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Confidential Facebook emails suggest uncertain data exchanges with Netflix and Tinder



Internal, and confidential, Facebook documents containing revelations around how the business agreed to hand over data to household brands despite it being unclear whether there was any user consent to do so have been published by the UK government.

As part of an ongoing inquiry into fake news and the fallout from the Cambridge Analytica scandal, the department for culture, media and sport (DCMS) has shared excerpts from sensitive exchanges between Facebook execs and companies like Netflix, Airbnb, Lyft and dating apps Tinder and Badoo.

Around 250 pages have been published, some of which are marked as being 'highly confidential'. Facebook has opposed their publication, saying they were "only part of the story" and were presented in a way that was "very misleading".

The documents appear to show that after announcing plans in 2014 to shut down a tool that allowed apps to access the data of users' Facebook friends (who hadn't necessarily given permission), Facebook continued to 'whitelist' certain companies for a similar privilege on a case-for-case basis.

The data mined by Cambridge Analytica, gathered by way of a political quiz in 2014, exposed a loophole this API for political gain, then sold it (against Facebook's policy).

According to DCMS chair Damian Collins, Facebook "clearly entered into whitelisting agreements with certain companies" which meant after the platform changes in 2014/15 these companies maintained full access to friends' data.

He added: "It is not clear that there was any user consent for this, nor how Facebook decided which companies should be whitelisted or not."

The fact the likes of Netflix and Tinder were offered a way to potentially still access data belonging to users' friends list after Facebook promised to clamp down on the practice is likely to prompt further questions from authorities and users around Facebook's commitment to user privacy.

The Drum has reached out to Netflix, Tinder and Lyft for comment.

What else do the documents reveal?

The documents under scrutiny were gathered by Six4Three, a startup tech firm which is embroiled in a lawsuit against Facebook. They were turned over to Westminster last week and Collins said they have been released "in the public interest".

Along with claims Facebook allowed some firms to maintain "full access" to users' friends data, Collins also noted that:

  • Execs, including Marc Zuckerberg, discussed the idea of linking access to friends data to the financial value of the developers relationship with Facebook

  • Facebook had been aware that an update to its Android app that let it collect records of users' calls and texts would be controversial. "To mitigate any bad PR, Facebook planned to make it as hard as possible for users to know that this was one of the underlying features," Collins wrote

  • Facebook used data provided by the Israeli analytics firm Onavo to determine which other mobile apps were being downloaded and used by the public. It then used this information to decide which apps to acquire or treat as a competitor

  • The files showed evidence of Facebook taking "aggressive positions" against rival apps, denying them access to data that caused their businesses to fail. Such examples included an engineer suggesting Faceboook shutting down Twitter-owned Vine's access to friends data, to which Zuckerberg replied "yup, go for it"

A Facebook spokesperson said: “As we've said many times, the documents Six4Three gathered for their baseless case are only part of the story and are presented in a way that is very misleading without additional context.

"We stand by the platform changes we made in 2015 to stop a person from sharing their friends' data with developers.

"Like any business, we had many of internal conversations about the various ways we could build a sustainable business model for our platform. But the facts are clear: we've never sold people’s data."

You can read excepts from Facebook documents published by DCMS below:

Exhibit 84 – whitelisting of Badoo [dating app]

Email: from Badoo to Konstantinos Papamiltidas (director of platform partnerships at Facebook)
16 September 2014:
'We have been compelled to write to you to explain the hugely detrimental effect that removing friend permissions will cause to our hugely popular (and profitable) applications Badoo and Hot or Not. ‘The friends data we receive from users is integral to our product (and indeed a key reason for building Facebook verification into our apps).’

Email: from Konstantinos Papamiltidas to Badoo,
23 January 2015
‘We have now approval from our internal stakeholders to move ahead with a new API – working name Hashed Anon All Friends API. The new API as well as the relevant docs will be ready next week. ‘How would this API work…For each of the FB logged in users, the API will return:
FBIDs: App friends that logged in before your migration to V2: App Scoped IDs: App friends that logged in after your migration to V2: Annonymous one-way hashed IDs: Non-app friends The API will hopefully let you understand some of the structure of the graph in order to determine which non-app friends to recommend to a given user.’

Email 5 February 2015: from Konstantinos Papamiltidas
‘We have whitelisted Badoo App, HotorNot and Bumble for the Hashed Friends API that was shipped late last night.’

Email 6 February 2015: From Konstantinos Papamiltidas to Badoo
‘Badoo APP ID has definitely been whitelisted…According to out logs you have already made 100 calls against this API.’

Exhibit 87 – whitelisting of Lyft [taxi app]
Email: from Konstantinos Papamiltidas to Lyft, 30 March 2015
‘As far as I can tell, the App ID below has been whitelisted for All Mutual Friends access.’

