, most often they express confusion.

Provides a simple 1-10 score, reviewed by demographics, player types etc.

Passively collected insights of a respondent during a 15-30 second ad without asking them a question.

People who feel more, do more

  • assess earned media potential
  • direct media allocation/placement

Lottery is all about emotions – functionally they sell a piece of paper, but in reality OLG is selling hopes and dreams.

They do a lot of advertising over the course of the year.

Have used the methodology to test about 12 different ads.

Can look to see both overall how an ad works with the target audience, but can also look at a moment-by-moment scoring of the ad.

Also

  • Facial biometrics has also led to creative alignment with busienss objectives
  • recent ads have stronger emotional resonance to grow player participation
  • prior advertising was well recalled, but worked best with current players

Ad agencies tend to like this method of research, because it is pure emotion, which is hard for them to argue with.

Can setup the research on a Friday, and have a de-brief on Monday afternoon.

Usually about 300 people in a study like this, is one on the internet.

Some use 30-40 for a pre-test, but they use 300 to be able to segment.

 

 

 

 

2016 Net Gain – Social Insight in Action

The following are notes from Margot Acton and Vanessa Killeen’s Net Gain 2016 presentation: “Social Insight in Action: The Power of Combining Social Media Analysis and Traditional Survey Research”. This post has had limited editing and hence there will be typos.

A lot of noise, and challenges for brands to cut through the clutter. P&G Chief Brand Officer pulled back on advertising because: “as the world was getting louder and more complex, we were simply adding to that noise”.

The bar of acceptability for brands is higher than it was 20 years ago, and it’s getting higher every day.  Harder for brands to break through – best in class advertising is going up.

Not every touchpoint matters? Consumers filter and manage their relationships with brands in new and powerful ways

20% of touchpoints can deliver – and this varies from one brand to another

Social media has allowed consumers to move from passive relationsihps with brands to co-creators or destroyers.

Q: If a research project is delivered and no one remembered it, did it happen?

Often brand tracking falls into this.

  • Tracking – often long surveys, with many people who have put in questions.
  • Declining response rates on panels.
  • Flat-line metrics – what do you do if nothing is changing? What is the insight?
  • Problem – data can take weeks or months to be received

Social media

  • full range of opinion
  • real-time
  • low cost?

But

  • Data is a bit spikey, volume of tweets can go up and down day-to-day
  • Too much noise – one study looked at 100k pieces of content for a brand, found 380 were relevant
  • Automated sentiment coding is poor
  • It’s not representative – though the real issue is not representativeness it’s predictability
  • Challenge – can we model survey KPIs for social and search data sources?

TNS study – reproduced survey based KPIs for 80 brands in six categories. Building models that can predict brand equity four weeks in advance.

Can predict sales accurately for categories such as cars and toilet paper.

Learning:

A lot of the traditional metrics are focusing on the wrong things and are not predictive.

Critical assets and what brand is generating is not being gathered by traditional KPIs.

Result: Turning off some metrics while leveraging predictive power of social.

Obtain much more dynamic insights from social media, as things change more often.

2016 Net Gain – When Behavioral Science Turns the Classical Marketing Model on its Head

The following are notes from Alex Hunt’s Net Gain 2016 presentation: “When Behavioral Science Turns the Classical Marketing Model on its Head”. This post has had limited editing and hence there will be typos.

Problem with today’s marketing – we are advertising by hitting people over the head with product benefits.

However, we think much more than we like to admit – more like Homer Simpson than we would like to acknowledge.

Alex provided a quote (can’t recall from who) saying that humans think in the same way cats swim – can but would rather not to.

Reference to System 1 thinking (Kahneman reference) that Hunt says that 95% of our decisions and actions are based on System 1 thinking, and occasionally System 2 comes into play.

 

Fame – it a brand comes readily to mind, it must be a good choice – current brand share. Doesn’t really speak to product benefits though.

Kahnman “Nothing in life is as important as when you are thinking about it.”

Example – Hunt has moved to suburbs so his amount of driving has increased, which means they are more likely to get in a car accident. However, they are not concerned about it – instead spend $50 per month on home security, even chances of a traffic accident air much higher.

Why do they make this decision? A: Fear of home invasion is top of mind.

Example: A mobile company in the UK that was in fifth place (called 3), gave a brief to their ad company to “make the brand famous” – had an add with a dancing pony, which went viral. Helped improve brand.

