What makes a disruptive technology?

This article was first seen on – http://betanews.com/2014/08/12/what-makes-a-disruptive-technology/ 

black swan

According to Clayton M Christensen, author of The Innovator’s Dilemma, disruptive innovations are characterized by their ability to create entirely new markets rather than merely update existing markets with new products. They are black swans, rare events where new thinking and changing markets combine to create radical change.

A common example is the light bulb and Pearl Street Station — a major gamble by Thomas Edison. Within years of its development the kerosene lighting industry was all but non-existent, and the world was a brighter place. (The kerosene industry had similarly put an end to the whaling industry — thankfully — a few decades earlier).

Disruptive innovations also tend to involve making things simpler and cheaper than the alternatives, with unexpected results on multiple sectors. The motor car in the late 1800s was too expensive to be disruptive, but Henry Ford’s innovations in the 20th century made it cheap enough to replace the horse drawn carriage (and suddenly there were no more street dung collectors providing manure for farmer’s crops…)

My company, VisualDNA, sits at the intersection of (at least) four disruptive innovations that have arisen over the last five years and that are already making their impact on global society.

I believe that as these four innovations continue to evolve over the second half of this decade, they will create the perfect conditions for VisualDNA’s unique approach to understanding people to create disruptive change on a global scale, across all industry sectors.

1. Big data

Five years ago this term almost didn’t exist but it is now one of the fastest growing ‘most searched for’ terms on the internet (according to Google Data Trends). Analysts Wikibon predict this new sector will grow from almost nothing in 2010 to $50bn dollars by 2017. A report by PAC predicts big data will impact on every aspect of the global economy during the same period.

PAC also found that the fastest growing area of all will be in services to understand big data. Most big data is historical and transactional. Understanding personality, psychology and motivation are the missing ingredients that will enable deep understanding of intent and truly predictive analytics.

VisualDNA’s unique ability to engage users and turn their personality into digital form will be at the forefront of providing this vital information to power conventional big data.

2. Psychology

Psychology is undergoing a number of dramatic changes as new technology allows us to understand how the brain and personality are linked. Neuroimaging allows us to visually track the workings of the brain and identify how different responses and our emotions work at a chemical and neuron-level. Daniel Kahneman’s work on decision-making and behavioral economics is just starting to make itself felt in the mainstream and will become increasingly influential as real-time big data allows us to understand and model human responses at a macro level. Computer scientists are developing ever more sophisticated simulations and models of the brain allowing experiments and analysis to be performed, changing the way we see the brain and raising the possibility of truly understanding ‘what makes us tick’.

As this understanding develops, it will have a major impact on our understanding of people and how they behave. VisualDNA is already leading the way in understanding how individuals behave online, and through our partnerships and research we will continue to be at the forefront of these developments. [Read more…]

Some new features for WHYanalytics – and a word of thanks to our 270 beta partners

I’m the lead product manager for the suite of WHY products, VisualDNA’s new audience visualisation tools for publishers, advertisers, e-commerce companies and agencies.

Since launching the first beta of WHYanalytics in June, we’ve made great progress in developing the tools with more than 270 companies currently signed-up and using the product: traditional news publishers, online retailers and services businesses large and small from across the globe.

In particular the response from retailers has been fantastic.  Since we have the technology to profile any site visitor in real-time, it’s possible for us to power an automated, personalised, shopping experience through our API.

For example, if you know any given visitor to your website is in-market for a holiday, and/or is a relatively conservative spender a retailer can give prominence to products that suit that individual.  We’re a way off being able to offer this as an off-the-shelf feature in WHYanalytics but we’re running some tests with a major UK supermarket retailer to see how this could work in practice.

As this is our first analytics product, we’ve experienced a very steep learning curve and while in many aspects we were technically ready to scale the beta programme, we have struggled to meet the increasing demand on the customer support front. As a result we’re currently expanding our team in order to streamline how we consolidate feedback and feed it into the product-development process. To those of you who have been involved, a big thanks.

