Category Archives: Analytics

GDPR, the best thing since spliced breadcrumb trails

Every site is ‘thanking us for our data choices’ but are we really taking the time to understand what our choices are? We all have a data footprint but to what extent we may not be entirely aware.

Since the beginning of the transactional web these footprints have been collected, stored, connected and maybe now and again actually used. I say now and again, because most businesses I come across haven’t really worked out the value of data to inform anything beyond their CRM program. It’s often left to one side when it comes to shaping a business model, design approach or even NPD. More often, instead the point of view comes from inside the boardroom or with an eye to the competition and what they’re doing.

But that time has passed, relevance centred businesses are servicing an ‘Age of You’ – the internet era that goes beyond eco-systems and leverages insight to inform purchase journeys and their wider experiences around a users actual needs and desires.

In this web 4.0 world where the IoT is starting to pivot around the individual not the brand, GDPR has come into force in order to harmonise data laws whilst protecting consumers who don’t quite know what the data cost of all this connectivity is. And it’s putting emphasis on businesses to be held more accountable. HOORAY.

The age of ‘my data, my internet’ is on the horizon and the exponential rate that technology will advance this far outpaces nearly every legacy data lakes in place today. So what to do? Now that is a question I’m getting asked in an equally exponentially increasing rate.

I have some simple starting questions that have helped me shape some of the data strategies I’ve been working on with clients embracing GDPR as a chance to positively shake themselves up (Chapeaux). These are just starting points and they will open up more questions but I have found if you keep coming back to them every time you disappear down rabbit hole, they help.

First things first, there are three main types of data:

1st party; the stuff you collect directly and that you ask for the permissions to own

2nd party; essentially someone else’s 1st that they share with you (normally advertisers and publishers)

3rd party; the kind of stuff you can buy from anywhere and is generally diluted and generalised (i.e. not very useful to anyone so I’m not going to cover this)

Your starting point is likely to be a ton of archaic stuff that’s been collected for years, decades even, and not really modernised. Or if it has been modernised it will have been done so through a brand or business lens therefore adding to it’s linearity.

You don’t need to chuck it all out though, where there is data there is insight you just need to know how to mine for it, so my first question: What can this existing pile of data tell you?

There will be many assumptions, heed caution. If you don’t believe the assumptions (and trust your gut on this one) get a data wizard (some call them scientists) to mine it for you. They will be able to develop a question set with you then deploy speedy algorithms and methodologies to offer up a different set of useful insights.

Once you know what your data knows, you’ll have some gaps against your objectives which leads to the next bit…

It’s likely you’re working for or with a brand or business who think they need to own all the data. You don’t. In fact it’s quite greedy to assume you should. I’m not saying a big bank of addresses is all redundant (do not underestimate the power of email) BUT 2nd party data can be a super useful shortcut to getting to know the answers to the gaps that the data you already have doesn’t give you right now.

Google for example, know quite a bit about most audiences you are likely to be trying to reach and engage. “Google conquered the advertising world with nothing more than applied mathematics. It didn’t pretend to know anything about the culture and conventions of advertising — it just assumed that better data, with better analytical tools, would win the day.”

And Google was right.

I didn’t say that by the way, Wired’s Chris Anderson did a little while back. I totally agree – as does most of the internet.

So, question number two: Who are your trusted 2nd party data partners?

Your lead agencies should have a good view on this, but you will too. Within your organisation you will have worked with media and publishing partners on initiatives and activations, plus a whole host of other partnerships will have proved useful along the way. Look at what’s worked and bring them into the fold then widen your horizons to the likes of Google. Once you a clear view you can work out how you’ll use each one to plug your 1st party gaps. Make two tidy lists; one for 1st party and one for 2nd party, then put them to one side for now.

The next bit is more tricky, and that’s working out a data roadmap to get you over your immediate hurdles and propel you into a consumer centric model so you can effectively operate in the ‘Age of You’. So, question number three: How are you going to map and further extend your two data sets to give you the answers you need, now and for tomorrow?

Using data to; inform the creative process, brand storytelling or simply just for personalised targeting and messaging requires using data to generate a contextual, or even better, an emotional connection. But there is a line, and this is where GDPR is reinforcing the interests of consumers. Balancing the digital data economy, with commercial opportunities and consumer rights is a minefield unless you truly start thinking consumer first. Your data map should flip every question you’ve asked yourself as a business or brand thus far to be just this, so instead of ‘data will help us do X and Y’ instead ask yourself ‘by knowing this piece of information about our consumer we can help them do X and Y’.

Once you’ve built out your consumer maps based on what (1st and 2nd party data points) you need to know in order to deliver on their needs and desires, you’ll be in a good place to start mapping your own goals to them, but another watch out – never reverse them or you’ll be right back to where you started in no time.

