Ik ben een aanrader

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10 point of views. You agree or not?

1.   Distrust of adver0sing is the most important reason why consumers rely more and more on recommenda0ons.

2.   Recommenders have a narcissis0c personality disorder. Helping others is the least of their business.

3.   Brands should invest more in their product and service delivery instead of raising their marke0ng budget.

4.   When brands measure sa0sfac0on and re-‐purchase inten0on among your own customers, you know everything you have to know.

5.   Don’t waste your 0me neutralizing the detractors of a brand and try to wake up the passives. Focus on the promoters.

6.   Recommenders win the baJle on facts/features based argumenta0on. Never on emo0ons. It’s a “Test-‐Aankoop” world we live in.

7.   Unless you day-‐aTer-‐day search for recommenders, make them happy end influence them you will never succeed in the new marketplace.

8.   RFM will remain thé parameter for segmenta0on of your marke0ng ac0vi0es.

9.   Media that have the highest % of brand recommenders should be the prime medium in which brands have to buy space/0me.

10.   Social media like TwiJer, Facebook, Instagram, Pinterest are the best performing media for recommenders to influence their followers.

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I am a recommender. One of the 15% recommenders worldwide.

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Recently, on my Facebook-page I recommended Europe … while I made clear I didn’t recommend Shanghai.

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I even was an active multimedia recommender. I took pictures and screenshots. That’s probably only 1% of all citizens doing that.

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I published my recommendations on Buzzfeed to make my case stronger and get more reach. (I did it once☺)

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There we go! That’s how it looked like when it was published.

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I could follow closely the (meager) results of my recommendations.

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I am more successful to attract readers for my recommendations on Tripadvisor though.

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Are all of these other snippets also recommendations? Like mine? Not really. But some people think they are…

And nobody mentions TV?

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Why do brands and media get more and more interested in recommenders?

Why do some consumers write recommendations?

Why do all the others read their recommendations?

Why are they willing to be influenced?

Q&A’s in the next 15 slides.

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Recommenders directly influence 20-50% of all purchase decisions.

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The motives of recommenders? Zocalo Research

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“Please indicate which, if any, of the following influence your decisions when deciding whether or not to purchase or sign up for a product or a service”

The latest data on influencing power from … China (Epsilon 2013).

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“Friends & family” are the trusted source. Where is advertising …?

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Zocalo’s research

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“Friends & family” - recommendations lead. Social Media?

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Social media?

https://www.custora.com/pulse/channel

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Reviews do indeed the job!

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Yelp? Trusted? (Zocalo)

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Trust? Watch out. Some reviews can be fake. Consumers are fully aware.

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The big issue: “Influencing power” and “action generating power” of paid, owned and earned media (Nielsen 2013)

Paid

Earned

Owned

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Hence: brands permanently remix Paid, Owned and Earned Media based on their respective ROI (Zenith 2013)

Earned media have most influence, are more trusted and generate more action

Paid media generate more touch points, hence they are strong in building awareness. Their “Opportunity To Be Seen” is bigger.

Paid media cost more than earned and owned media. Brands plan to invest less in paid media in the coming years.

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1980-‐1990:Adver.sing +10% = +2.2% marketshare 2008:Adver.sing +20% = +2.2% marketshare

“ …. the authors conduct a meta-‐analysis of 751 short-‐term and 402 long-‐term direct-‐to-‐consumer brand adverCsing elasCciCes esCmated in 56 studies published between 1960 and 2008. the study finds several new empirical generalizaCons about adverCsing elasCcity. the most important are as follows: the average short-‐term adverCsing elasCcity is .12, which is substanCally lower than the prior meta-‐analyCc mean of .22; there has been a decline in the adverCsing elasCcity over Cme.”

Gerard Tellis, PhD Michigan, is Professor of Marke.ng, Management, and Organiza.on, Neely Chair of American Enterprise, and Director of the Center for Global Innova.on, at the USC Marshall School of Business. He is Dis.nguished Visitor of Marke.ng Research, Erasmus University, RoVerdam and has been Visi.ng Chair of Marke.ng, Strategy, and Innova.on at the Judge Business School, Cambridge University, UK. Tellis specializes in the areas of innova.on, adver.sing, global strategy, market entry, new product growth, promo.on, and pricing.

Prof. Dr. Gerard Tellis

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One of many brands’ issues: “The ROI of advertising is not what it was before”.

+7% +3,8% +3,8% +4,6% +5,2% YOY-growth

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And when people talk advertising. They still talk TV. Let’s have a look.

