Move over programmatic, predictive marketing is the new kid on the block
Mailee Creacy explains four tools of predictive marketing that will see it transcend programmatic’s capabilities.
The innovation centred on a better buying mechanism. Now, predictive marketing is transcending these capabilities through the application of artificial intelligence and big data – helping marketers anticipate consumer needs and wants, optimise every part of the marketing mix, and deliver seamless, always-on, always-relevant experiences.
The innovation centres on intuiting and anticipating consumers’ needs for products and services.
The predictive revolution in digital marketing is upon us, brought about by four key factors: unified profiles, algorithmic attribution, applied AI and dynamic creative. By harnessing these forces, the most innovative predictive marketers will be able to create individualised, custom consumer experiences unlike anything done before.
1. Unified profiles
Creating unified profiles across devices is not a challenge today because of the amount of data available, it is the ability to turn data that into action. Predictive allows marketers to formalise ‘data strategies’ that start with combining data from different locations in different formats from different systems of record, for example sales force automation systems and customer relationship management processes.
To utilise the full benefit of this data, marketers will need to focus on data hygiene to ensure this combined data is accessible and actionable across technologies. Ultimately, this will result in a deeper and more unified understanding of consumers as individuals versus consumers as part of a segment.
There’s already a tug of war between the value of identity solutions as a way of powering contextually relevant, cross-device advertising versus the need to protect consumers from unwanted or excessive third party data collection. This will only become more intense. Marketing leaders from the world’s biggest brands will need to work with industry trade groups and government regulators to frame a viable solution.
2. Algorithmic attribution
Predictive marketers are starting to better understand their audiences as individuals via unified profiles and are able to create meaningful experiences across every device, leading to last touch attribution falling short.
By definition, last touch doesn’t capture the complexity of interactions between a brand and consumer that might result in an eventual move from awareness to conversion, nor does it capture the complexity of interactions across devices.
Predictive marketing will see the testing and development of new AI-powered ‘algorithmic attribution’ models that move away from last touch and toward more comprehensive assessments.
These models will certainly redefine the way predictive marketers measure ROI and, potentially, could radically change the way they allocate dollars. There will be new winners and many losers across the adtech landscape.
3. Applied AI
Predictive marketing will come about as AI takes another step forward in prominence and capability.
The past few years have seen marked advancements, with IBM pushing its Watson AI more aggressively and Salesforce launching Einstein. AI is cool again.
Part of this is due to the rise of the Internet of Things. There will be 30 billion connected devices by 2020, according to McKinsey and Co, and while not every connected device is an advertising opportunity, every device is a potential source of data that can deepen a brand’s understanding of a consumer’s context, behaviour and intent.
Big Data has now become immense Data and the challenges of collecting, processing and acting have multiplied exponentially. Predictive marketers who want to take advantage of this data to create meaningful experiences in the moments that matter need the computing power to augment both their ability to understand, act and respond in real-time – what we call Applied AI.
To tie all the pieces together, marketers need the ability to learn from the data and improve the specificity of a campaign – getting the right creative to the right audience at the right time. With the amount of data being assembled and put to use, an amount that is beyond human scale, artificial intelligence provides the most effective way to process the data and make informed decisions.
4. Dynamic creative 2.0
The next marketing problem that AI will help solve is creative. An interesting example is ING’s ‘The Next Rembrandt’ campaign which used data to create an astonishing, entirely new painting in the style of the old master. But this is only the beginning of what we call dynamic creative 2.0.
Through AI-powered media optimisation, marketers can identify millions of people as individuals (aka unique snowflakes) but they are limited by the cost and complexity of building creative – especially video – to create truly individual experiences for each person.
There are several exciting startups, like Flite, that are already able to seperate video assets into components. Predictive marketers will combine AI with these creative management systems to process and assemble creative components into custom experiences in real time.
Dynamic creative will enable the most innovative creative directors to bridge the gap between art and science, which has far-reaching implications for how agencies and brands are organised. We have already begun to see the heralds of this shift with the debate about the agency of the future.
The best thing about being a part of the digital marketing world is the continuous innovation.
Nothing is constant.
As always, those creating impactful campaigns have connected the dots across trends to understand when there is really a new paradigm versus just a flash in the pan. We believe that the predictive marketing revolution is grounded in a major shift in how we think about data, tech and identity, and will open up incredible new opportunities for those marketers who are able to embed AI into their marketing DNA.
Mailee Creacy is country manager at Rocket Fuel ANZ.
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