The GDPR applies to all businesses that are established in the EU, regardless of whether the data processing takes place in the EU or not. And if you think you have a loop hole, even non-EU established businesses will be subject to GDPR if your business speaks to consumers in the EU. You can’t stick your head in the sand over this one and the world isn’t go to wait for you to figure it out, so best to get cracking.

Bottom line? You need to know what your data knows, work out what you don’t understand and shift to a consumer first approach.

GDPR data post

Image found on Google courtesy of gigaom – thank you

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3 Steps to adopting AI

I’ve being asked for my view on AI a lot this year, more so than last. It would appear the industry has caught up with the ‘hype’ being a reality.

I’m not getting the slightly twitchy ’Is it going to steal my job?’ anymore though, now the questions are; Is this something I need to be bothered about?, Can I afford it?, Where do I start?

All good questions. If you’re a brand or business looking to get ahead, simply keep up, or maybe even catch up, you can’t afford not to be thinking about this and getting a beta into place. If you don’t your competition will, and then you will be sat watching them eat your share of market or voice, or both. Either way, I can’t imagine that’s something you’re keen to see happen.

Roughly a third of the brands I work across either have a version of an AI (artificial Intelligence) ready IA (Intelligent Assistant) or have jumped straight into an AI trial or beta. Every single one of them has seen positive results. Every single one of them is now developing a roadmap with us to put in place milestones to be better, faster and more informed on a real time basis.

The shape of these solutions vary from bettering service response levels to informing fashion design and everything in-between. But the steps to get there are the same, and here they are;

Work out what the problem is you want to solve

Ok, obvious right? But actually I highlight this because I recommend you don’t ‘do a chatbot’ because your competitor did.

Is there a challenge that advertising or marketing isn’t fixing for you right now? Do you have a human centered design idea that you can’t quite get to grips with? Do you have micro communities you don’t understand or can’t reach in meaningful ways?

All of these are problems AI can help you with, quickly and effectively. So consider where you might want to turbo charge a solution and put a brief together around that.  Be clear about your brief as well, if you’re vague about what you want to achieve it’s tricky to train an AI to think comprehensively, it in turn will be vague.

Review and understand ALL of your relevant data

AI is only ever as good as the data you feed it; the more data you have, the more connections can be compiled and the faster it will evaluate and learn. It’s not magic, it’s algorithm on speed. 

Define the goals you want to achieve in order to reach the objective in your brief, or work with an AI data partner to do this (most good agencies should have someone who can help you get started and then find the right partner for you, the answer isn’t always ‘Watson’ btw). You will likely have a mass of data you understand and a bank of data you’ve never really thought about, once you have it all in one place you need to work out where the gaps are and fill them in.

This up front bit seems tedious, that’s because it is. But don’t cut corners as you’ll only pay for it further down the line. The better the data set, the more robust your AI solution will be and the quicker you will see results.

Choose your AI partner

What you want your AI to do will depend on what supplier or partner you choose. There are many solutions already available at both scale up and enterprise level to choose from. They offer everything from; language skills, analytics, tech stacks that speed up services, listening, finding ‘moments of serendipity’ through to predictive analytics and forecasting.

A read of IBM Watson and AWS are good places to start if you want to dig more into what’s on offer, but also check out the likes of DigitalGenius and DeepMind for something smaller or a bit more creative.

Of course you may be looking to create something truly bespoke in which case you may have to hire a bunch of experts to create your algorithm from scratch, or seek a start up willing to work with you and co-create. There are an abundance of really cool start ups just about to break on to the scene so this is a truly valid and cost effective approach, don’t rule it out.

That’s it. From here, you should be in safe hands. You know what you want, you have the data in play to get it and a partner who knows what to do with the data to get what you want.

My parting piece of advice is to remember that AI / ML (Machine Learning) are solutions that learn and develop, think of it as a child going from kindergarten to PHD level but in weeks rather than years. There may be a few mistakes along the way but be patient and think big, because with direction and correction the results are nothing short of impressive.

And it’s not just customer service stuff either…

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Marchesa and Watson’s cognitive dress – read more here

P.S. Here’s a mini wiki;

IA; Generally speaking an Intelligent Assistant is a pre-structured agent used to deliver automated responses but does not include self correction or ‘learning’, therefore is not always classed as AI. It’s often the step before AI and used to validate the quality of data. That said some do include NLP (Natural Language Processing) and are connected to the IoT (Internet of Things) so the line is often blurred.

NLP; Natural language Processing is a computer science that uses AI and handles human speech between computers and humans.

AI; Artificial Intelligence is an intelligent or cognitive behavior exhibited by machines, sometimes also referred to as problem solving or learning.

ML; Machine Learning is a sub-field of AI that includes programming computers to deepen the learning process.

P.P.S. If you find AI interesting generally you might want to check out my other blog, co-written with @kayperbeats – it’s a bit more off the wall but it’s insightful none-the-less.

