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  • Daisy Powell-Chandler

Why everybody hates you: Data – part 1 of 2

Reputation is formed from three main components: your own behaviour, the manner in which the behaviour is communicated, and the context in which that behaviour takes place. The first of these you can change, the second you can influence but you need to understand the third – context – in order to do either of the others well. Many of the issues that affect your reputation are exceedingly complex. Race, gender, inequality, climate change – how can you possibly be expert enough in each of these areas and more in order to stay ahead of your critics and be (seen as) a good company?


This essay is part of a series that explains the context in which your corporate reputation is being formed, so that you can guide your own company safely through. Today we’re looking at data use, misuse and abuse. Data makes our lives easier and cheaper, and has the potential to make customer service exquisite. But what are the trade-offs for you and your customers?


The deluge

One of the less entertaining aspects of the Attention Economy is the spam. Each day the average office worker receives well over 100 emails – to their work address. Now consider any personal email addresses. Plus the phone calls, unsolicited post, the internet pop-ups and even approaches at the front door. So many of those are irrelevant or spam that we only open around a third of the email we receive and we act upon a tiny fraction of that. Telephone pollsters find that they now have to make 40 phone calls for every one person who will participate.

Image by cattu from Pixabay

This constant contact is driven by data: data that we knowingly give away, that streams off us unwittingly, or that is left behind as ‘digital exhaust’. What do I mean when I talk about data? Data is information of any kind – and if it can be linked back to you (via your name, a post code or another identifier) then it is personal data. Companies gather data in three ways:

  1. Asking consumers directly. For example, a sign-up form, poll or feedback questionnaire.

  2. Indirect data gathering. For example, click-through rates from particular emails or webpages, CCTV footage of shoppers in supermarkets, loyalty card data.

  3. Appending data from elsewhere. This might be bought from a data broker or direct from a source such as a magazine publisher or club, or it could be created using a fragment of the company’s existing knowledge. For example, using the post code to calculate distance to nearest railway station or average house price.

A dataset that brings together these three components is more than the sum of its parts. Take the example of a supermarket shopper who signs up for a loyalty card. There is a short form that registers his date of birth and address, as well as name and email address. Already the store can profile his age, likely income, ethnicity and political views (as well as a whole host of other demographics). Next they gather information about his shopping habits and, once he has logged into their website, cookies may continue to track his behaviour online. If they choose to, the analysts could then buy in numerous datasets that will tell them about his membership of particular clubs or products he has bought previously. All of this helps to tailor their picture of the customer and the advertising that they send him as a response. This data will then fuel decisions about everything from which vegetables to stock to which celebrity spokesperson they choose, or even which charity to sponsor at Christmas.


This snapshot gives us a glimpse of a new self-propelling cycle. As a larger proportion of our lives is either lived online or governed by apps and programs that do, we leave behind a higher volume of data in our wake. A new generation of companies offer us online services ‘for free’ in exchange for our data and these utilities or games make it easier and cheaper to move more of our life online. Simultaneously, the rise in behavioural science has given the same companies the tools to predict future behaviour from past data. The myriad uses of your data have in turn made it incredibly valuable – thus increasing the motivation of companies to gather it – and in the unregulated environment of the internet, companies such as Google and Facebook led the way in proclaiming our “data exhaust” to be their property, their “behavioural surplus”. This set a precedent which thrives to this day.

In her book, The Age of Surveillance Capitalism, academic Shoshana Zuboff makes several compelling and slightly disturbing points about the resulting system of economic incentives surrounding our data. First, it makes the link between consumers and companies less important. Many of the organisations gathering your data don’t even have to sell you anything. This gives them fewer reasons to keep you happy. Second, the companies that control these vast swathes of behavioural data employ a remarkably small number of people Power is concentrated in the hands of very few. And, perhaps inevitably given the first two points, Zuboff makes the case that “surveillance capitalism depends upon undermining individual self-determination, autonomy and decision rights for the sake of an unobstructed flow of behavioural data to feed markets that are about us but not for us.”


