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VW BP Daimler Toyota Mitsubishi Why do companies risk their reputation?

Please read Updates about Mitsubishi, Opel, GM, VW in the comments below.

Summary: VW intends to repurchase 480,000 vehicles in the US. Estimates put the final bill in excess of €45 billion. But how much damage will this cause to their:

– brand
– image (e.g., the image of the VW or Audi brands), AND
– reputation (e.g., reputation of the brand or company)?

Volkswagen has an interesting portfolio consisting of luxury brands as well as truck and low-cost car brands, as shown below.

[su_custom_gallery source=”media: 3719″ limit=”7″ link=”image” target=”blank” width=”780px” height=”449px” Title=”Volkswagen brands read like a who’s who – how much will the emission scandal damage the value of their brands?” alt=”Volkswagen brands read like a who’s who – how much will the emission scandal damage the value of their brands?”]

1. Defining the terms

In daily life we may talk about brand, image and reputation interchangeably, without drawing a line between them.

But we cannot truly appreciate the value of something, if we do not unterstand what it is we are examining. And Jeff Bezos may have thought he was talking about brand, when he was actually talking about the reputation of the Amazon brand with its clients:

Your brand reputation is what people say about you after you have left the room.

We need to define brand, brand image and brand reputation. Only then can we be sure that we share the same vocabulary, which is the basis for understanding each other.

You need a great product. Your image of offering great design or R & D does not hurt the company either (for example, Apple). Crowned with a reputation for offering great client services (e.g., your neighbourhood grocer), you should do well in the market place.

[su_box title=”Table 1. Defining brand” box_color=”#86bac5″ title_color=”#ffffff”]
The word brand originated with the practice of putting a hot stamp on the bodies of young livestock to indicate ownership (i.e. branding calves). In the corporate world, a company’s logo or the lettering used for write its name may similarly serve as a stamp. It brands the firm.

The cattle brand helps one separate stock from Ranch A and Ranch B. In turn, the company’s brand or its logo help us recognize the product on the shelf.

The brand symbolizes what we stand for in the minds of people that we are trying to reach, influence and move to action (see Deborah Maue, 2015).

Brand is what the corporation tells the public or its investors, the news it shares about itself or the product, and most importantly, what it wants and aspires to be.

This gives the brand manager some control over the brand.

A brand helps reduce uncertainty for a client. The customer knows what they get, such as a hotel chain’s rooms offering the same features (make-up mirror, good hair dryer) as standard around the globe.

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But these days, mistakes can damage a brand’s image and consumer trust in the brand may evaporate as well.

For instance, in a 2016-04-20 media briefing Mitsubishi Motors president Tetsuro Aikawa tried to take responsibility for the manipulated fuel-economy test data. It affects 4 mini-car models sold in Japan, about 625,000 vehicles since 2013.

In just three trading days, the fuel-economy scandal has destroyed 42 percent of Mitsubishi Motors’ market value.

[su_custom_gallery source=”media: 3711″ limit=”7″ link=”image” target=”blank” width=”780px” height=”339px” Title=”With the increasing complexity of the marketplace, CONSUMERS focus on select dimensions of products such as price, not quality.” alt=”With the increasing complexity of the marketplace, CONSUMERS focus on select dimensions of products such as price, not quality.”]

As the above research illustrates, with increasing market complexity, consumers may fall back on such factors as price, instead of focusing on quality.

Hence, as BP’s Deepwater Horizon disaster suggests, Mitsubishi Motors can hope that people will forget that the company abused customer trust by falsifying fuel economy test data.

If that happens, customers may no longer focus on this disaster. Instead price or options offered with its cars may be most important in the decision-process to buy or not to buy a Mitsubishi Product.

[su_box title=”Table 2. Defining brand image” box_color=”#86bac5″ title_color=”#ffffff”]

A brand’s image tells us the qualities of the company or its products. Image is based on how much effort a company spends on getting its message across and its target audience to believe it (e.g., just do it – Nike).

Advertising is about image.

For instance, green advertising helped BP recover from the Deepwater Horizon oil spill. This means the visual, the look, the controlled viewpoint about an issue the company cares about, such as the environment, makes up the corporate or brand image.