 

Feeling – If i feel good about a brand, it must be a good choice -predicts fruture brand share

Example – did a presentation in front of a room of accountants, and asked how many had done a cost-benefit analysis before buying their last car. Only one had – clear example that system 2 hadn’t been used in a very important choice.

Commercial example:

Most effective ad out of 500 in 2014 in how it made people feel. One year after campaign launch sales had doubled in France. Put emotion front and centre, not product-focused at all, and by making people laugh had the most successful ad. Take-away: better to ask what people think about a brand than ask product detail questions.

Fluency – if I recognize a brand quickly, it must be a good choice – gives you the toolkit to build brand share.

Example – British Airways in 80s stood for trust, and used the Union Jack as their symbol – a shortcut for trust. They ended up removing it – Richard Branson bought the rights to do this and it worked well for Virgin. The power of distinctive assets.

Fluency examples – P&G logo, Gatorade’s stylised G.

Commercial example:

While it is important to test attributes and look at logos at the very end of the questionnaire, important to put the focus on distinctive assets like logos.

Bringing in behavioural science means we should be able to simplify marketing.

 

 

 

2016 Net Gain – The New Age of Advertising Research Measurement

The following are notes from Melanie Drouin’s Net Gain 2016 presentation: “The New Age of Advertising Research Measurement”. This post has had limited editing and hence there will be typos.

Advertising measurement changing dramatically. People having a hard time figuring out where to spend their money.

  • Digital media keeps growing & changing – over the past 3 years total digital media time spent has grown 53%.
  • There’s a lot of data out there – In August P&G announced it will move away from ads on FB that target specific consumers.
  • Feeling that maybe digital is providing diminishing returns

Methodology: panel-based advertising measurement and optimization.

Adding new tools and models to our existing methods:

Track ad exposure:

  • Print and tv still big channels – large trust in these channels
  • Out of home – testing geo-targeting
  • Cookies for mobile and desktop
  • In-app via advertising ID protocols

Survey exposed and unexposed audiences – aren’t talking about the ads though – looking at impact on brand equity and things like that.

Analyze campaign effectiveness – can determine which media buy alone or combined had the best lift.

Tracking ad exposure is now about people and their devices.

Media combinations – the model can tell you which combination of types of media used help to improve various types of elements regarding brand (awareness, brand equity etc.)

 

2016 Net Gain – Reincarnation: The Death and Rebirth of Marketing

The following are notes from John McGarr’s Net Gain 2016 presentation: “Reincarnation: The Death and Rebirth of Marketing”. This post has had limited editing and hence there will be typos.

The Death of Marketing?

John Hoffmire: “Marketing is increasinlgy seen as a tactical, rather than strategic function in organizations.”

But – what about the Dove Real Beauty Campaign? Others point to advertising like A&F which has been heavily parodied.

Argument that digital marketing is unsustainable, people don’t want to see advertising on digital platforms – rise of Ad Blockers for example.

Problems around content marketing as well, as are not usually high in popularity.

Other example – Sony launching a waterproof phone, showing a people taking photos underwater – with a disclaimer saying not to take photos underwater.

What is causing the death?

  1. Preoccupation with media platforms and digital tools
  2. Bypassing the meaning in the customers lives
  3. Misuse of market research as only a tool to find how to sell to them

How do we market to them?

  1. People are drawn to what reflects their values
  2. People behave in irrational ways, albeit predictably

Lulemon took advantage of two things: people want to be healthy, and belief in religion is at an all time low, but people want to be spiritual – so picking yoga was a smart move.

Collapse of American dream – after housing crash – two movements emerge:

  1. Occupy Wall Street – Blames the banks
  2. Tea Party – Blames the goverment
  3. A collapse of optimism in UK – Only way to protest was to vote for Brexit
  4. A Collapse of optimism in US – US election is quite timely

Scion Sales USA

Irony for Toyota created Scion because Millennials didn’t want to buy Toyotas. Then after collapse decided they wanted to buy Toyotas but not Scions.

Results of Family study

  • Famlies expect the unexpected – use words like lean and adaptable, always being tested. Losing optimism. Now they only look to themselves. — Brand response — Uber, Car sharing — low commitment flexible options
  • Families adopt “frontier logic” – be tough, be prepared — live and let live unless encroached on — result – TV Survival series like “Bear Grylls”
  • Achievement over Authenticity – setting goals and working on individual improvement  – track progress and achieve – it’s never too early to get started — triathlon for kids
  • Who’s the adult here anyway? – children emulate adult tastes, parents’ nostalgia creates continuity for kids, parents don’t want to be uptight authoritarians – result children’s movies films have things to appeal to adults as well
  • Technology as a force for good – nest cam, cellphone for toddlers

Rebirth of Marketing – It is every marketer’s responsibility to be a force for good in their consumers’ lives. Which requires real commitment.