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18-24 Year Old in the US watching less TV. Doing what instead?

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They generate content. “User generated content”. Or they watch that UGC. (Recommendations are part of it).

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User generated content grows. Mainly on mobile. But of course, not all UGC are recommendations.

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“50% of the internet traffic today is video. Looks like 80% to 90% of internet traffic will be video in the next few years. Video for us is a place that consumers really like.” (Tim Armstrong AOL)

“Google's biggest revenue driver in the future. Mark Suster of Upfront Ventures, which invests in a large YouTube content partner, suggested to the Wall Street Journal that the video platform could soon be generating $20bn in revenue.”

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VRT & Video & Instagram. Vers nieuws. Fijngesneden (1)

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Next to TV there are also media like Twitter. Let’s have a look here too…

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Tweeted online recommendations are “massive” broadcast + engagement.

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Only 3.6% of tweets are about brands. Majority of recommendations are offline!

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Offline! Still the method of recommendation.

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Males complain. Females talk.

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Industry sectors in which brands are mentioned on Twitter

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The social consumer. Buying a lot based on recommendations from other social consumers

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No comment.

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Some buy a lot online and tell it to a lot of people online too.

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What do we suggest you to do @ this ever changing marketplace?

1.   Measure your own recommenda0on power – use NPS – read Reichheld’s books – or hire us for a while.

2.   Measure your compe0tors’ recommenda0on – use “public, real 0me, con0nuous” benchmarkers like Holaba.

3.   Never stop the search for recommenders. Also in your own database. They are so important.

4.   Iden0fy recommenders (Socio – Demo). Don’t rely on samples.

5.   Gather insights (why do they say what they say) of real recommenders.

6.   Select the media to influence these influencers

7.   Don’t push too much. Let them find you.

8.   Be nice and trustworthy. Be human. They are human media too.

9.   Do all the above points permanently, wherever you can.

10.  Add the recommenda0on data to data-‐streams about traffic, conversion, sales, sa0sfac0on, re-‐purchase intent, rfm models ….

11.   Adjust your tac0cs everyday. Never give up. Measure everything.

12.  DIY!

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Evalua.ng Buying

Selec.ng brand

Consuming

15% are “influencing”

others

Selec.ng shop

85% are being

influenced

1st moment of truth 3rd moment of truth 2nd moment of truth

(Neutral) Promoter><Detractor Consumer

Moments of truth. All 3 are crucial.

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Why is “recommendation measurement” so crucial?

7

14

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Benchmarking.

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Recency, frequency, monetary value (RFM) of clients are decisive for investment in marke.ng communica.on. Therefore lots of money spent (wasted) in this group of heavy and recent buyers RFM -‐ axis

Light and non frequent buyers are oden “neglected”

From RFM to RRFM to decide about what to invest where.

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Recommenda0on axis

+RFM & -‐ REC

-‐ RFM & -‐ REC

+RFM & + REC

-‐ RFM & + REC

RFM -‐ axis

The recommendation power of clients becomes the decisive tool to decide on marcom-investments

Does not mean they all give posiCve recommendaCons

Frequency and intensity of recommenda2on.

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Sun Tzu, The Art of War, hVp://www.youtube.com/watch?v=erZ2YidTZp4. Minute 06:45

“If you know the enemy and know yourself, you need not fear the result of a hundred battles.”

“If you know the enemy and know yourself, you need not fear the result of a hundred baWles. If you know yourself but not the enemy, for every victory gained you will also suffer a defeat. If you know neither the enemy nor yourself, you will succumb in every baWle”

Marke2ng is War. It s2ll is.

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58,1% of the scores are 9 or 10 38,1% of all scores are in between 0 and 6 included. SUM OF 9 & 10 SCORES SUM OF ALL OF ALL 0 TO 6 SCORES

58,1 -‐ 38,1 = +20

10 9 8 7 6 5 4 3 2 1 0

How likely is it that you will recommend this brand?

NPS-‐score & Holaba-‐score are indicators of recommenda2on power. In this case the score of the brand is +20

The One Ques2on Holaba asks always and everywhere.

The calcula2on of the recommenda2on score based on 1000’s of consumer scores.

A NPS-‐score is a number in between -‐100 and +100

-‐100 +100 Benchmarking to find out where all major compe2tors are on this scale.

“Net Promoter System” was launched in 2003 by Mr. Reichheld of Bain.

It not only measures the recommendaCon power of brands but also of consumers. Consumers can either be promoters, detractors or just fence siWers.

