Will social data eat the market research industry? A look at the opportunities and challenges of...

© Copyright 2012 Beyond. All rights reserved. Private and Confidential

Will social data eat the market research industry?Nils Mork-Ulnes, BeyondTwitter: @nilsmu @beyond


INSIGHT

TECHNOLOGY SOCIAL

LESS FINANCIAL RISK, MORE CREATIVE PRECISION, AWESOME RESULTS


LONDON

BAY AREA

NEW YORK

Our clients drive the social web... and harness it


The opportunity: From scarcity to abundance of data

Source: New York Times, 2013

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“Between the dawn of civilization and 2003, we only created five exabytes [of data]; now we’re creating that amount every two days.” - Google


from this, Two kinds of digital data

2

1 Behavioral data

Attitudinal Data This presentation will focus here


Source: The Keller Fay Group, 2013, based on 1 year of data from more than 31,000 US consumers aged 13-69 @nilsmu @beyond

“Word of Mouth” has a always had a large impact on brands - but now we can measure it more readily

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2.1 billionUSin the

a dayalone

6% occur online


“the global market research market...

[is valued] at $31 billion...

...We believe 100 percent of the current marketing analytics market can be captured through social technology… with savings of up to $1Bn”

- McKinsey, The social economy: Unlocking value and productivity through social technologies, July 2012

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An industry ripe for disruption?


TheBENEFITS ➡Removes survey biases:

unprompted, uncompensated and unfiltered observations

➡Greater speed: more timely insights into preferences and trends

➡Cost: reduced data collection costs allows more of the budget to be spent on analysis

McKinsey report on benefits of social data:

OF SOCIALdata

@nilsmu @beyond


Thechallenges

➡Noisy unstructured data is hard to analyse at scale

➡Lack of standards - and lack of methodology maturity - leads to lack in buy-in in C-suite

➡Silos in organizations means that this data isn’t shared - social data tends to remain in comms function

➡New biases introduced, favoring the vocal & connected consumer

OF SOCIALdata

@nilsmu @beyond


short-term impact + predictive power, but...New game & movie release WOM has measurable correlation with sales

To start a new section, hold down the apple+shift keys and click to release this object and type the section title in the box below.

Executive summary

Context and methodologyTwitter provides a platform for people all over the world to instantly share news, announcements, thoughts and opinions about any topic, at any time of the day. With 200 million monthly unique users worldwide sending 400 million Tweets per day, the platform captures, generates and makes accessible global word-of-mouth in a way that simply did not exist a few years ago.

Companies have started to take notice of this word-of-mouth embodied in Tweets, and the power it can have on the success of products and brands. However, there is little available research that quantifies the impact of Tweets on actual business outcomes.

Twitter commissioned Deloitte to undertake a study to test whether Tweets have a predictive impact on the sales performance of video games that is additional to and separate from the effect of other drivers of demand, and to quantify that predictive impact. This report provides an overview of the methodology and findings from the study.

This study uses a state-space hierarchical Bayesian modelling approach to isolate and quantify the impact of positive, negative and neutral Tweets and the total potential impressions generated on the sales of the 100 bestselling Xbox 360 and PlayStation 3 titles in 2012. It uses commercially available data on sales of video games and the advertising1 of those games together with data on Tweets about the games supplied by Twitter. The Bayesian approach enables the estimation of title specific impact of different types of Tweets, while controlling for other key drivers such as market dynamics, traditional advertising spend and price. It also accommodates unpredictable shocks to demand caused by for example competitive title launches that otherwise could bias the estimated impacts. The result is a robust quantification of the impact that Tweets have on the sales of any particular title.

Key findingsThe results show that Tweets are statistically significant and commercially relevant drivers of the sales of video games in the UK. In other words, Tweets capture and can generate valuable word-of-mouth effects that directly impact demand, and that impact can be quantified.