If you get it right you can be like Lululemon, get it wrong you can get it wrong like Scion.

 

 

2016 Netgain – Emotion Analytics Can Predict What People Do and Explain Why They Do It

The following are my notes from the 2016 Net Gain presentation of “Emotion Analytics Can Predict What People Do and Explain Why They Do It”, by Lana Novikova of Heartbeat Ai Technologies Inc.. This has been posted shortly after the end of the presentation, so there has been little editing and hence there will be typos.

Had an idea 15 years to create a product to analyse open ends, while she was at another job when internet data collection was just starting. In March 2016 she had built a prototype of her product. Product would analyse phrases and anaylse the meaning into different emotions.

First project was based on how women would feel toward chocolate – feelings of anger, joy and sadness.

Last election – bubbles of joy toward feelings for Justin Trudean, “the opposite” toward Harper.

Emotion Analytics can predict what people do and why they do it.

Using this technology the company predict the Australian election very accurately, which polls said was quite close.

Daniel Kahneman: “Nobody would say, ‘I’m voting for this guy because he’s got the stronger chin,’ but that, in fact is partly what happens.”

Q: Where do we find the answer in the sea of data?

How do you get information from the human brain?

Language is the oldest human tech – according to Chomsky emerged between 60k and 200k years ago.

Customer service reps can tell from your tone in voice – within 30 seconds – how you are feeling and what type of intervention will be needed.

Use the following emotions: void, fear, anger, joy, trust, love, surprise, sickness, body sense

The software has 8,000 words and is coded to one of the emotions that they are using in the software.

For example: In a survey can ask: “How do you feel about ____ (Candidate)” — and then use the algorithim to segment each response into emotions.

Australian election polling

  • Used Google Consume Surveys
  • N=943
  • May-July 2016
  • 5 questions
  • Results of election Turnbull 50.36%, Shorten 49.64% – margin of 0.72%
  • Rational model component — Voter Preference + Voter expectations of who woudl be PM
  • Example: Q: How do you feel about Shorten/Turnbell becoming the next PM? Responses of emotional breakdown were pretty much the same between each candidate.

Examples – banking study found people do feel emotional toward their bank, but customers do not toward telecom companies

 

2016 Net Gain – The Internet of Things

The following are my notes from the 2016 Net Gain presentation of “The Internet of Things”, by Greg Dashwood of Microsoft. This has been posted shortly after the end of the presentation, so there has been little editing and hence there will be typos.

IoT isn’t futuristic, it is here and being used by companies now.

Klaus Schwab “The Fourth Industrial Revolution”

  • First – 1760-1840: driven by steam engine – impacted transportation
  • Second – 1870-1940: electricity and combustion engine – impacted manufacturing
  • Third – 1960-2010: driven by the microprocessor
  • Fourth – Physical (robotics, 3D printing autonomous vehicles), Biological (genetic diagnostics, genetic treatment), digital (machine learning, IoT, disruptive business models)

What is driving prevalence of IoT?

  • every 2 days we create as much information as we did from the beginning of time until 2003
  • 90% of all the data ever created in past 2 years

– by 2020 expected all digital info will be from 3.2 zettabyte to 40 zettabytes

-86% of CEOs consider digital their #1 priority, they believe technology will transform their business

-Blockbuster had 9,000 stores at its height, Netflix tried to sell to them for $50 million. BB turned them down and Netflix disrupted industry entirely

  • likewise Uber upsetting the Taxi industry
  • but, technologies like these need analytics

Systems of intelligence

  • engage your customers
  • transform your products
  • empower your employees
  • optimize your operations

 

What is IoT?