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music is morphing

The audioscape is shifting. In 2014 digital music revenue matched the physical and though the report isn’t out yet I suspect the balance tipped over towards digital in 2015. Whilst downloads still just about make up the bulk of that revenue, streaming services show continued growth to satisfy the personalised on-demand needs of the ever impatient consumer.

It is becoming more apparent however, that rather than posing a threat to traditional radio broadcast, it is in fact providing pre-cognitive insight to help programmers find the next hit, or know when to stop playing a track to death, thankfully.

Having long been an advocate of services such as Spotify, Amazon Prime and YouTube I’ve pondered several times where the data connections between airplay, streaming and record sales will join up.

A short while back I spoke with Spotify (the world’s biggest music streaming service) about how their platform can inform what’s next, allowing them to be ahead of the curve on everything from up and coming artists to how to name their playlists, the value is clear; it’s a completely accurate analysis of listener choice.

Streaming is a mainstream activity. Over two thirds of internet users accessed a licensed digital service in 2015 and the strength of the industry today is seen in the total flexibility it provides, allowing artists to reach a much wider audience in a way they want to be reached.

This has seen a shift from music models based on ownership to those based on access, which coupled with consumers streaming more and more on smartphone and tablets (up 114% in 2015 according to Wells Fargo) means subscriptions will continue to shape the music portfolio available.

So what’s next? I reckon we have three things to look forward to:

First up, music will become more intuitive. The Echo Nest acquired by Spotify provides an intelligence platform that mixes human skill, clever algorithms and social curation, meaning you can quickly get personal. This thinking will spread.

Secondly, enhancing how we perform by influencing the frequency of our brainwaves will continue to improve. We all know that faster music makes us run faster and slower music focusses breathing for yoga. This thinking is already built into how Spotify’s algorithm can work, for example their partnership with Nike which matches music to your tempo.

Thirdly, a merger of these two approaches to create a constant seamless service that will use prediction to enhance our brainwaves through binaural beats so we all become super intelligent thanks to music.

OK, maybe that last one is a few years away… but it will happen.

 

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A view on the programmatic semantics of binary trading predication

What the..? You may ask.

Well, I’ll tell you… you see most of my meetings this week have been about the uptake of programmatic, commonly I’m being asked; Is it robust? Is it robots? Are the robots robust? How do I plug it in? Do the robots plug it in? Are the robust robots plugged in?

Breath in.

So, having explained this a lot, I find it’s easier to start with what it is not:

It is not: Real Time Bidding (RTB)

It is not: A new type of media

It is not: A new format, a new device, a new tactic, a new insight or a new inventory. 

It is, quite simply put; AN AUTOMATED PROCESS.

Programmatic Trading simplifies the buying and selling process by digitally connecting the buyer and the seller of the ad space. This brings automation to the process adding operational and pricing efficiencies which take the mundane and repetitive tasks away from humans.

It is important to note that this doesn’t mean that creative is any less important, studies show that creative is still responsible for 70% of the effectiveness, the placement and timing making up the other 30%.

Marketing is, and will always be, about getting the right piece of content to the right person at the right time. Programmatic quite simply means we can be quicker, more effective and therefore scale in a more structured and relevant way. 

I love this example from Nike and Google, it’s a great demonstration of what can be achieved with clever design and RTB, and just recently Unilever have explored the use of video in their Romeo Reboot campaign.

So in summary, you still need a wicked idea, a clever plan and some digital genius behind it, but if you embrace the fact that you can’t be in total control of the real time exchange and you’re prepared to sit back and enjoy the ride, then some really cool stuff can happen. 

And contrary to the title of this post, it’s not that tricky…

image found on adweek.com - thank you

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Analyse this

I would imagine you’re all familiar with Google Analytics and Omniture, and seen reports for PPC, SEO and Display, no doubt clearly outlining the return for every penny spent including; entry sources, bounce rates, CTR, page views and so on.

I’ll also wager you’ve asked your planners and buyers what the return for Social Marketing is? Yes? You’ll have asked: What’s the value of a Facebook ‘like’ or a ‘retweet’? We’ve all stared at pretty diagrams that show us the reach and potential eyeballs hit but, so far, it’s been an algorithm we haven’t quite mastered with the confidence to go back to the board and solidly say that the money spent has returned an incremental profit of ‘X’ through social.

Well this level of measurement has just taken two more big steps forward.

Firstly, Google recently announced that they are adding social media reports to their analytics suite which will show the social value through measuring; visits and visits via social referral, the conversions this led to, plus assisted social conversions and last interaction social conversions.

Secondly, Adobe has just unveiled its social analytics tool: ‘Adobe Social’. Apparently a more comprehensive version of Adobe Social Analytics, according to their Product Director Matt Langie. The new software still provides the basic listening tools already familiar to users but in addition now allows management of creating and publishing content and ads. It also follows similar tracking to GA so you can report from seed to purchase or drop off.

I wonder what this means to the likes of Radian and Sysomos, will these two giants take over?

 

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