The Holaba data. Produced by our benchmarking-tool based on the “Net Promoter System”.

What is NPS*?

* Net Promoter, NPS, and Net Promoter Score are trademarks of Satmetrix Systems, Inc., Bain & Company, and Fred Reichheld.

Very likely Not likely

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The flow of the ques0ons that produce the data. Easy and fast for consumers. S0ll delivering plenty of informa0on.

Scores in between 0 and 10.

5 experience levels

Easy for consumers to give posi2ve and nega2ve comments

0 1 2 3 4 5 6 7 8 9 10

None Have it Had it Will have it Could have it

This is what I like: “….”

This is what I don’t like: “…”

This is what they should improve: “…”

1. How likely is it that you would recommend this brand?

2. What is your experience with this brand?

3. Why do you give the score you give?

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Day after day Holaba-users can share opinions. That way they gradually build their brand profile.

Scores 9 -‐10

Scores9-‐10

Scores 9-‐10

Scores9-‐10

Scores9-‐10 Scores

9-‐10

Scores9-‐10

Scores9-‐10

Scores9-‐10

Scores9-‐10

Scores9-‐10 Scores

9-‐10

Scores 0-‐6

Scores 0-‐6

Scores 0-‐6

Scores 0-‐6

Scores 0-‐6

Scores 0-‐6

Scores 0-‐6

Scores 0-‐6

Scores 0-‐6 Scores

0-‐6

Scores 0-‐6

Scores 0-‐6

The brands these boys also recommend

Brand Profile of boys 18-25 yrs old, who all give a very high 9 & 10 recommendation score to brand X.

The brands they don’t recommend at all.

Scores 0-‐6

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1. We know the brands they do & don’t recommend

-‐ all users build their personal brand profile. 2. We know (since they tell us) their status with the brands they review

-‐ actual user -‐ past user -‐ future/potenCal user

3. ATer a while we also know who is a recommender or not.

-‐ Why is that important? •  The more recommenders we iden.fy, the higher the return on marke.ng investment will

be: only then brands can start influencing these influencers first.

•  Someone who mainly gives 6_7_8 scores is probably not a recommender, but a fence siVer.

° And of course we know: 1.  Gender & Age 2.  Loca.on 3.  Educa.on (income?)

4.  Network 1.  Size of their network 2.  Degree of interac.vity – Klout score 3.  SNS-‐member ship

5.  Phone number & Email address (physical address if needed)

Holaba-‐data are data our registered users share about the brands they recommend.

Our users build profiles brand aYer brand.

We learn who the recommenders are.

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Holaba is the ideal tool for companies … –  Who already monitor their own recommenda.on power. –  Who want to partly base their strategy and tac.cs on the score of

their compe.tors.

EXAMPLE Your own research points out that your recommenda.on score is +20 Now you want to know how good or bad that is, since an isolated score

shows only your part of the story.

Very bad ranking for “You “

Excellent ranking for “You”

More or less ok ranking for “You”

1 Competitor 1.You (+20) 1 Competitor 2 Competitor 2 Competitor 2 Competitor 3 Competitor 3 Competitor 3 Competitor 4 Competitor 4 Competitor 4 Competitor 5 Competitor 5 Competitor 2.You (+20) 6 Competitor 6 Competitor 6 Competitor 7 Competitor 7 Competitor 7 Competitor 8 Competitor 8 Competitor 8 Competitor 9 Competitor 9 Competitor 9 Competitor

10.You (+20) 10 Competitor 10 Competitor

Brands indeed win more baJles when they monitor their own recommenda0on score … and compare it to their compe0tor’s.

Are you in a good posi2on for the coming months?

When you know the score your clients give, you also need to know the ranking of you and your compe2tors.

A high score and be^er ranking is a prelude of a growing market share.

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Two reasons why the Holaba-‐data are important for ‘paid media’ too.

1. Media that know which brands their audience recommend or not recommend (i.e. brand profiling) have more arguments to sell space & 0me to brands. Selling to readers/viewers/listeners who recommend a brand is different from selling to non-‐

recommenders.

2. The more “recommenders” a medium has, the higher its value: they can sell access to them at a higher price. Selling to the 15% brand recommenders gives brands more potenCal to generate free word of mouth.

Recommenders have a vast network and talk

about brands.

Fence sitters have a smaller network and talk

less about brands.

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For brands that buy time & space in media, the added value of the 15% (or more) recommenders among media-audience is huge.