Exhibit 91 – whitelisting of Airbnb
Email: Konstantinos Papamiltidas (Facebook's director of developer platforms and programs) to Airbnb
18 March 2015
As promised, please find attached the docs for Hashed Friends API that can be used for social ranking. Let us know if this would be of interest to you, as we will need to sign an agreement that would allow you access to this API.’

Exhibit 92 – whitelisting of Netflix
Email: Netflix and Chris Barbour and Papamiltidas at Facebook
17 February 2015
Netflix wrote on 13 February ‘We will be whitelisted for getting all friends, not just connected friends’

Exhibit 97 – discussion about giving Tinder full friends data access in return for use of the term ‘Moments’ by Facebook
Email: discussion between Konstantinos Papamiltiadis and Tinder regarding allowing Facebook to use ‘Moments’, a term that had already been protected by Tinder Email from Konstantinos
Papamiltidas to Tinder
11 March 2015
‘I was not sure there was not a question about compensation, apologies; in my mind we have been working collaboratively with [name redacted] and the team in good faith for the past 16 or so months.

He’s a member of a trusted group of advisers for our platform (Developer Advisory Board) and based on our commitment to provide a great and safe experience for the Tinder users, we have developed two new APIs that effectively allow Tinder to maintain parity of the product in the new API world.’

Email: from Konstantinos Papamiltidas
Tinder 12 March 2015
‘We have been working with [name redacted] and his team in true partnership spirit all this time, delivering value that we think is far greater than this trademark.’

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Marketers need to be polymaths: the modern-day marketing challenges



The marketing industry is having change thrust upon it, with the capabilities of data and technology upending traditional roles and career paths. For chief marketing officers (CMO) and marketing leaders, whose job it is to make sense of that rapidly changing space, the price of innovation is constant attention and readjustments.

The Drum and Domo invited a table of brand marketers gathered to discuss what keeps marketers up at night and what solutions are available: Anand Narayanun of Panasonic, Monique Aziz of Universal Music, Tom Stancliffe of Tribe, Debarshi Pandit of Sky, Sam Lloyd of Travis Perkins and Mark Johnston of Domo shared insights over coffee and breakfast on winters’ day in the cosy Merchant’s Tavern in London.

What keeps marketers up at night?

The great hope for marketing in 2018 and into 2019 is personalisation. Anand Narayanan, head of digital for Panasonic believes that modern data capture and analysis tools enable marketers to be creative around much more targeted groups:

“If you have a better understanding of who your customer is, what their motivations are, and what will they care about, you can be more creative in delivering that surprise and delight to them, as opposed to coming up with an idea that’s creative [but] for everybody. It's insight as opposed to data. As long as you have impartial insights, that is where the real value lies, in enhancing creativity.”

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His advice speaks to the ongoing trend towards targeted campaigns and advertisements that focus on a subset of the total audience rather than a mass, anonymised group. Tom Stancliffe, co-founder of natural sports nutrition products Tribe, argues that for challenger brands and start-ups, “targeting with insight” can lead to more tangible results and ultimately growth around super-engaged consumers.

Data and audiences

But when customer experience is the new brand and winning requires data, and more of it, how do marketers balance data and technology as a tool for creativity in equal balance to measurement and performance?

The marketers around the table agreed that they were all wary of embracing data for its own sake, arguing that without insight data has the capability to be misused. Debarshi Pandit, head of special projects at Sky advocates for a recognition that while data is useful, it cannot quantify everything about how audiences interact with and relate to brands.

“Digital is very measurable, everything we do in digital is supposed to have a numerical value. You can't do that with a brand. There are things like net promoter score, which, if it is your KPI, is your measure. Delivering that is something that isn't in my advertising campaign or my influencer campaign. At the end of they day, that is the brand experience,” he explained.

Mark Johnston, marketing director at Domo, admitted to a certain envy for sales teams, whose KPIs have not been shifted by the rise of digital as marketing has: “It seems to me that it's very easy to look inwards at marketing and historically it would have been that inward-looking thing of 'we own or we control'. Then this digital thing came along and people become more focused on the added complexity of digital, whether it's the mediums or the channels, where the technology inside of a marketing organisation, analytics and insight now being a core function of every large marketing organisation.”

However, the group also noted that ‘digital’ is necessarily changing the expertise of the people making key marketing decisions. Where once creativity was the be all and end all for rising to the CMO position, now skills that were once considered the domain of tech and finance teams are taking priority. Narayanan believes that it is the responsibility of a modern CMO “to be more financially savvy”, to aid in demonstrating ROI around campaigns to the rest of the organisation.

Marketers as polymaths

As much as the group argued for changes that are responses to changing consumer habits and the rise of digital, they also advocated for internal changes within marketing companies. Especially when trying to keep up with constant innovation and layers of complexity at organisations.