As the video clip below shows, with the help of TV spots Volkswagen was trying to portray itself as producing cars running on “clean” diesel engines. Until September 2015 when the fuel emission scandal began, consumers believed this story.

[/su_box]
Advertising can be used to improve a corporate image or try to portray a greener image than one might otherwise have in the public’s eyes.

Watch this humorous VW commercial aired in the US.

[su_box title=”Defining brand or corporate reputation: An experience-centric concept.” box_color=”#86bac5″ title_color=”#ffffff”]

Attitude denotes the subjective, emotional, and cognitive based mindset (see Schwaiger, 2004, p. 49), which implies splitting the construct of reputation into affective and cognitive components.

The cognitive component of the construct can be described as the rational outcomes of high reputation. Examples include high performance, global reach and one’s perception of the company (e.g., great employer, wonderful customer service).

The affective component of reputation is the emotions that respondents have towards a company. Thus, people talk about these things with friends (word-of-mouth). Media coverage can also influence how we feel toward a company.

Reputation is hard-earned and generally long-standing. Nevertheless, it can be harmed by a new product that is shabbily put together or a big product recall as Toyota experienced with Prius in 2010 in the US and elsewhere.

Reputation is temporal, meaning for example that bad customer service will result in bad customer testimonials on webpages or blogs (what is called earned and social media).

Reputation is primarily based on my experience (i.e. cognitive) and what my friends say (affective). Hence, a bad experience may get me to write a bad product review or a post on Facebook. Good and bad press about a brand is also shared with one’s friends…

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Interesting: Fiske, Rosanna (2011-01-26). Image vs. Reputation: Which Reigns Supreme? Advertising Week, retrieved April 24, 2016 from http://www.adweek.com/news/advertising-branding/image-vs-reputation-which-reigns-supreme-125527?page=2

General Motors learned the temporal nature of reputation in 2014, after managing to deflect most of the blame for a 30 million vehicle recall on the habits of the old guard of the company, prior to a massive government bail-out. Never mind that GM knew all along about the ignition switch issue that caused up to 169 preventable deaths.

On February 1, 2010, Apple co-founder Steve Wozniak claimed that he had experienced a ‘software-related acceleration problem’ with his Prius, that causes the car to go wild under certain conditions when cruise control is engaged.

This and his comment that, “This is software. It’s not a bad accelerator pedal. It’s very scary, but luckily for me I can hit the brakes,” spread like wildfire via newswire, Twitter and others.

In early 2010 I wrote:

“We all remember when Audi (Volkswagen) faced unintended acceleration problems with its 5000 model in the US in the mid-1980s. Its initial response was to run advertisements of its top executives talking about its vehicles’ mechanics.

Audi was vindicated eventually but its effort to regain customers’ trust flopped amid perceptions that it built bad cars and was not taking the problem seriously. It took Audi 10 years to recover from this public relations debacle…”

Of course, one can only hope that #dieselgate will not hurt Volkswagen and its brands for another 10 years. But the share price drop as well as the compensations to be paid to US car owners whose models are affected suggest that it will be worse (see chart below for more information).

[su_custom_gallery source=”media: 3710″ limit=”7″ link=”image” target=”blank” width=”780px” height=”456px” Title=”-15.4 percent yer-on-year fall in VW brand car sales in the US, 1 in 20 German jobs depends on the sector.” alt=”-15.4 percent yer-on-year fall in VW brand car sales in the US, 1 in 20 German jobs depends on the sector.”]

2. Bottom line

Arguing which has greater influence — image or reputation — is likely a moot point.

Nevertheless, the two are linked, as the BP Deepwater Horizon oil spill suggests. At the time, many Americans called for BP boycotts and sales took a hit despite the fact that BP was still selling the same fuel it was selling before the crisis.

In essence, BP’s negative reputation caused consumers to perceive BP’s brand differently.

Reputation, then, is best used as a way for companies to differentiate themselves from other organizations with the same brand.

It takes plenty of money and effort to build a great image. But one mistake can cost a company dearly. For instance, Nike and TAG Heuer ditched Maria Sharapova, the world’s highest paid female athlete, after she revealed she had failed a drugs test at the Australian Open in January 2016. Both brands felt it was too risky to continue sponsorship.

Although the two brands avoided a fall out from Maria Sharapova’s problems, the athlete’s marketability as an image ambassador was severely damaged.