When brands influence the influencers in your audience, they influence more than just the influencers.

Extra-contacts generated : brands reach consumers through media, but since not all consumers are created equal, some become “human media” and some not.

Brands

Media

“Human media” “Just humans” “Just humans”

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NPS

Do ask the total market. Do ask “non-clients” too. They too judge, talk and influence. That’s why we ask them.

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Those who “want, but cannot yet” are strong recommenders too. In 2006 I recommended even Jaguar. Now …Tesla.

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1.Comparison based on turnover/M2

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2. Comparison based on sold items/ticket

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3. Comparison based on ticket price.

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4. Comparison with objectives in annual budget

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5. Comparision with sales in previous year

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Brandprofiles built on the Holaba-platform

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“Recruitment” will be most oTen indirect.

Through brands’ ‘owned media’ and ‘paid media’ like yours.

•  On your newssite (widget on home page, pop-‐up to survey)

•  In your email to clients, prospects, former clients

•  On the package

•  Off -‐ & online shop. (The best recruitment moment is immediately ader a purchase)

•  During phone survey’s through your call center.

•  In an SMS.

We target recommenders. They search and check review sites more oTen. –  They are indeed very ac.ve when they are in a pre-‐purchasing phase and

search for second opinions.

•  Like they use Tripadvisor or any other review-‐site … when they feel the need.

–  We indeed want those respondents (15% of all consumers) who are pro-‐ac.ve and eager to voice their opinion. Pushing someone into giving his opinion doesn’t work.

15% of all consumers are recommenders. We find them where they are ac0ve.

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Which real users give high/low scores? On which brands?

Real consumers are beWer than any anonymous survey Why do they give the scores they give? How does your audience compare -‐on a brand profile level-‐ with other users in

our plaworm?

What is the % of recommenders in your audience?

She gave a 6

He gave a 5

He gave a 10

He gave a 10

He gave a 7

She gave a 9

She gave a 3 He gave a 9

He gave a 8

He gave a 10

Paid media that support the Holaba-tool & drive traffic to it in their medium will get access to the data. Which data will they get?

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Confidence level 95% 99%

Confidence interval 4 4 Sample size needed 600 1.040

Confidence interval 3 3 Sample size needed 1.067 1.849



Hypothetical population 100.000.000

We concentrate on those consumers –recommenders-‐ who make the difference. They influence the others: recommenders influence directly 20-‐50% of purchases.

We don’t survey a couple of 1000 average consumers (representa2ve for 10.000.000 average consumers)

We iden2fy the “born recommenders” (15% of all consumers) who are representa2ve for other recommenders.

We don’t need a lot of “samples” to have trustworthy results, representative for “the recommenders”.

All consumers

hVp://marketresearch.about.com/od/market.research.surveys/a/Surveys-‐Research-‐Confidence-‐Intervals.htm

15% recommenders

What is a valuable survey within a given period? • 1. Each survey should have 1000-‐1500 parCcipaCng registered users. • 2. Brands reviewed by less than 100 consumers spontaneously, will not be included in the overall result.

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1.   Evalua0on of exis0ng products and services 2.   New product development-‐ideas 3.   Price elas0city calcula0on 4.   Distribu0on analysis 5.   Marke0ng posi0oning 6.   Communica0on audit 7.   ATer-‐sales performance

–  Dealer performance data 8.   BeJer segmenta0on

–  Up to the level of age, loca.on (country, region, city,) gender,

… and plenty of other data you want to gather in the private part

Brands/Media will get plenty of data to improve their business on several aspects. Even at dealer-‐level.

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1.   Discovery of recommenders on as many external plaworms as possible

You will find them anywhere. You only have to be ac.ve. 2.   Iden0fy them.

Name, gender, loca.on, brand-‐scores & reviews, brand profile and many more. 3.   Understand them.

Why do they give the score they give. They intui.vely update your SWOT-‐analysis. Based on individual consumer insights, brands can target and personalize

their communica.on on several plasorms. 4.   Influence them.

“Influence the influencers” because influencers are media -‐ human media

Even more ac0onable data: discovery and Iden0fica0on of new, interes0ng consumers.

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Almost finished

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The difference between a “recommended” beer and a marketing beer.

http://www.ratebeer.com/

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The difference in family fortune between a “recommended” beer and a marketing beer.

€ 270 million

$11 billion

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Appendix

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Boston Consulting Group launched BAI (december 2013)

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BCG-2.

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BCG-3. Predictive power

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BCG-4. Leaders have recommenders. Laggards detractors