For most of advertising’s history, advertisers have produced their messaging primarily based on the gut instincts of their creative teams.

Sure, we’ve always had surveys and market research to provide loose guidance on the demographics and preferences of our potential customers. But until recently, the end result has always been a single, one-size-fits-all ad that succeeds or fails based on the creativity and the guesswork of the people who produced it.

With the development of digital advertising technology, this has all started to change. In recent years, advertisers have begun combining Human intuition with Artificial Intelligence to produce some of the most powerful ads we’ve seen yet. Today, many of the world’s leading brands are using customer data and powerful Machine Learning algorithms to inform smarter creative choices, and sometimes even to produce parts of the ad itself.

Far from removing creativity from advertising, AI is helping brands deliver effectively, evocative ads, tailored to the unique wants and needs of each customer. And its impact on digital advertising is only getting stronger.

The Future is Now

At its simplest, Artificial Intelligence is any technology that allows machines to perform Human Intelligence tasks as well as, or better than humans, at greater scale and lower cost. Through Machine Learning, these AI tools are often able to learn from their results, creating a feedback loop that allows them to make smarter decisions over time.

Already, AI is helping brands develop a number of creative, engaging advertising experiences. For instance, the fitness chain Orangetheory used an AI breakdown of its audience’s online browsing habits to learn that its customers frequently gravitated to the chain via ads on college sports content and music platforms, rather than health and wellness sites. Orangetheory then applied this information by producing video ads that linked the gym to the audience’s hobbies outside of it.

Meanwhile, other brands are using AI to edit and optimize their ads while their campaigns are ongoing. At VidMob, where I serve as Chief Product Officer, we use AI to evaluate how different aspects of an ad are driving advertising success (e.g. Is a nature scene more engaging than an urban landscape?), and then use the insights to optimize the campaign’s creative via our network of designers. Through creative versioning tools like ours, companies like AB InBev are using AI to pursue the most effective ad for every campaign.

The future may even see AI-powered ad messages inserted into entertainment content in real-time. The Chinese video site Tencent is testing a new feature that would allow advertisers to serve personalized ads as native product placements while people are watching its shows. For instance, if an on-screen character is waiting for the bus, a brand could cover the bus stop with an ad relevant to the viewer’s interests.

Through Our Powers Combined…

Too often, those of us in the ad industry get the wrong impression about the impact of AI on our field. While some see AI as eventually replacing human creatives and strategists, the truth is that this is unlikely to happen at any sort of scale.

Certainly, ad agencies have released a few pieces of video content created entirely by AI, but these have largely been experiments designed to push creative boundaries and impress awards show judges. A more likely outcome is that brands will use increasingly effective AI technology to free their human staff from the rote grunt work of data analysis and campaign optimization, giving them more time for creative conception, campaign strategy, and other more interesting tasks.

Indeed, the future of advertising isn’t a robot takeover—it’s a creative, efficient and effective collaboration between Artificial and Human Intelligence.

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To quote Eoin Treacy in ‘How to Profit from the Peaceful Rise of the Robots’: “Don’t think of robots as replacements for humans – think of them as things that will help make us better at tackling many of the problems we face.”

Never has this been more appropriate than now in the digital media industry. The three key problems that machines can fix are costs, monetisation and ad quality.

As programmatic advertising evolves at a rapid rate, the need to deploy smart technology to continue to add value has increased. In August, the New York chapter of the IAB launched a working group to help the industry navigate the impact that artificial intelligence (AI) and machine learning (ML) will have on digital advertising.

The pressure will be on every company in the complex ad tech ecosystem to show they are not contributing to costs without adding the expected value back. As a company with our roots in the SSP side of the equation, we are no different. The truth is that SSPs will have to evolve or risk becoming irrelevant – much like any player in our industry.

Many of the productivity and financial gains for publishers will remain under the hood, and will not attract major headlines.

However, we all know that a lot of advancements will only prove fruitful with the right education. Here are some of the ways that machine learning technology can be used to put more money in the hands of publishers.

Bid throttling

The volume of ads flowing into the bidding landscape has skyrocketed thanks to header bidding – a tactic that has been a game changer for publishers, but has led to challenges on the buy side. SSPs and DSPs are receiving more traffic than ever before, so the problem, for buyers in particular, is keeping infrastructure costs down.

The answer is bid throttling, which leverages machine learning to help ensure the SSP is only sending traffic to DSPs that they are interested in, thereby increasing the efficiency in how they work together.

Here’s how it happens in practice. If we are working with 150 DSPs, and 10 billion ad impressions, it could require up to 1.5 trillion bid requests being sent to the DSPs, incurring significant infrastructure costs for them, and for us, when only the one DSP will actually win the bid.

In order to keep a cap on technology costs, we use machine learning to predict which DSP is likely to win the bid. Our algorithms analyse data on a historical basis and identify which attributes actually affect a DSP’s win rate. We use machine learning to look at trends in order to ascertain what’s being bought and what’s not.

Then we adjust our systems according to that trend data. If you can predict who is likely to bid and win on an impression, and then send an impression to those DSPs, we can generate significantly fewer bid requests. Our bid throttling efforts typically result in a reduction in QPS (queries per second), an increase in win rate, plus an increase in spend per QPS for DSPs.