This, then, is the first challenge for corporate reputation in the age of data: whatever the original motivation behind gathering data, organisations that gather substantial data caches find themselves part of this asymmetric relationship where consumers can start to matter less than the data they emit.


We love data!

That all sounds a bit scary – surely it isn’t all bad? Not at all. There are great things about pervasive data – good use of data can save us all time and money. Some applications may seem a tad trivial but others are life-changing:


Amazon now prefers that you speak to a chatbot rather than calling a helpline, but they make that process more convenient by super-charging the bot with your data. Have you just looked at the order for an item that never arrived? The bot sees your browsing history and immediately asks if you have a query about that order. In a few clicks the computer has checked the order status and, if need be, dispatched a replacement to your home address (it knows this too).


Just in time supply chains – often driven by real-time data and AI – save money, reduce emissions from unnecessary transport and cut waste. But what about just in time services? British Gas are already envisaging a day when as well as the thermostat that keeps your house cosy, they will also have sensors inside your boiler that give them advance warning of a potential failure. Instead of you calling them and having to take an unexpected day off work because your house is freezing, they will be able to send you an alert and book in a convenient time for an engineer to check the device before it fails.


Staying on power: GE were able to increase the power output of windfarms by 10% by bringing together the data from multiple different power generating companies and using it to calculate the best angle at which to position the turbines. Using similar techniques to tweak gas turbines and jet engines they believe they can improve US productivity by 1.5% over a 20 year period leading to a 30% increase in incomes.


Big data is also fuelling new discoveries in the health sector. For example, Linguamatics is using the untapped data held in medical records to find new avenues of research. Until now it has been hard to use the parts of records that are written ‘long hand’ unless someone reads each record and interprets the information into ‘codes’ that a computer can process. This company uses natural language programming to search records for trends such as symptoms that frequently occur together, or side-effects that may previously not have been documented. This could literally save lives – but does involve giving more people access to your medical records.


These examples show that gathering warehouses full of data and using it to predict human and non-human behaviour can create both small and far-reaching benefits that will allow us to live happier, less-encumbered lives.


GDPR & transparency

For those of us interested in reputation and in doing the right thing, the question then is: how do we deliver these benefits and stay on the right side of the data debate?


Interestingly, the EU has got our back (and probably the UK and California too, but let’s keep things simple for the time being). If your company collects or holds any type of personal data in the EU then you should have heard of the General Data Protection Regulation and you have probably be trained to think of it as a major pain in the ass: suddenly you can’t use that list of email addresses you had before, or maybe you can but they need to be saved in a different place, or you have to ask the list if they still want your emails.

Yes, GDPR may be a bit of a time-suck for you right now, but in the context of your corporate reputation, GDPR is your friend. Not only can compliance save you a hefty fine and poor press coverage, it is also trying to guide you towards a relationship with customers that is built on consent and engagement. The principles of the law say that you should: collect the minimum amount of accurate data required, use it in a legal manner for the purpose you told subjects you were gathering it, not lose the data and then delete it when you are finished.

Image credit: www.hubilo.com

All of these things also help you. The GDPR methodology is remarkably aligned to corporate interests AND consumer needs and though it may primarily be about data use, it is clear how much interplay there is between this subject and the attention economy. Let’s start with transparency. One of the key tenets of GDPR is that is should be clear to ‘data subjects’ what you are going to do with the data you gather and from my experience this is the principle that companies struggle with most. It goes entirely against the grain to explain why you want to know certain information and if you think about it this is really odd. If someone in the street asked you for your personal details, you would want a justification, wouldn’t you?


I believe that there are two primary reasons why corporations struggle with the transparency criterion. First, we have been taught that having more data is implicitly a good thing. And that means sometimes we are gathering data ‘just because it would be nice to have’ and not for any strongly defined reason (more on this in a moment). Then when we are challenged to justify our need for those data points it is easy to feel defensive. The second main reason we don’t like transparency is, if we are completely honest, because we don’t think consumers will like what we are doing with their data. This is the nub of the problem. Transparency is a great form of corporate reputation insurance not only because you are being compliant and doing the right thing according to GDPR but also because it creates an important prompt to introspection by your team. If you aren’t 100% happy to tell people what you will do with the data, maybe you shouldn’t be doing it.