Sam Lloyd, head of science and data analytics at Travis Perkins believes that increasing the permeability of walls between divisions and silos within companies should be a priority for marketers in 2019: “For me it's about being a maven, someone who can connect various parts of the business, really just to break down silos.” In order to do so, he argued, CMOs need to be “polymaths”.

Pandit agreed, noting that the UK marketing industry has traditionally relied upon specialists, whereas what is required today is generalists who can connect each part within a business.

The changing skillset – and the ephemeral nature of digital advertising – requires that marketers feel empowered to make decisions much more swiftly than has historically been the case. For Monique Aziz, the UK marketing manager of Universal Production Music, “It's good to have a fast response. Sometimes it's difficult. You need to talk to so many people along the way – social is too fast and if you don't do it at the right time that's the moment [gone]. I'm really close with sales, with technology [so] if I make a choice I'm confident to do it… you need to build that relationship.”

Monique Aziz


Despite those upheavals, the group was keen to point out that certain fundamentals of marketing have not changed, and that the evergreen problems that have kept CMOs up at night for decades haven’t disappeared.

Narayanan referred to the consumer marketing dilemma that you can only sell the features of a new product once, so finding new ways of selling it to an audience is paramount. He noted that “there's only so much you can say, whether that's about TV, camera, Blu-Ray, take your pick… unless you have stories that connect with your audience there is nothing to say.”

The rise of digital has created new reasons for CMOs to lie awake worrying at night. From the speed of change and the need for swift deployment of messages to the change in culture within marketing companies, there’s a lot more to consider.

Despite that the panel was optimistic that the CMO role in particular prepares people to adjust to those changes and deliver upon the promise of marketing in 2019.

Find more insights on The CMO Challenge with Domo here.

All copyrights for this article are reserved to their respective authors.

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Data & Analytics

2019: Data will be the new IP



Research from Dresner Advisory Services reveals that 53 per cent of organisations are using predictive analytics to help them enhance marketing communications, reduce risk, detect fraud and optimise operations. Airlines use it to set ticket prices, insurers use it to calculate the likelihood of water ingress, credit providers use it to determine spend limits, banks use it to identify account take overs, retailers use it to predict next likely purchase and so on. Clearly predictive modelling is an incredibly useful tool to determine future behaviour.

There are two types of predictive (or supervised) modelling, classification and regression. Fundamentally, classification is about predicting a label and regression is about predicting a quantity. So classification models calculate class membership e.g. determining whether a customer is likely to leave, how someone might respond to an offer or what someone might want to buy next. Whilst regression models predict numbers such as lifetime value of a customer or how many months it will take to acquire a certain amount of customers.

The most widely used predictive modelling techniques include decision trees, regression analysis, neural networks, bayesian analysis and KNN (K nearest neighbour). However, there are many more. What is important to understand about all these techniques, is the fact that today it is possible to download open source algorithms and packages such as keras, scikit-learn, KNIME, Orange : or buy them in from commercial sources such as SPSS Modeler or KXEN. What this means is that ultimately the building block architecture of most predictive models used commercially or otherwise is the same. Therefore what separates one from another is the data that trains it.

Training data is literally just that. It is the data that data scientists use to ‘teach’ the model so that the predictions it makes are as accurate as possible. Training a model is no different to teaching a child. If you keep showing a child a ball and say ‘ball’ eventually the child will understand that the spherical object is a ball. The same is true for a model – only they have a much greater capacity for learning than humans.  So it is possible to model algorithms to find relationships, detect patterns, understand complex problems and make decisions all at speed. Eventually, the quality, variety, and quantity of the training data will determine the success of the predictive model. To do this training data has to be correctly labelled and classified so that the model can learn from it. To make more accurate predictions the more training data there is available the better. This is why organisations such as Google and Amazon have such a great competitive advantage – because they have so much quality data they can use to train their algorithms. They are walled gardens and have never let their valuable data leave their ecosystems or be shared with competitors. The selection and labelling of training data tends to be a long and laborious process. Each piece of data has to be checked that it is relevant, up-to-date and annotated correctly. The more effort that is put in at this stage the more accurate the predictions will be at the end. As with all data-based applications – rubbish in, rubbish out.

It has already been proved that organisations that use predictive models are more successful that those that don’t. However, as increasing number of organisations turn to predictive modelling it is clear that competitive advantage will lie in training data, not the model itself.


Emma Duckworth, Data Scientist, Outra


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Data & Analytics

D4t4 Solutions plc Continues Global Expansion with New US Leadership



Analytics Industry Expert Bill Bruno Announced as VP, North AmericaTuesday 11th December 2018, Sunbury-on-Thames, UK: D4t4 Solutions plc has announced new US leadership to drive continued growth in one of its key global markets. Bill Bruno, an experienced executive in the media and analytics industries, has joined the company to accelerate the opportunity that exists in the US for D4t4’s unique range of solutions for customer data capture and enterprise managed services.Bruno joins D4t4 from…

Source: RealWire

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