The balance of sentiment in Tweets is a more powerful driver of sales than reach (or volume) alone, with positive Tweets having generally a higher impact than negative Tweets. Therefore, to gain the most out of the online word-of-mouth embodied by Tweets, companies would be best served by addressing the balance of sentiment about their games through increasing the number of positive Tweets.

Table 1 summarises the impacts on sales volume from a 30% change in positive Tweets, negative Tweets and traditional advertising on each genre, as a weighted average of the estimated title level impacts. The results in the table on the impact of positive Tweets correspond to a thought experiment: “How would the sales of a title respond to a having a 30% higher number of positive Tweets about it?”

1 “Advertising” in this report refers to traditional forms of ‘above-the-line’ advertising spend, such as on TV spots and magazine column space. In particular, it does not include any advertising spend on Twitter

Table 1. Impact of key levers on sales volume in the first 10 weeks of release by genre

Genre 30% more positive Tweets 30% fewer negative Tweets

30% more non-Twitter advertising

Shooter Sports Racing Action Role playing Other

Overall

8.1%6.3%6.3%4.2%8.0%3.1%

6.1%

2.4%6.7%3.3%2.0%7.5%3.1%

3.3%

1.6%0.7%0.9%1.9%3.2%3.9%

1.6%

Source: Deloitte analysis of data from GfK, Nielsen and Crimson Hexagon

The balance of sentiment in Tweets is a more powerful driver of sales than volume alone.

The results show that for the key genres of shooter, sports and racing games, the impact of having more positive sentiment is a multiple of the impact of increasing traditional advertising spend by an equal proportion. Across the full sample, the impact of increasing the number of positive Tweets is approximately four times the impact that would be obtained by spending more on traditional advertising in the same proportion. Additional positive Tweets are on average 9 times more effective in driving sales uplift than increasing advertising for sports games, and 5 times more effective for shooter games.

2

Caveat: correlation ≠ causation

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E.g., positive Tweets explains 5x the variability in sales of advertising for video games


Companies mostly use quant metricsMetrics used

Source: Duke University’s CMO Survey, 2013

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"When we say it's positive, the machine about 21% of the time says it's negative"Eric Schmidt, senior manager-marketing strategy and insights at Coca-Cola, on automated social media sentiment


Don’t confuse

this thiswith

Carefully researched conclusions


No Black boxesFor “market research grade” insight, you still need sound, transparent research methodology


getting the best from bothHUMAN: Ambiguity Machines: Scale

Ex: Twitter using custom mechanical turk pool for “human computation” = accuracy+scale


Key points

Social data is still a massive untapped opportunity for both brands and agencies to use along with conventional research

2 31

Abundance of data means investments must be made in “human computation” to overcome challenges of unstructured data

Methodological rigour and transparency needed for C-suite buy-in

28:32

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Social Methodology Traditional Methodology

Pros & Cons

• Targeting : Filtering through demographic data allows traditional methods to target a specific sample segment• Structured Data Collection: You will get answers to all your questions• Scaled Response: You can quantify a consumer attitude or a feeling • Representative Sample: Targeting specific demographics allows the researcher to study a representative sample • Data Analysis: Analysing quantitative data is easier than analysing qualitative data, which is unstructured

• Lower Cost: Permits frequent measurement• Targeting of Influencers: Online conversations are often conducted by influencers• Uncovering Trends: Unprompted observations are likely to lead to trend discovery• Group Comparison: Fairly easy to identify people who are pre versus post purchase• Uncovering Intent: Previous research indicates that online word of mouth is strongly related to intent• Data Versatility: Conversational data is unstructured and thus not limited by expected responses

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• Lack of Demographic Information : Demographic information within social media conversation often has to be inferred• Possible Response Bias: People more likely to share a very positive or very negative experience than a more neutral experience• Difficult to Analyze: To get high quality data, unstructured conversational data needs to be cleaned and analyzed in a labor intensive manner

• More expensive: Incentives and human factors drive costs• Group Comparison: Group comparisons are often costly and difficult due to the additional sample required• Structured Trend Discovery: Discovery of trends is limited to the type of questions the researcher is asking• Selection Bias: Incentive based studies attract a specific type of respondent• Observer Bias: Prompted observation miss what the researcher did not think of measuring


Where Social Data Can Add ValueExamples of uses of social data

Social Data Type Product Development

Marketing & Sales Operations Customer Service

Consumer Insights - category (unsolicited)

Discover unmet needs

Proposition development, positioning, and testing. Channel and campaign planning.