Defining

  • Things
  • Connectivity – can connect every asset in factories around the world
  • Data – can generate massive amounts of data with this information
  • Analytics – Can turn this data into insight
  • Action – How you take the analytics and profit from it

IoT is key to digital transformation

  • 73% of companies surveyed currently active in IoT
  • 60% of those working on IoT are aiming to grow revenue and profits

Examples:

  • Johnson Controls have their chillers running 9X faster
  • Rolls Royce are putting sensors in get engines to save fuel usage
  • ThyssenKrupp – use sensors to monitor health of elevators and schedule maintenance – reduce downtime by 50%

What is enabling IoT today

Billions of connected sensors

Big data

Machine learning/artificial intelligence

Number of connected devices will explode in the next few years:

fitbits, clothing, glasses, body armour, keys

“Things” Found in the Enterprise

  • Devices that empower and enable your people  –
  • Others: vehicles, robotics, assembly line equipments, buildigns, infrastructure – can add chips and sensors to protect and monitor assets

 

2016 MRA CRC – Are You Asking The Right Research Question

The following are my notes from the MRA CRC 2016 Presentation “Are You Asking the Right Research Question?” presented by Doug Field (MSG Networks) and Susan Kresnicka (Troika)

Q: What does it mean to be a fan?

2015 Network Anthem

  • Very little game footage
  • Copy is emotional
  • Avoided player callouts
  • Brief network mention
  • Character voice

MSG – 11 million subscribers & over 325 live games (New Yor Rangers, NY Knicks, NY Islanders, NJ Devils, NY Liberty and New York Red Bull)

Weekly ritual is going over weekly Nielsen data – but it is only conjuncture, you don’t know why the ratings turn out why they did for sure.

Also assumed:

  • fans “watched like us” – that fans that watched all season long
  • gender bias

Consquences: 1) shouted at viewers 2) were transactional 3) reported instead of connecting 4) Formal and authoritative

Decided they need to change graphics – premiered: first game with new format was last night – during game ticker, music etc.

They needed a new question – needed to know “Why they watch” not when.

Methodology of study on fandom:

  1. Literature review
  2. Netnography
  3. Digital ethnography
  4. Quant survey
  5. Focus groups
  6. Personal narrative analysis
  7. Analysis & reporting

Wanted to study

  • how people watch
  • why people watch
  • (missed the third)

Video data collection – people sent snippets (30 second selfie videos) – a total of 1,309 videos

Data overview (rangers fans) – 756 snippets – 26 games in total, 18 games were wins – 38 avids, 23 moderates and 14 casuals

What they learned

  • Being a fan is like being in a relationship
  • You text each other daily
  • You try to sit down for dinner with each other when you can
  • When it’s rough you complain to your co-workers
  • Sometimes you go on a romantic date (going to the game)
  • Sometimes it feels really great (winning a championship)
  • Makes you feel like you’re bigger than something else
  • Sometimes you fall so deeply that it feels like what happens to your team happens to you

Like all successful relationships – it requires commitment and maintenance.

Origins of Fandom

It’s often one of our first loves – often became a fan during childhood

  1. Social connection – passed down by a parent or other family member
  2. Attachment to place – if you are of this place you need to be a fan of this team
  3. Living through a period of team success

What makes a casual, moderate or avid fan?

  • MSG defined it by number of games watched, but, during research found that what they said about the team sounded familiar across types
  • self-perception and viewing behavior don’t always align
  • not always a linear progression through  incresing levels of fandom

What keeps us together?

  • Televised game viewing is among the most important and frequent practice.
  • Televised sports serve as treasure “me” time

People watch in the course of everyday life – can be surrounded by distractions.

Viewers dip in and out of game, because of these other activities.

Implications

  • Fans are in a relationship with their teams and you are in a relationships with those fans
  • You benefit when your relationship with fans is long-term, withstanding vagaries in team performance and life’s competing priorities
  • Make it as easy impossible to meet fans needs and expectations
  • Make it sure they know you appreciate their fandom
  • Acknowledge their feelings – might be sad after a loss – and calibrate your response
  • In other words – meet fans where they are

Presenting the findings a tough sell

  • Used to dealing with numbers – this had some ambiguity
  • Anything that challenged processes made it difficult for people to understand
  • Question was: how do you break the cycle?

Step 1 – The fan first philosophy – a new brand position – “MSG Networks. We satisfy our fans desire to emotionally connect.”

Change in Attitude:

  1. Moved from Authority to Empathy
  2. From Transactional to Relational

Triangle of Fandom – pillars:

  • Identity – who I am
  • Escape – entertainment – me time
  • Tribalism – connection/ united what i watch

Projects:

Game package redesign

Wanted to strengthen the connection between the fans and their players. Immersive fan experience focusing on the team colors. Linked their advertising campaign to bright uniform designs.

Marketing campaigns

Used the “United we watch” tag to bring across the message of fans being part of something larger.