The feedback we have received so far has been extremely valuable and is behind some of the key changes we’ve made to the product in recent weeks;

  • Segment reaches are a useful metric for seeing what the composition of your site is. We’ve now also introduced indices – metric highlighting uniqueness of a segment for a particular site or a section compared to a base – the entire universe of VisualDNA profiles.  Moving forwards, we’re planning to expand this functionality to allow website owners to choose a base of their liking whether it is a section of their site or an entire industry sector.
  • Another key change is a new look of the Snapshot view.  The ‘widgetised’ dashboard allows us to present more data points at glance (demographics, intent and brand preferences as well as our emotive segmentation). This was a very important first step in an attempt to make the page adaptable for various screen sizes but also in preparation for making it customisable on per user basis.
  • And finally, we’ve made the signup process much smoother; after completing a simple one-step registration process you can delve right into the tool – no need to go back to your email to click on a confirmation link. Yes, we hate those as well!

We’re big advocates of single login and hence anyone who has already signed up and deployed code for WHYanalytics will be able to use the same sign in to access WHYcampaigns – a new product coming soon that will allows advertisers and agencies to see who’s interacting with their campaign from the moment an ad is shown on the network through to conversion.

Another product – WHYplanner – also coming soon, will allows agencies and planners to browse and discover VisualDNA audience segments and combine these to create a target audience in an answer to a campaign brief. Since no code is required to use this product – that won’t require a sign in at all. Follow us on twitter or this blog using Feedburner RSS or email for updates.

Finally – thanks to all our beta partners for your ongoing support and for helping us to build a better product. If you’ve not yet signed up and want to try it out simply visit http://why.visualdna.com. It’s free.

How to use data to optimise targeted advertising campaigns

This article originally appeared on The Drum on 13 September.

The growth of big data has seen a rapid increase in the use of data segments to power targeted advertising, the science of reaching anonymised niche groups of people defined by common characteristics.

VisualDNA uses the first-party, self-declared data we generate from visual personality quizzes to create high-quality Demographic, Brand Preference, Purchase Intent and, now, Emotive segments that power targeted advertising.

With multiple factors affecting the performance of a campaign much like effective media buying effective data buying is something of an art form. Programmatic? Not really: data optimisation is done manually, and by smart people. Our top tips for using data segments successfully:

1. Beware of ad-blindness! Don’t over-impress…

A car brand running a campaign of 50 million impressions will likely want to reach people in-market for a car. In theory, a segment such as “Car Buyers” would yield great results, but if that segment comprises just 500,000 people then each user would see 100 impressions a month. Not good.

Over-targeting an audience that’s too small bombards your audience – reducing conversions and increasing CPAs per impression. So define the total number of impressions served and work back. If an average of 15 monthly impressions per user is about optimal on a cost-per-impression/ conversion basis, then a 500,000-strong segment is only enough for a reach of 7.5m impressions.

A run-of-network to make up the remaining 42.5m impressions means you’re reaching a wider audience, reducing the average data and media cost per impression –plus you can test the performance of the data. Data can dramatically increase conversions, but you won’t hit that uplift and justify your data spend if you over-impress.

Trading desks know this of course but thinking about data at the media planning stage helps them buy better segments. The IAB’s Data Usage and Control Primer is a great intro for anyone using data as part of a campaign.

2. Use analytics tools to define and build your audience

At face value, choosing segments should be straightforward.  Quality data, demographics from trusted providers such as Experian, and intent data from sector specialists should perform. Descriptive VisualDNA segments such as “iOS Preferrer”, or Emotive segments, built from self-declared data collected through our quizzes, are also obvious ways to build an audience.

But not all segments are self-evident. Audi, for example, might intuitively buy an “Audi Preferrer” segment from one provider, but it might be that a “Luxury Holiday Preferrer” segment from another provider is a better fit for their brand.  Adding such a segment to a campaign would increase the size of the targeted audience, improving the performance of a campaign

In addition to industry-standard audience insight tools such as Quantcast, WHYanalytics profiles a website’s audience to show which VisualDNA segments rank most highly – powerful new insight for publishers, ecommerce, advertisers, media agencies and brands.