Two intangibles VW and Mitsubishi Motors and competitors have to face.

  1. The effect on sales over the next couple of years, AND
  2. The effect of tighter regulations on future margins of car manufacturers.

How do VW car owners feel about the option of being offered a buy back or going for the fix and getting paid for it? We do not know if they will prefer getting $5,000 on top of the $1,000 they already have, or returning their car at market value. However, how owners perceive these options (positive or negative) and how VW handles European regulators and customers will affect its reputation.

As far as Volkswagen’s image is concerned? The damage may pass. However, regulator fines, compensating customers and losing sales will continue affecting the firm’s bottom line for a while yet.

Nevertheless, both VW and Mitsubishi have not had their last chance to hurt their reputation. How they handle their respective scandals from here, and whether they reform their corporate culture, will matter. Moreover, the possibility, not yet addressed, of the scale of lawsuits from aggrieved U.S. dealers and individual U.S. states for VW’s possible fraudulent advertising should worry shareholders.

What VW’s #dieselgate and Mitsubishi Motors’ falsified test results says about these companies’ internal procedures and ethics is another chapter in this saga.

3. Have your say – join the conversation

Source: Brand image and reputation: VW pays dearly for #Dieselgate

What is your opinion?

  • What do you advise a company to do when a public relations disaster is in the making?
  • Will people forget Volkswagen #dieselgate as they did the BP Horizon Deepwater disaster?
  • Did UK and French regulators do the right thing, waiting until US regulators set the stage (e.g., how much compensation per car, fines, etc.)?

The author declares that he had no conflict of interest with respect to the content, authorship or publication of this blog entry (i.e. I neither own any of these brands’ products nor are they our clients).

Download PDF file: How to save advertising dollars on Facebook and YouTube.
2015-09-28 Update thanks to Rubén Cuevas,

Fake views of ads by "bots" cost advertisers more than $6.3 billion US globally during 2015.

Data show, video fraud-detection on DailyMotion, vimeo, YouTube and others fails to filter out invalid traffic properly.
 
Here I distill our knowledge into 3 takeaways.

Check out what Sir Martin Sorrell WPP has to say about the matter.
According to Media Rating Council (MRC) and IAB (Interactive Advertising Bureau) standards, a viewable impression of a digital ad occurs when 50 percent of an ad’s pixels are on screen for one second.

In December 2014 Google published data regarding display ads in browsers (desktop and mobile). The study revealed that 56 percent of the display ads it served on its own and others’ sites never appeared within view on someone’s screen.

Nobody really knows for sure how Google or any other video platform or ad server come up with these numbers. For instance, Google provides explanations of what one should look for in these numbers it serves advertisers about their ads. How it collects them is, however, not explained.

1. What is the challenge?

The US Association of National Advertisers (ANA) released a report in December 2014, which estimated that

  • 23 percent of video ads, and
  • 11 percent of display ads

are viewed by “bots”. These are computer programs that mimic the behaviour of an Internet user.

The ANA estimated that this would cost advertisers about $6.3 billion US globally in 2015. This is a concern for two reasons.

1. Adertisers are spending ever larger amounts of money across both display and video advertising (see graphic below), and

[su_custom_gallery source=”media: 2336″ limit=”7″ link=”image” target=”blank” width=”530px” height=”310px”]

2. Spending for video ads is estimated to grow 21.9 percent compound annually from 2015 to 2020 (US data) (see also online video celebrities – chart below).

[su_custom_gallery source=”media: 2335″ limit=”7″ link=”image” target=”blank” width=”530px” height=”284px”]

2. Google and Facebook want a larger slice

Google and its YouTube platform want to garner the largest share possible of this growth in video advertising. Nonetheless, the competition will surely want to prevent this.

In April 2015, Facebook boasted it had over 4 billion video views each day. This number continues to grow.

For now, YouTube data suggest many more videos are viewed daily on its video platform than on Facebook.

For Google, display and video ads create tons of cash for the company, but things are changing. For instance, the rate for pay-per-click ads has been dropping (view chart as shown below). Google explains this was lower rates on YouTube than its other platforms.

