Maximising Brand Reputation Online

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© Copyright 2013 Beyond. All rights reserved. Private and Confidential Maximising Brand Reputation Online Nils Mork-Ulnes @nilsmu

description

My slides from SAScon 2013 on the topic of "Maximising your online reputation" with some tips around how to gain and use insights into social conversations to manage brand reputation online.

Transcript of Maximising Brand Reputation Online

Page 1: Maximising Brand Reputation Online

© Copyright 2013 Beyond. All rights reserved. Private and Confidential

Maximising Brand Reputation OnlineNils Mork-Ulnes@nilsmu

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© Copyright 2013 Beyond. All rights reserved. Private and Confidential

TheAgenda

➡ How can you maximise something you no longer have much control of?

➡ Tips for making sense of your online reputation

➡ Going beyond basics

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People

growth

In the face of Exponential

informationare getting smarter

at using and filtering information

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10.4Source: Google/Shopper Sciences, The Zero Moment of Truth Macro Study

doubled to

the average numberof information sourcesused by shoppers

From 2010 to 2011

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You now have little control

Filtered Personal Stream

Content User state

TimelyRelevant

ActionableValuable

Shareable

Grazing/Lean-back

Lean-forward

• Bite-sized/Visual• Not actively considering• E.g.,

• Competitions• Videos• Infographics

• Transactional• Consideration & post-purchase

sharing• E.g.,

• Forums• Influencer content• Tools/guides

Social graph

Interest graph

Knowledge graph

of the customer journey

Consumer

Facebook

Twitter

Google

Tumblr

Pinterest

Forums

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of The roleword ofmouth

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2.1 billion a day

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

WOM Brand impressions

IN THE US ALONE

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6% occur onlineMANY MORE THAN

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

Of all WOM

BUT SEEN BY

OFFLINE WOM

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Highly Credible by 63%OF RESPONDENTS

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

Experience-based WOM rated

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51% say “try it” 5% Say “avoid it”

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

WOM tends to be positive

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Online word of mouth is correlated with salesPositive Tweets explains 5x the variability in sales of advertising

for video games in study of 12 months of 2012 data

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.

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Caveat: correlation ≠ causation

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Still, Companies mostly use quant metricsMeasuring social media ROI

Source: Duke University’s CMO Survey, 2013

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Key points

People like to share good experiences more than bad ones

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People trust experience-based word of mouth - and when it happens online it’s findable for many

And yet many companies don’t measure this

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MAKING SENSE OF YOUR ONLINE REPUTATION

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Don’t confuse

this thiswith

Carefully researched conclusions

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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 their tests of automated social media sentiment

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Monitoring is good for spotting and fixing problems quickly

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

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QUick methodology primer

21 3 4 5

Test and retest queries

Clean data Sample Tag Aggregate Analyse

Random & statistically significant

By humans, using custom

taxonomy

Do the sums & eliminate

errors

Make sense of it

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Key points

Do use SMM software to monitor for problems

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Do make the proper investment in time and £s if you want to share data with the C-suite

Make sure there is methodological rigour

? 28:32

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GOING BEYOND BASICS

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TROUBLESHOOT THROUGH SEGMENTATION

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Segmentation gives diagnostic clarity

Top 10 (Dis)satisfaction Drivers Top 10 Attractors

and Barriers

Top 10 (Dis)satisfaction Drivers Top 10 Attractors

and Barriers

1. Call Centres2. Reliability3....4....

1. Pricing2. Reliability3....4....

1. Call Centres2. Reliability3....4....

1. Reliability 2. Features3....4....

Former Customers4%

Non-Customers18%

Considering Customers12%

Customers66%

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LOOK BEYOND YOUR BRAND

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Most conversations aren’t about your brand

0.5%0.5%

99%

You and your competitors

Their lives Brand A Brand B

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Cultural Tensions: My needs

My family’s needsSociety

Popular culture

Brand Proposition:How awesome we are

Meeting needs

Content strategyBranded utilities

Product development

Staying relevant

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“white space” ANALYSIS

Discover conversations that:

1.Fit with your brand

2. Are large or will be

3. Appeals to your audiences

4. Is "interesting" 0

10 20 30 40 50 60 70 80 90

100

Apr-12

May-12

Jun-1

2

Jul-1

2

Aug-12

Sep-12

Oct-12

Nov-12

Dec-12

Jan-1

3

Feb-13

Mar-13

0.00% 0.00% 0.00%

1.73% 0.00%

0.65% 0.22%

HP Oracle

IBM Google

Cisco Mcsft

GE

How “White” is the space?

Potential Market Value: $131 B

Trend data

“White Spaces” are topics that cover the common ground between a brand’s offerings—whether this offering is as tangible as an actual product or as abstract as a value proposition—and the needs of its target audiences.

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“Pain Point” ANALYSIS

WHAT ARE MOMS DISCUSSING?

negative positive

VOLU

ME

VOLU

ME

Baby clothes

Car seats

Cribs Diaper/changing

bags

Strollers

Slings/wraps

Maternity clothes Feeding chairs,

furniture

Carriers Products

Materials/fabrics

Comfort

Safety for baby

Ease of use/installation/navigation

(includes ability to use outdoors)

Compatibility with physical activity

Appropriateness for baby (e.g., is

baby too big)

Ease of cleaning colors

Patterns

Price/value

Attractiveness/ trendiness

Product CONSIDERATIONS

At which point of the need staGE CUSTOMER JOURNEY are moms posting?*

0%

Pre-awareness

7% 14% 17% 12% 67%

AWARENESS FAMILIARITY CONSIDERATION &

RESEARCH

Purchase

Recommendation & sharing

*Posting only about baby/maternity products or comfort/style with regard to pregnancy/taking care of baby.

WHICH EMOTIONS ARE MOMS EXPRESSING?* *Posting only about baby/maternity products or comfort/style with regard to pregnancy/taking care of baby.

36% Happiness

21% Confusion/FEAR

19% dissatisfaction

14% Excitement/hope

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Key points

There is more to social data than just measuring brand health

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Diagnose problems by segmenting data

Look beyond mentions of your brand and look for new opportunities to connect or pain points to address

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THANK YOUtimefor your

contact usNils Mork-UlnesHead of Insights and Analytics, EMEA+44 771 307 [email protected]@nilsmu