This approach allows DSPs to be more efficient, which ultimately benefits publishers.

Dynamic Floors

The legacy programmatic pricing waterfall system was characterised by manually set rules, and were based on aggregate historic pricing data. This approach was up-ended by header bidding.

It’s about using platform data and machine learning to predict how advertisers will value an ad impression, and we work with our publisher partners to set a price floor in the auction that we submit to the bidders.

The publisher is able to reclaim more control over the ultimate price, thus the value of the publisher’s ad inventory is protected and they can garner more information about a buyer’s willingness to pay. We have seen a noticeable increase in monetisation for publishers – compared to the old auction systems.

Ad quality

The continued growth of programmatic trading has made it difficult for publishers to maintain quality control over the advertisers and creative that appears on their sites.

Thanks to machine learning, we can predict the quality of an ad before it appears on a publisher’s site. We scan ads as they come through our platform and use machine learning algorithms to identify an ad that is harmful (malware, offensive content, high bandwidth) and block it before it is shown on the publishers’ site.

We look at more than one hundred variables about the ad, and we also overlay known bad creatives, to feed our algorithms.

Machine learning has the ability to deduce over time whether it should block an ad, and which partner to trust. We also use algorithms to help discover if the ad being sent is something the publisher won’t like, which goes a long way in protecting advertisers’ brand safety.

In the movie 2001: A Space Odyssey, HAL 9000 said: “I am putting myself to the fullest possible use which is all, I think, that any conscious entity can ever hope to do”.

Meanwhile, we’re only beginning to scratch the surface of what machine learning can accomplish. Consciousness is still a long way off but HAL 9000’s sentiment was in the right place.

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We’re in an algorithmic world. Everything we do on digital is recorded and now, being understood. By mapping a consumer’s digital footprint, marketers are able to understand their audience better and run targeted advertising. In essence, we are data. Bits and portions of us tell us a lot about who we are. And yes, there are privacy concerns, and yes, there are major areas that require regulatory control. For this, there’s a lot of authored articles out there and I’m not going to attempt talking about one. As a digital advertising man, my area of expertise is to understand how data can make advertising better. How data can help marketers sell their products, bring in more accountability to the opaque world of digital advertising and so on.

From the era of TV advertising to now, data is a clear differentiator in how advertising has changed. There’s a lot more accountability in advertising now. The good old days of ‘TRP ratings’ are obsolete and we’re already witnessing early days of mobile and desktop advertising budgets taking over TV ad spends – more so in mature markets. Digital advertising is the future and the role of big-data analytics in advertising is an interesting space. I pick mobile because it’s far rampant than desktop advertising and a lot more personal. Maturing markets like India and Indonesia have catapulted desktop advertising because users’ access to the internet is via a mobile device and not desktop.

In an environment like mobile, personalized campaigns and hyper-targeted messages play a vital role. It offers a lot more opportunities for marketers to sell. It is the ability of data – in combination with thoughtful analytics around segment, intent, desire, and more that provides value to brands. For data to be meaningful to decide your strategy, it needs to be captured and stored efficiently. Then someone must manage the data, analyse it, and extract value from it. Data, big or not, doesn’t add up to anything worthwhile if it cannot be mined to show predictions, results. That is where Big Data Analytics plays a crucial role in the collection, storage and deciphering data to meet the marketing goals.

We have observed that data on mobile has been far more comprehensive than on desktop. For example, we ran a campaign for a leading ecommerce provider that wanted to boost sales for their gadgets category. We ran a campaign for both mobile and desktop and the results were unsurprisingly in favour of mobile. We had nearly 9,000,000 impressions (ad views) a day and got 20,000 conversions on mobile while we received only 5,000 conversions on desktop. While one could argue this was an isolated incident, our network tells us that mobile ads are far more promising for conversions than desktop advertising.

When the real estate for advertising is squeezed into is a 5-inch screen, the ad elicits a lot more response than a desktop – simply because the probability of the user observing the ad increases. This makes mobile a lucrative area for marketers to look into. Comparatively, the outcome of impressions captured on mobile is easier to predict with its nature of anytime, anywhere impressions recorded by users such as frequent travellers, recent travel booking for cold places, visiting online jacket stores, etc.

By segmenting the data using powerful analytics tools, you can break down your audience into different buckets. Will XYZ, male, 20-24, traveller, online shopper, avid sports reader be more likely to buy a pair of trekking shoes? By deriving this insight through analytics, I am more likely to ‘convert’ a user to buy the product – making the marketer that spends their ad dollars with me, a lot happier. When our business model is built around these conversions, my data needs to be robust. I need to look at technology and ensure that I have enough conversions any given day. By bringing in this accountability, digital advertising is changing. It’s more accountable. It’s more transparent. Above all, it’s setting a standard for what good ad tech needs to look like.

For any business now, while mobile is the centre stage of a digital strategy, big data pushes the benefits of mobile advertising with more personalized, hyper-local and real-time marketing approaches. Which brings me to the big question – do I define a company that works on this technology as a big data one, or an advertising one? For me, every ad tech company is first a data-driven company and advertising happens to support a company that’s able to mine this data.

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