[su_custom_gallery source=”media: 2334″ limit=”7″ link=”image” target=”blank” width=”530px” height=”288px”]

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Some suggest that in the US, millenials spend nearly 60 percent of their time watching movies on either a smartphone, tablet or desktop/laptop.

To keep advertisers pouring more money into video ads, however, Google and Facebook have to up their game. Accordingly, both must provide strong evidence that their fraud detection systems work. Until fraud detection works, three things must be addressed as outlined below.

3. Focus on not getting charged for invalid video views

Each video platform wants to charge advertisers for video ads according to whatever the market will bear. In turn, advertisers want to keep costs for ads down, but this is becoming a challenge.

Apparently, some companies offer tens of thousands of YouTube views for as little as $5 US. Such data could in part help explain why 23 percent of video ads are viewed by fake consumers.

Of course, no advertiser wants to pay for these “views”.

How does one avoid paying for fraudulent views?

That is difficult to say, because…

Filtering invalid traffic before advertisers are ever charged is not getting easier.

Recent research sheds light on this important issue. Researchers uploaded two videos to each of five video platforms (YouTube, DailyMotion, Myvideo.de, TV UOL and Vimeo).

They bought ads on these platforms, which targeted the videos they had previously uploaded. Then, they directed their “bots” to these videos.

What are bots?

[su_box title=”EXPLANATION: What are bots?” box_color=”#86bac5″ title_color=”#ffffff”]

Bots are used by DrKPI, Google and Qwant Search to crawl the web.

They are little programs that allow DrKPI  to collect data about blog entries (e.g., text, data of blog entry, etc.).

Google uses bots to index webpages. Bots can also be computer programs that mimic the behaviour of internet users viewing, e.g., a video ad.

About 60% of internet traffic is due to bots.[/su_box]

Each platform’s two videos were visited by the bots about 150 times. The researchers explain in their paper that the bots used were far from sophisticated tools as cyber criminals might use. Nevertheless, the results are worrisome for advertisers.

[su_custom_gallery source=”media: 2324″ limit=”7″ link=”image” target=”blank” width=”530px” height=”319px”]

If detection mechanisms work properly, marketers do not have to pay for ads on YouTube viewed by robots.

Data show that YouTube seems to have the best fraud-detection mechanism of the five platforms tested. It was followed by DailyMotion.

YouTube’s fraud detection tool identified 25 of the 150 bot visits to a video as real users viewing the video.

This means in 16.67 percent of cases, YouTube wrongfully identified a bot or robot to be a human watching a video.

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What is most unsettling, however, is that Google charged the researchers for 90 of the registered fake views. This is a 60.67 percent error rate!

So what is the bigger problem:

1. That YouTube wrongfully identified 25 robot “views” to be humans out of 150 times the video ads were “seen” = 16.67 percent error rate, or

2. Google AdWords, instead of charging for the 16.67 percent of views wrongly identified as humans by YouTube, deciding to charge for 90 views done by robots =  a 60.67 percent error or false positive rate?

How could YouTube’s false positive-rate be so inflated? The process of counting views (i.e. public view counter and number of counted and monetized views) is opaque on YouTube.

Thanks to Rubén Cuevas for pointing out: “YouTube has two different mechanisms in place to discount views for the:

public view counter, and also the
monetised view counter”

Important is here to understand as Rubén pointed out to me, the public view counter seems to be more strict in the detection of fake views.

This is to say YouTube increases the count, and therefore, charges the advertiser for even more fake views than the public view counter would suggest.

[su_custom_gallery source=”media: 2376″ limit=”7″ link=”image” target=”blank” width=”443px” height=”242px”]

Read the research findings in detail:

Marciel, Miriam; Cuevas, Ruben; Banchs, Albert; Gonzales, Roberto; Traverso, Stefano; Ahmed, Mohamed and Azcorra, Arturo (July 2015). Understanding the detection of fake view fraud in Video Content Portals. Retrieved September 23, 2015 from http://arxiv.org/abs/1507.08874

Check out the FT article for non geeks, including comments by the researchers left here:

Cookson, Robert (September 23, 2015). Google charges marketers for ads on YouTube even when viewed by robots. Financial Times, p. 1. Retrieved, September 23, 2015 from http://www.ft.com/intl/cms/s/0/53ac3fd0-604e-11e5-a28b-50226830d644.html

[su_box title=”3 takeaways: Focus on verifiability of video views to fight off deception.” box_color=”#86bac5″ title_color=”#ffffff”]

1. Better process transparency for fraud detection
Having over 15 percent of bot views identified as “real” is a high error rate. While this is bad, YouTube is better than the rest.