Crowdsourced Insights - category/brand (solicited)

Product co-creation

Proposition development, positioning, and testing. Channel and campaign planning.

Brand product/service conversations

Brand health measurement & positioning

Forecasting & monitoring

NPS measurement

Positive word of mouth; fan interaction

Advocacy and loyalty programs

Customer service interactions via social

Improve customer service processes

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HOW WE APPLY IT


Service attributes: Segmenting by customer journey stage

Complementing Voice of Customer Research:1. Ongoing monthly KPIs instead

of yearly cycle of VOC

2. Fraction of the cost

3. Captures important social WOM factor VOC can’t

4. Captures moment-of-engagement rather than after-the-fact

Current''60%'

Considering''12%'

Unknown'27%*'

Former''1%'

Top 10 (Dis)satisfaction Drivers Top 10 Attractors and Barriers

Top 10 (Dis)satisfaction Drivers Top 10 Attractors and Barriers

1. Call Centres

2. Reliability

3....

4....

1. Pricing

2. Reliability

3....

4....

1. Call Centres

2. Reliability

3....

4....

1. Reliability

2. SPEED

3....

4....


Consumer insights: which features people want

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Pin

Deals/Coupons Transaction tracking

RFID

Remote swiping

Ads P2P Payments

Digital Receipts Loyalty/Rewards cards Customer service

Digital/NFC keys Fingerprint verification

Frozen money

NFC Sticker Sonic Payments

Barcode Scanning Digital tickets Transaction errors

Transaction Speed

Battery dependency Multiple cards

0%

1%

1%

2%

2%

3%

3%

4%

4%

1"1.5"2"2.5"3"3.5"4"4.5"5"

Avg. Sentiment

Very%Posi*ve%Very%Nega*ve% Balanced%

Reasons to Adopt

Threats%

Barriers to Adoption Complementing qualitative research for market entry in electronic wallet space:1. Understand early adopter needs

2. Unprompted attitudes

3. Competitor SWOT

4. Fraction of cost of focus groups/surveys



Fewer people are searching for virgin.com

0

10,000

20,000

30,000

1-Sep-08 1-Jan-09 1-May-09 1-Sep-09 1-Jan-10 1-May-10 1-Sep-10 1-Jan-11 1-May-11 1-Sep-11 1-Jan-12 1-May-12 1-Sep-12

All Organic Search Referrals to Virgin.com "Virgin" Keyword Search Referrals to Virgin.com

Virgin.com referrals from search have grown by people searching for Virgin’s brands. People searching for Virgin itself have decreased.


Conversations align with some values and not others

Smart Disruption

(49%)

Red Hot (29%)

Delightfully Surprising

(19%)

Percentage of Conversations Including Brand Value*:

Insatiable Curiosity

(4%)

Fair Play (4%)

Heart-felt

Service (4%)

What Virgin should strive for in a content strategy:

Delightfully Surprising

Insatiable Curiosity

Heartfelt Service

Red Hot

Smart Disruption

Fair Play

Content

* Out of a sample size of 900 conversations © Copyright 2012 Beyond. All rights reserved. Private and Confidential

Who’s talking about virgin and entrepreneurship?

68.1% Male

31.9% Female

Skews Male

Skews 18-35

0% Under

18

63% 18-35

34% 35-65

3% Over

65

Business/Technology Interests

Business Owner Focus

31% Business Owner Self/Employed

22% Corporate Executive

17% Works in /with Technology

11% Musician/Artist/Writer/Filmmaker

3% Student

16% Other employee type

36% General Business

20% Technology/Engineering

18% Startups

7% Sports and Fitness

6% Art and Design

5% Music

4% Travel

2% Environment

61% USA

11% UK/Ireland

7% Asia

7% Western Europe

4% Australia and N.Z.