Final Thoughts:

  1. Challenging assumptions – not always comfortable but critical – ask why? and dig deeper
  2. Empathy – presenting findings to staff properly is important
  3. Leading by example – don’t narrow range of options, paint a picture of what success looks like to allow creative people to work

 

 

2016 MRA CRC – Mapping the Millennial Path to Purchase

My notes from “The Millennial Path to Purchase” presentation – Brandon Shockley(Plannerzone) and Kelly Bowie (Guardian Life Insurance)

Facts:

  • 92M millennials
  • largest generation in US history
  • by 2020 1/3 of population
  • Most educated generation ever
  • Will spend over $200 B annually.

The challenge: Deliver millennial insights to internal  stakeholders

  • Method: engage millennials to talk about a sensitive topic (insurance)
  • Result: will have to use a creative approach
  • Path to purchase homework
  • Photo sort
  • Personification

To make an “authentic” millennial presentation the Shockley  took some selfies

Wanted to see the journey from the customer’s perspective using focus groups

 

Challenge in focus groups:

  • flawed memory
  • observer effects
  • group think
  • communication breakdown
  • can be uncomfortable

And then it has to be presented.

Solution – give them homework! Will this work?

Kelly was very skeptical – but a great deal of work, went into the homework in many cases.

Techniques in context:

Nine focus groups across three markets

  • Married/committed (no children)
  • Married or single (children)
  • Single (no children)
  • Mix of intenders and recent purchasers
  • Process – receive prompt in advance, complete assignment at home, timed presentation

Homework prompt

  • ideas and assumption  you started with
  • thoughts and feelings at different steps in process
  • times when you felt an urge to act
  • how you made program at different steps
  • setbacks or obstacles

Homework tips –

  • incentivize creativity
  • probe key milestones,
  • set a timer,
  • welcome collaboration

Process

  • 25-35 photos (stock, not related to topic)
  • choose one that depicts where you are
  • limit number of photos
  • identify content in each photo
  • probe similarities and differences

Common themes

  • building and rebuilding,
  • making progress,
  • confident relaxation

Personification 

  • select a notable person who has qualities that you associate with insurance agents
  • select another person who has the qualities that would make the ideal insurance agent

Implications:

Has brought a lot of attention to the insights department of the company.

2016 MRA CRC – Stats 2 Story

The following are my notes from the MRA 2016 CRC presentation “Stats 2 Story” by Dave Decelle (Netflix) and Ted Frank (Backstories Studio). As there has been limited editing there will be typos.

Presentation ends with lights off and clips from “Moneyball” — good start!

Most people loved the movie — it was one big pitch for the use of data in sports. “Once again, nerds rule!”

But, before Moneyball, Bill James spent 20 years trying to get used to what he had come up with — and many people ignored him.

Dave is here as Ted’s case study on storytelling.

The movie had story-telling on its side, which had the advantage that Bill James did not.

Executivtes often say that seeing chart after chart of stats of a presentation is like a firehose.

So, rules:

  1. Keep it simple – like movies cut out about half of the book.
  2. Highlight what really matters – three or four things. Find out what they need – reformulate a product for example, then hit what is important
  3. Cut out everything else
  4. Parse it into chunks the brain can handle

Example – Annual Netlfix meeting called QVR

Dave had 30 minutes  to present, and he used these principles:

  • First used an example of not simple – used a MaxDiff methodology to show differences in use of “Netflix Original” logo. Took 1 minute 33 seconds.
  • Then he asked audience a quiz to see how much information people had rememberd
  • Then when he just used visualization and focused less on then details, and took a bit less time it was much easier to understand.

When he did the same thing at TMRE, he split the session into two groups, asked one two view the first method, second the other. Those who had seen the more detailed one generally knew the methodology but not the result.

Make it real (like movies)

  • setting
  • characters
  • action

Other example: 78% of Netflix members have heard of House of Cards (great), but only 38% know you can watch on Netflix (problem) and only 30% know that it is exclusive to NF.

Showed this by having an original picture of a picture from the show, then showed decreases beside them for each situation.

Make it Powerful & Emotional

works by – 1) deepening clarity and empathy/compassion and 2) inspires people to got off their butts

But – we usually speak to rational side.

Difference b/n Netflix & HBO Content Promotion

NF – helpful, informative, convenient, relevant – used bar charts to show that Netflix performs better than HBO on these scores, then used clips of customers with similar opinions

Make sure to create tension, play music, use framing, pacing and anticipation

Business generally doesn’t use anticipation – which is why everybody falls asleep in meetings. You do not convince them to stay engaged.