YouTube seems to use a sufficiently discriminative fake view detection mechanism, but this applies only to the public view counter.

For the monetized view counter (i.e. those for which advertisers get billed), YouTube seems to ignore this mechanism for discounting fake views (see section 3 above – verifiability).

This is, of course, unacceptable for advertisers. Moreover, it makes the process of how YouTube detects these deceptors totally intransparent for advertisers.

Bottom line: With the help of third party verification, this challenge should be resolved quickly.

2. Improve measurement and use a set of standardized metrics
Even with third parties verifying numbers for advertisers, if our KPIs (key performance indicators) are not comparable we are stuck. For instance, Facebook defines a “view” as someone watching a video for three seconds or more. Others like YouTube talk about around 30 seconds before counting.

These different standards make it difficult for advertisers to get a clear feel and comparable numbers across platforms. Thus, even focusing on numbers, as Google suggests, is of limited value.

Bottom line: Define and agree upon the metrics used by the advertising industry. Make them comparable across social networks and video platforms.

3. Establish third party collecting, verifying and auditing of numbers
Facebook has followed the practice of self-reporting on viewability of ads, pages, reader engagement, and so forth. But as Volskwagen’s #dieselgate shows, self-reporting is always vulnerable to misuse, sloppiness and abuse of the system.

Bottom line: We need third party collecting and verification of numbers. Such efforts must in part focus on minimising charges for advertisers when ads are viewed by robots.

Eliminating fraud in online advertising is key

You are supposed to count the actual number of measured views of a video ad. Ergo, filter out invalid traffic from bots.

In December 2014, the ANA/White Ops study identified 23 percent of video ad impressions as bot fraud. Combine that number with the results from data reported here, and this means:

Google AdWords takes at least 60.67 percent of the 23 percent bot fraud views on YouTube and charges advertisers for them.

Thus, it follows that advertisers pay for at least 14 percent of video ads not viewed by humans!

The lack of transparency, standardized metrics and a regular audit of how video platforms handle fake ad views costs advertisers dearly.

Accurate metrics matter. For the first time ever worldwide mobile advertising will overtake print in 2016 ($71 billion US versus print shrinking to $68 billon US).

As well, social media advertising will top $25 billion US this year. Facebook is expected to take the biggest slice, more than $16 billion US. Instagram will account for “just” $600 million US.

Advertisers are justifiably wary and suspicious. Based on the above predictions, we better make sure that we pay only for those imprints, views, etc., that were executed by humans and not robots. Will #GoogleAW2015 tell us more about how YouTube plans to address this issue? Not really.

Download the checklist as a PDF (320KB file).

Interesting read

a) More content about advertising and viral content
b) Google: hidden ad costs
c) IAB’s efforts to establish a more trustworthy supply chain
d) YouTube frozen views
e) YouTube search for counted views – zero information provided
f) Facebook partners with Moat to verify video ad metrics
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What is your opinion?

Now that you have read “Epic fail: Video view fraud detection“, I would like to ask you a question or two.

– As an advertiser, how do you deal with this issue? Please share!
– What type of video advertising works best for your business?
– What do you know about Facebook’s handling of this challenge?

More about advertising fraud

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Summary: How can David Cameron trust ex-HSBC boss Stephen Green enough to appoint him trade minister?
Why should I trust your recommendation and read this book?

The above are all situations in which we as consumers, co-workers, citizens or purchasing officers have to decide, can I trust this information?
Discover four tips right here, plus see our handy checklist – with slides!
How do you do it? Leave a comment, I am curious to know.

Can I trust you?

Every day we tweet and share information, but how much can we trust these things?

Can you trust Dominique Strauss-Kahn? The French former politician and International Monetary Fund (IMF) chief recently gave evidence at his trial on pimping charges, which threatens to expose his double life. Stating that he rarely attended sex parties because he was “saving the world” and had “other things to do”, he probably knows what it means to lose one’s reputation and wife’s trust and respect.