Such tools help publishers meet high-value brand campaign briefs or identify niche audiences while, in ecommerce, online stores can personalise around personality. Plus agencies can pick the best segments to build a targeted brand advertising campaign.

3. Set the right metrics, test the data & monitor throughout.

What does success look like? A Victoria’s Secret creative showing an attractive woman sat on a beach may attract a disproportionally high number of clicks from men, suggesting that male-skewed segments should be used, at the expense of other female-oriented segments…

So it’s about conversions above clicks but track both with a short, un-targeted, “Discovery Phase” at the start of the campaign against each creative.  Whether it’s a direct response or brand campaign, by analysing the ad (impression beacon) and conversion page (conversion pixel) we can tell you which segments are responding. From here you can optimise around the right segments, and have a benchmark from which to measure performance.

We’ve created WHYanalytics to help publishers, ecommerce websites, advertisers, brands and agencies better understand VisualDNA data, and use it to create a better-personalised experience for their audience – we’re working with our beta partners to build more features and functionality that will make it easy to track. Free to use and easy to deploy, try it out at why.visualdna.com/analytics

Reaching beyond demographics

I joined VisualDNA earlier this year from Google to lead the media sales team – and after almost 10 years in the business the technological advances have been mind-blowing. Exciting as this is, the art of marketing, media-buying, timing and nuance is being replaced by machines. Define your audience, set your budget, input your criteria into your media exchange of choice and off you go. It’s a growing trend too, some 25% of all UK online ad spend in 2012 ran through RTB platforms and most experts expect that percentage to grow further and faster.

But as the ad world becomes more efficient and effective at trying to reach the right person with the right ad at the right time, it’s getting a bit soulless, disconnected from real human connection. For all the insights the internet can deliver, and disruption it’s caused to traditional mass media companies – advertisers still rely largely on demographics and mass when buying media online.

Demographics hail from the Mad Men age. It was the National Readership Survey (NRS) that first carved people into ABC1/ C2DE groups – classifications borne out of a regimented 1960s class system where your preferences were inferred from your buying power with the ‘professional or higher managerial’ upper middle-class grade A at the top to Grade Es ‘at the lowest level of subsistence’ – dependent on the State – at the bottom.

Simply put, demographics tell us little about ‘who’ our customers are – and even less about ‘why’ they are motivated to buy stuff. Kantar’s Target Group Index (TGI), Experian’s Mosaic and IPA Touchpoints all provide a more nuanced way for media owners, agencies and brands to segment and reach an audience, since they are enriched by multiple behavioural data sets (clicks, browsing, purchases and so on). which is great. But they still don’t get to the core of what makes people tick. It’s all too assumptive, ‘umbrella-level’ data…and lacks human feeling and ‘real’ insight.

We know smart brands want to understand people on a deeper, more emotional level, and this is the real opportunity for VisualDNA. No other adtech company, to my knowledge, goes anywhere near as far as we do to understand what actually makes people tick.

First, our in house team of psychologists, mathematicians and designers iterate  and refine our quizzes to an astonishing level of detail – this is why the data we collect and the profiles we can build on an individual are both rich, accurate and at mass-scale.

Second, we capture emotive data –  and we’re approaching a point where we can scale it too. Brands define their ideal customers by their emotive characteristics so it makes sense if they can buy media against those characteristics too.

And third, we’ve proven that our audience targeting technology actually performs. We match captured behavioural data from our network with that of our quizzed users, and we can match any custom campaign brief we receive. Our sales are growing at an incredible rate – which proves to me that the demand is out there for quality data. We are seeing a larger appetite from Media Agency data teams, wanting to take in our data, digest it, and supply out to their Advertisers as ‘portfolio management’ data. It’s great to be at the heart of this ‘real’ consumer insight and business intelligence.

Sitting behind this is a broader vision to humanise the web by helping businesses to truly understand why their customers behave in the way they do – and meet their needs in real time.

It’s a really exciting time, and while we’re not saying the end is nigh for demographics and mass media, there’s a real opportunity to do something different and we’re relishing the challenge.