Example of using Tension in Presentation

David mentioned how social media was showing buzz around OTNB skyrocketed, but Netflix name did not.

Tension, how do you bring up the name of NTFX at the same time?

Then mention about study that showed the “Bill Burr” effect – adding the “A Netflix Original” logo, help to increase Netflix awareness of tie with show.

And make sure to add the other elements: pacing, music, tension etc.

Presentation was extremely well received, asked to show it to many different internal stakeholders – the power of a memorable story.

 

 

 

 

 

2016 MRA CRC – Gail Galuppo Keynote on Using Research to Be Customer Centric

My notes on Gail Galuppo (Afflac) presentation at the 2016 MRA CRC. Limited editing so there will be typos.

Takes 3-4 years to build an infrastrutcutre that is customer-centric.

Gail was ready to retire, but took this job 9 months ago and moved to Georgia for it.

Challenges:

  • Only 29% of customers are hapy with their current insurance provider. Gail was confused “doesn’t everyone love the duck?” – yes but not their insurance company.
  • Wasn’t relying on customer-driven, more on what agents wanted. So? How where they going to drive change.
  • Young customer don’t know what Alfac offers, and where to take money out of their budget to pay for products.

Age of the customer

  • 1900 – Age of manufacutirng
  • 1960 – Age of distribution
  • 1990 – Age of information
  • 2010 – Age of the customer

Aflac’s 2020 vision re-written to be voice of consumer.

“If we don’t put the customer at the heart of everything we do we’ll never convert from ‘sold not bought'”

Problem: even though duck was popular, research showed Aflac wasn’t highly differentiated.

Ciaims experience – big differentiator for Aflac, in the business to pay claims, not deny them. On average they paid within 4 days. Wanted to move that to 1 days.

One Day Pay became the standard – pay and deposit into customer bank account within that time.

Commercials focused on speed of payment, research had said that was of key importance.

One day pay was incorporated into all advertising channels to focus on benefit.

After 9 months of campaign

High

  • Awareness
  • Interest
  • Brand association.

Millennials:

  •  important to Aflac because by 2020 will make up 50% of workforce.
  • 62% of millennials feel online content important – used BuzzFeed to advertise
  • 1/4 say they understand disability – decided need to simplify conversation on disability insurance
  • Shared a video with their sales-force on how to sell short-term disabilities insurance to millennials

Result over last 18 months have seen an increase of 17% of business from millennials.

Consumers are not engaging with insurance companies outside of paying premiums – normally 1-2 times per year.

Some think that better not to contact them more often or they might cancel. BUT 49% want more content from their insurance provider.

Aflac has reached out to provide digital content, using the duck to talk about affordable care impact and other things.

The duck even has his own Twitter channel.

 

 

 

2016 MRA CRC – The Disruptive Force of Crowdsourced Data

The following are my notes from the presentation given by Simon Callan (Foursquare) at MRA’s 2016 Corporate Researcher Conference. Due to limited editing there will be typos.

Original premise of FourSquare – use your mobile phone to find cool places and compete with your friends. Still has two consumer apps to use on your phone.

Very much now on personalized city guides. After a week of using FourSquare it will start pinging you and suggest places to go to.

Swarm — is a game, allows you to check in and compete with friends over who checks in most at a certain place.

FSQ:  These apps generate a lot of data – 500 mllion photos, 87 million tips etc. From these can start mining tastes.

Swarm: 10 billon+ global check-ins, 8 million check-ins/day, 85 million public places and 105 million total places.

What do they do with it?

FSQ powers the geolocation of Waze, Uber, Twitter and What’s App.

Last year made a prediction of how many new iPhones were sold based on foot traffic to store. Predicted 13-15 million, and according to Tech Insider predictions “right on the nose” – closer than the analysts.

A lot of hedge and quant funds for stock predictions. Retail and CPG use it for customer analysis.

Predicted Chipotle’s sales dropped 30%; actually dropped 29.7%.

Location is hard to do, which is why it has taken FSQ several years to do.

FSQ can tell with confidence when someone is in an Apple Store, and stays there for a few minutes. Not looking at users being near places, need to be in the location.

Q: How do they turn the signals into data?

Have 55 million active monthly users global (25% in US). Look at this base and carve out a panel of about 12 million that they link to census data. Generates a total of 300 million visits a month.