Dominique-Strauss-Kahn-faces-pimping-charges-in-Lille-court

Once tipped to be the next president of France, Strauss-Kahn does not deny having had group sex. But he refutes the charges of “aggravated pimping”, described as aiding and abetting prostitution. He denies knowing that women at the orgies were prostitutes, which is punishable by up to ten years in prison.

Similarly, the UK government was in possession of detailed evidence about wrongdoing at HSBC’s Swiss bank. That did not stop David Cameron from appointing former HSBC boss Lord Green as trade minister. But Swissleaks and other HSBC leaks haunt the ordained Church of England minister. The alleged money laundering activities happened during his watch (see below).

You are what you tweet

Remember last time you saw a tweet or got a newsletter in your inbox? You shared it on Twitter and Facebook, but did you first ask yourself, “Can I trust the information that I just passed on?” Was the study well done or based on a convenience sample (i.e. we asked our friends and know what people want based on that)?

Remember the last time you saw a blog entry like this:

Top 10 social media agencies in the world

So, the world consists only of the UK and US? I don’t think so!

11 power tips to increase your Facebook engagement

More like power trips, not tips!

These things are often based on people’s personal experience and opinions, but people still eat this stuff up and share it like candy.

Just reflect a moment, would you trust your banker to invest your pension savings based on personal beliefs? I prefer facts. I surely hope that our pension’s fund managers analyze data more carefully than just following such blog entries’ advice.

Remember, if it sounds too good to be true, it probably is.

How to check trust levels for a blogger => DrKPI Trust Index

Garbage in, garbage out? Nope!

Corporate blogs are often simply about the product. In other words, they all just push products, some openly, and others in more subtle ways. The SAS blogs do better than most, that is for sure. For instance, whenever possible the authors refer to other material that is beyond reproach.

Erwan Granger blogged about cloud computing (see below). Smartly, he refers to the definitions of cloud computing as put forward by NIST (Peter Mell and Timothy Grance), a well-respected agency.

SAS-writes-about-cloud-computing-uses-NIST-definition

While the above blog entry tries to push product, it still provides content that is valuable, helping the reader better understand what cloud computing is. By linking to other material it provides me with useful links where I can learn more, thus saving me time. Well done! If you have to push product, this is a good way of doing it (please note, we do not do business with SAS, directly or indirectly).

PS. How independent these SAS users are we do not know. But some do a very nice job blogging !

Curious? Join 1500 other subscribers to this blog’s newsletter and read on!
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Bottom line

There are four things to abide by before you trust your banker, corporate blogger, client or dentist:

1. Take five minutes to check the facts. Before you believe your banker or a tweet you saw, do a check. Is the eMarketer study or Jeff Bullas’ tip based on sound facts and research, or just the author’s gut feelings?

Is the research or opinion good enough to bet your money on? Probably not, unless it is personal investment advice from the Sage of Omaha, Warren Buffet, or maybe well-done survey research from PEW.

eMarketer-cites-study-with-invalid-data-trust-lost-in-their-material

2. How did you come to trust this person? Think about your family physician: they are a non-nonsense person you trust based on their experience. They check a few things to avoid making a hasty, and possibly wrong, diagnosis.

Online content from a corporate blogger is similar. Have you followed them and read their material for some time; is their material trustworthy? Just like building trust, writing great blog content is time consuming and requires more than writing what you believe – show me the numbers.

3. Will your behaviour decrease the other person’s trust in you? Last week you agreed to have dinner with a business partner this Tuesday. Now you have to cancel.

It goes without saying that appointments should be kept whenever possible, but if you must cancel, do it personally and start with an apology. Make sure the other person does not feel offended or let down. Finally, agree on another time and place right then.

Stephen-Green-ex-HSBC-Boss-do-not-preach-just-live-ethically

4. Lead by example and don’t preach, just do it right. Lord Green, the ex-HSBC boss published a book in 2009 (see above image) that preached at leaders to not only behave legally but ethically, going beyond “what you can get away with”.

In 2005, Green became chair of the supervisory company of HSBC’s Geneva unit, HSBC Private Banking Holdings (Suisse) SA. In this capacity, he was responsible for compliance and oversight when the alleged money laundering activity took place at HSBC’s Geneva branch.

The trust people put in you, the reputation you had and how good you made people feel is what you will be remembered by.