The dark side of data

This is where we come to the second major challenge for data and corporate reputation. For every algorithm being used by Netflix to more carefully craft content that makes you laugh, there is another doing darker deeds - and we can’t talk about that without mentioning Cambridge Analytica. You would have to been hiding under a rock not to have heard this name over the past three years. Cambridge Analytica were briefly the poster child of the analytics-fuelled campaigning world, the next evolution of techniques born in the much-lauded Obama campaigns. I was a pollster during the Brexit and Trump elections and there was a lot of buzz about the firm and their approach. It sounded sexy (in a nerdy kind of way) and it sounded like it worked. Then it all turned sour, Cambridge Analytica found themselves the focus of multiple legal cases and government inquiries and filed for bankruptcy. Sustained hard work by a bevvy of committed journalists has uncovered a tale that goes a bit like this:


SCL were a defence contractor that sold ‘psy ops’ services to the UK and US armed forces. Psy ops – psychological warfare – is the use of propaganda to defeat the enemy. SCL, however, had a rather fluid definition of what constituted the enemy and therefore decided to set up ‘SCL elections’. This was eventually moved into a separate firm – Cambridge Analytica, run by Alexander Nix. Nix claimed to have worked on “40 political campaigns in the US, Caribbean, South America, Europe, Africa and Asia” including a pretty unsavoury operation in Trinidad and Tobago’s 2010 election that created a false grassroots campaign intended to suppress turnout among young black voters by convincing them that choosing not to vote was an act of defiance.

Do So! campaign described in a Cambridge Analytica sales presentation

What finally landed them in hot water (other than an apparently compulsive need to tell undercover reporters about their shadiest tactics) was their use of millions of pieces of illegally scraped Facebook data to inform a massive propaganda campaign against Hillary Clinton. In essence, they used what they knew about the personalities and preferences of Americans to write propaganda that they knew would change their minds. The company’s managing director, Mark Turnbull said: “We just put information into the bloodstream of the internet, and then, and then watch it grow, give it a little push every now and again … like a remote control. It has to happen without anyone thinking, ‘that’s propaganda’, because the moment you think ‘that’s propaganda’, the next question is, ‘who’s put that out?’”


In the case of Cambridge Analytica’s activities in the US, there are evidently legal irregularities. The people whose data they used did not understand how their information would be used. But where do we draw the line? Had Cambridge Analytica jumped through all of the necessary legal hoops, would we consider their actions legitimate? To many Trump supporters, it might seem that pushing out facts about supposed Clinton wrongdoing was an acceptable campaign tool – possibly even a public service.


For decades, political parties have tailored their communications for specific audiences: tweaking the stump speech depending on the venue, playing upon local sensitivities in a leaflet for one constituency, or creating different versions of a letter campaign for people who have stated different political interests. Where does the boundary lie between exceedingly efficient data use to target an effective campaign, and interfering with democracy? It is fairly likely, for example, that the work that Cambridge Analytica did in Trinidad & Tobago was legal in that country but it remains morally abhorrent. This is the reason why you can’t just ask your teams to abide by the law and wash your hands of the problem.


What to do about it? Three key points to consider


Next week, I’ll talk more about whether it is possible to have too much data, and whether data is morally neutral. I will also touch on the politics of algorithms and how to handle a data breach. To make sure you don’t miss part two subscribe here for weekly updates. For the moment here are my key takeaways:

  • Question your right to both gather and use consumers’ data. Is it really yours?

  • Being legal is not enough. Most data questions have a moral element that goes beyond what your lawyer advises.

  • Treat GDPR as an opportunity to ‘sniff test’ your own activities. If you worry about how to explain it to your customers, should you be doing it?


There is much, much more to this conversation and I would love to hear from you. What have you read that cast a light on this? How have you tackled this problem in your organisation? Get in touch.


Want to catch up with the rest of this series on Why everybody hates you? Find the earlier essays here.

Copyright Meyland Strategy Ltd 2020