Apply normalization to panel, weight census to gender for example. Also have to account for changes in app that might impact visit counts.

What does Foursquare provide:

  • Store level data – anyone can buy every single store – foot traffic, demographics, where they go before and after and  tastes
  • Chain level data – trends across chains
  • Market share reports

Turning this into insights

  • Can use to look at competitive reports of foot traffic counts between different stores within the same category
  • Can do this at a city level, to see which are under-performing or over-performing
  • Can see what people visit after they visit a certain type of store

Foot traffic

  • No need for expensive beacons, surveys
  • Is highly granular
  • Is near real-time
  • Provides context to other data (transactions, staffing, locations, media)

 

 

2016 MRA CRC – Enabling Business Growth Through Global Qualitative Research

The following are my notes from a presentation by Michael J Rosenberg (JP Morgan Chase). There has been very little editing, so there will be typos.

Has been to over 40 countries doing qualitative research, over 250 projects.

Normally with C suite, CFOs and treasurers.

Today’s reality – just in time research, flow results, actionability, Quantifiable ROI

Focused on – 80% growth and 20% performance — probably reversed from 10 years ago

Three dimensions of growth

  • Clients – new clients or increasing share of wallet
  • Geographies – expand into new countries?
  • Products – new product development, enhance existing products

Qualitative research is optimally suited to answer the key questions to help drive key growth

  • depth interviews (80% of what they do)
  • user groups
  • ethnography
  • focus groups
  • online communities

Looking on methodology is not enough

Top ten enablers (2014):

  1. Understand the business
  2. Ensure senior level business sponsorship
  3. Integrate the business into the research process
  4. Leverage “client friendly” methodologies
  5. Introduce new research methodologies and technology, but don’t push
  6. Leverage top talent
  7. Don’t ignore the numbers
  8. Deliver the results in the language of the business
  9. Engage in implementing the results
  10. Communicate regularly and with impact

Question: do these really enable business growth?

Important when speaking with clients to be able to understand & talk about the business on an educated level.

Business Intimacy is knowledge of:

  • Business strategy& tactics
  • Clients
  • Competition
  • Current capabilities
  • Market dynamics
  • Product/solution skills suite
  • Regulatory environment

The three Ds

  • Dialogue: more important to engage in a discussion than strictly asking questions Level 1) objectives, personna, cultural nuances, macro-economic environment 2) relationship history 2) micro-economic environment 3) personality
  • Deliverables: Who? What? When? How?
  • Delivery: 1) Business intimacy 2) Facts and results 3) Compelling messaging 4) Call to action

 

2016 MRA CRC – Building a World Class Research and Insights Function

The following are my notes from the MRA 2016 CRC panel discussion with the following participants: Brett Townsend (Pepsico), Mark Kershisnik (Transform Strategy Partners), Rob Stone (Market Strategies International, Pratiti Raychoudhury (Facebook) and Jill Donahue (Nestle Purina North America). There has been little editing on these notes, and hence there will be typos. Responses are from individual panel members, and have not been attributed.

Recent survey

  • 1/3 say dep’t does poor job of proving ROI
  • 1/2 say acting on insights is often/always a challenge

Key techniques to transform research into driving ROI?

  • Problem: Often get wrapped up in the “interesting”, which isn’t sales. “We don’t have the luxury of interesting”. Need to have the mindset of everything must lead to sales.
  • How i measure research is have i changed their mind
  • A lot of it is pre-work and post-work — what are you trying to learn/what is the objective and what is the answer you are looking for. Helps to determine how to design methodology. Post role is to always be voice of consumer.
  • Important to try to make the human beings that are customers as tangible. So they can find out what customers are feeling — helps them with inputs into what candiates they are choosing.
  • Have allowed researchers to say “no” if they don’t fit into the year’s strategic goals — can think of 100 things that need to be done but doesn’t mean they should work on all of them at once.

Recent study – 65% corporate researchers say they have too many projects for staff, 50% have too many projects for budget.

Q: How frequently do you allow researchers to say no?

Q: What are the other things you are doing to make sure you are delivering enough insights.

  • Have people look at what they already know before taking on another project if for example a previous study already answers 85% of what they need to know.
  • Also looked at researcher’s time to see how their day was spend. About 1/3 was spent on managing logistics. Ended up setting up a system and reduced it to 5% – created more capacity.
  • Put more of the burden on agencies hey work with. Said we are busy, you have to do more for us and you need to be more consultative in nature not just give us an 80 page deck.