Looking at the above suggestions, before you re-tweet something next time, check it first. For instance, can you trust the study’s data? Otherwise follow Harper Lee’s 2007 advice, “Well, it’s better to be silent than to be a fool.

Sounds like a good motto for corporate bloggers: Check and re-check the facts, otherwise do not publish. Especially if somebody wants to quote you, make sure you read the book first before making a fool of yourself.

More Information
Download PDF file with additional graphics and 20 slides (317 kb) – Can I trust my banker, blogger and this book review?

Check out the SLIDES here, they’re definitely worth your time!

[slideshare id=44382283&doc=check-1st-or-loose-reputation-and-trust-150207080226-conversion-gate02]

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You can view more presentation slides from DrKPI here.

What is your opinion?

We address 3 questions: 1. What data do we really need answers for? 2. Why is a sound methodology critical? 3. Do metrics that focus on small but useful improvements make sense?

With business analytics, the toughest challenge is collecting data needed for questions one needs answered. My emphasis here is on:

– must-have answers, not – desired answers!

This is the third post in a series of entries about big data. Others so far are:

– Facebook mood study: Why we should be worried!
– Secrets of analytics 1: UPS or Apple?

New technqiues will not do

Often, we focus on predicting or forecasting the future. However, in management it is more important to understand the analytic HOWs and WHYs. These matter more than the promise of prediction. In the past we did not call things predictive analytics but forecasts instead. We used

– time series, as economists still often do, and – tried our luck with multivariate analysis (both part of what is called parametric statistics). These days, we still use the above methods. However, new ones have come to the fore, such as:

– k-means clusters, and – random graphs.

2014-09-08-EPFL-evolution-of-random-graph-Erdoes-Renyi

A random graph is obtained by randomly sampling from a collection of graphs.

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KEY INSIGHTS
Why little data mean a lot: Incremental innovation is key.
Google Trends shows a spike in searches – iPhone6: Remember the flu trends? Increased searches do not make something a fact…
Constant experimentation and rapid implementation: Strive for lots of small and frequent advances, because that is good enough.

We address three questions

1. What does it mean when Google Trends shows a spike in searches?
2. Should we aim for lots of small wins from ‘big data’ that add up to something big?
3. Do metrics that focus on small but useful improvements make sense?

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Subscribe to our award-winning blog: DrKPI – the trend blog

CLICK - Caution - things may not be as they appear. Check the methods.

1. ‘iPhone slow’ and Google Trends

There are three types of business analytics:

Descriptive analytics that look at historical data,
Predictive Analytics that try to determine what might happen, and
Prescriptive Analytics that focus on giving us different options, in which case we choose what we think suits us best, given time and money constraints.

The question remains whether we have the right data… To illustrate this challenge, we can look at the Google Flu Trends (GFT). Using search results from Google, the GFT supposedly indicates how the flu spreads and affects people in various countries.

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CLICK - Facebook Likes tell a lot about you, such as if you drink beer, have sex regularly and are happy.

Facebook engaged in a large study to see if users’ emotional states could be affected by their news feed content.
Consent of Human Subjects: Subjects not asked for permission first.
Findings: Extremely small effects.
Research methodology: Poor algorithms used, questionable findings.

Key finding: A reduction in negative content in a person’s newsfeed on Facebook increased positive content in users’ posting behavior by about 1/15 of one percent!

We address 3 questions

1. Why did some of the checks and balances possibly fail?
2. Should we worry about the study’s findings?
3. What benefits do Facebook users get out of this study?

Non-techie description of study: News feed: ‘Emotional contagion’ sweeps Facebook

1. Some checks and balances failed

Following the spirit as well as the letter of the law is the key to successful compliance. In turn, any governance depends upon the participants doing their job thoroughly and carefully.

In this case, the academics thought this was an important subject that could be nicely studied with Facebook users. They may not have considered how much it might upset users and the media.

Cornell University has a its procedure in place for getting approval for research with human subjects. As the image below illustrates, the researcher is expected to reflect on the project and if in doubt, ask for help.

CLICK - Why does the media not get the facts right about the Facebook study? #BigData

The university points out that it did not review the study. Specifically, it did not check whether it met university guidelines for doing research with human subjects. The reasons given were that its staff:

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