3% South Africa

3% Other Africa

2% Central /

Latin America

2% Eastern Europe

1% M.E.

USA Focus


Conversation is on average neutral

Negative Tone

Positive Tone

Neutral Tone

High Penetration

Low Penetration

Business Tips and Advice

Small Business Focus

Startups + Tech Culture

Young Entrepreneurship

Work/Life Balance

Social Responsibility

Sustainability

Entrepreneurship Subthemes within Social Media Conversation Takeaways

• Content could take more of a stance or instigates a response, put forth a genuinely unique idea, or looks at business from a different angle.

• Harnessing Virgin’s community of followers to write content about entrepreneurship – tech and startups in particular – could provide content on either side of the neutral line to help drive conversation.


what emotions are surrounding the conversations?

Contest: (47%)

Anticipation: (29%)

Wonder: (8%)

Recommendation: (4%)

Experience: (1%)

Humour: (1%)

Other: (9%)

Emotive Reaction Within Conversations

31% 2% Call to Action* 20%

No reaction: 47%

*Call to action: the post actually directs users to engage with the content or with Virgin itself (e.g., “you must read this ASAP”).

Takeaway

Music festival content should build on consumers’ sense of anticipation, as well as aim to keep the experience alive after the festival itself.

What triggers reactions in the Emotive Conversations?


Target personas Kate Randolph @kran2010 Age 20 Student Manchester, UK

“Music Festival Junkie”

Key points: • Student at university • Very outgoing, social • Knows hot music trends

What’s important to her? • Keeping up-to-date on the music fests in the UK with lineups, prices, dates and giveaways. • Having a great time dancing and listening to music with her friends, as well as meeting new people with whom she can share these fun experiences.

What’s she looking for? • Deals on tickets and special access for the music festivals she wants to attend, as well as information on early lineup releases, new venues and after-hours shows. • Fun ways to enjoy the music festivals she can’t attend from afar, via television, streaming or a combination.

• Loves to dance • Interest in sports and fitness • Uses Facebook, Twitter

James Fallow @singinjames Age 28 Independent Musician Austin, TX

“Self-made Musician”

Key points: • Devoted to his craft • Trying to ‘make it big’ • Seeking sponsorship

What’s important to him? • Making time outside of his work to write his music and practice the guitar, as well as prepare for shows, promote himself, and try to raise money for an EP. • Maintaining his musical originality and seeing its effect on people’s pleasure.

What’s he looking for? • New methods for promoting himself, financing a record, travelling to shows and finding equipment. • A means to get involved in the music community by keeping a handle on the scene and by supporting his fellow musicians.

• Side job as radio DJ • Uses Twitter, Facebook • Works at a recording studio

and a local venue at night

Sam Spear @samuelspear Age 37 Senior Account Manager, PWC London, UK

“Enthusiastic Music Advocate”

Key points: • Self-proclaimed music fanatic • Explores the current trends • Side interest in sports and art

What’s important to him? • Being the tastemaker among his group of friends by discovering the best new indie and pop artists. • Attending concerts and music festivals after work and on the weekends to meet new people and share common interests about music and other culture.

What’s he looking for? • An online content destination where he can discover new artists, read profiles and download playlists. • Exclusive behind-the-scenes looks at music festivals and concerts, ways to watch concerts and festivals he isn’t able to attend, and access passes for other shows.

• Mid-level employee • Often attends concerts • Spreads taste to peers

Target personas*

*Personas are formulated based upon the most common trends within the demographic data. They are not actual people.

From social data through strategy to designing new Virgin.com


THANK YOUtimefor your

contact usNils Mork-UlnesHead of Insights, Analytics & Strategy

+44 (0)2079 086556+44 (0)7713 [email protected]

mailto:[email protected]

mailto:[email protected]

Will social data eat the market research industry? A look at the opportunities and challenges of...

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