What Have You Done to Help Your Agencies to Do This?

  • Tell them “give me the trade-offs and let me choose”
  • We expect them to be better story-tellers. If you can tell the story in 10 slides than you don’t have a story, you just have data.
  • Kick-off calls at the beginning of a project is important. Sometimes supplier wants to provide everything about the industry where only a tiny piece might be necessary.

Staff are becoming busier, how are you driving level of stakeholder engagement outside of project so it’s not an order-taking function?

  • Insights people are embedded within teams, brand insights people within brand team for example so that never occurs.
  • As a result of this researchers aren’t playing catch-up, they are aware of issues as things happen.
  • One panelist indicated that his company set up a lab where people could come into a room where they could watch research taking place remotely so they are part of the conversation.

Are you doing anything different with technology to engage stakeholders?

  • Not a tech result but created a quiz and had stakeholders figure out the answers.
  • Had video clips, asked questions they asked of execs, and then played video responses to the same questions from consumers.

Talent pool is difficult these days, skill sets keep changing (more big data, secondary data, social media research and text analytics, less trad focus groups, paper surveys, telephone interviews, in person interviews)

  • Try to have a very diverse pool of employees, split between academia and industry. Spent a lot of time understanding what students are learning in schools, then honed in on certain programs.
  • So many different skills exist now in research that it is impossible to have someone that can do everything. Important to get specialists in different areas, then setting up that person as the go to at the same time it is necessary to have generalists.
  • Need to look within the market research function, looking at people within different industries – example data scientists from financial industries, people with backgrounds like psychiatry, consumer behavior etc.
  • Also important to make sure that researchers keep themselves current.

 

 

2016 MRA CRA Presentation – What is a Good Experience Really Worth

The following are notes from the presentation by Wayne Hwang (Twitter) and John Mitchell (Applied Marketing Science) on “What is a Good Experience Really Worth? – Using Conjoint Analyisis to Quanitfy the Value of Customer Service”

Wayne told a story of United Airlines losing his suit one day before his wedding. He went through the standard customer service channels and nothing worked. Late at nigh he reached out over Twitter and complained about it. It was found within 10 minutes.

Got him thinking about the relationship between tweets and customer service.

Presentation based on airline industry.

85% of companies think they give good customer service, 8% of customers agree.

Airlines had record number of complaints in 2015.

Disconnect: in publication like HBR talks about how important customer service is.

80% of social customer service requests come from Twitter Not all of them are happy.

People generally say they don’t get responses from companies on Twitter, but are happy when they do.

Research questions:

1.Do customers remember good or bad experienes

2. Are they willing to pay more after a good experience?

Problem of asking people what they want  – want everything but they want to pay less.

Have to be cautionary, could be argued that even asking a question changes results. For example, 18% roughly of Republicans claim they would be very upset if their child dated a Democrat. However, roughly the same proportion of Red Sox fans say the same thing of their children dating a Yankees fan – so maybe asking research sets up a response.

The Study.

Group: Twitter users who in past 6 months receied a responses from an airline via Tiwetter (“Test Group”) as well as one that hadn’t (“Control Group”)

Summary stats

  • tweets included top 5 major US airlines
  • median time to response was 21 minutes
  • in 7,217 out of 273,359 tweets (Top 3) was less than one minute
  • In 59,514 response less than 5 minutes
  • Longest was 2,298 hours

Conjoint Basics

  • What is it – survey technique and model used to measure preference for products and services
  • Underlying assumption – consumer overall value or utility for a product is a weighted sum of the value of each of its parts

Used it as follows:

  1. Assigned people to cells given observed behavior and known experiences
  2. Varied the product attributes in a defined way in choice tasks
  3. Analyze how people choose
  4. Examined deltas in utilities across cells to back out brand value in dollars

Survey showed up in users Twitter feed.

Choice tasks based on airline, seat location, % on time arrival and price. Seat location and % on time were dummy variables, were really only trying to see if people would pay more if their issue was resolved.

Also asked willingness to recommend and a few other questions afterward.

Ran a hierarchical Bayesian regression.

Challenges

  • Hard to adminster because user experience had to be consistent with Tiwtter’s brand value – short, concise, clean and mobile
  • Analysis – control for halo around certain brands, ensure enough sample for pairwise comparisons among cells and build all final analyses by hand

Results

Responding quickly drives value

Customers were willing to pay slightly more if they were responded to in over 67 minutes, but over $20 more if it was only a few minutes.