Artificial, business, and consumer – the ABCs of dynamic intelligence in marketing

Mailee Creacy writes about the ABCs of dynamic intelligence: artificial, business and consumer.

This article originally appeared in The Intelligence Issue, our April/May issue of Marketing magazine. It was contributed by our issue partner, Rocket Fuel, to let readers know they can access a copy of ‘Nine Ways to Put your Data to Work‘ »

 

Mailee Creacy headshotThe use of artificial intelligence (AI) has already revolutionised our everyday lives – think customer service chatbots, personalised recommendations from Netflix and Amazon, auto-braking mechanisms in cars and smart home applications that adjust room temperatures automatically. The sheer volume of data available and the capabilities of AI allows for such an in-depth knowledge of a consumer’s behaviour that our relationship with technology has fundamentally changed from static interactions to a connection that is dynamically evolving.

This is evident in how we’re consuming media. Accessing the day’s news no longer requires sitting down and reading the newspaper in the morning or watching the evening news bulletin; instead, we live in a world where we can now access news sources 24 hours a day. Having our devices with us at all times means that platforms such as Facebook, Twitter, and Reddit are becoming our real-time news sources.

The 24/7 nature of news could result in an overwhelming number of stories being delivered to our devices, however, because these platforms use machine learning algorithms to analyse the massive amount of content, we receive news that is based on our interests and is relevant to us.

We call this a dynamic news feed.

This development is also true for advertising. Online marketing was once based on static display ads largely similar to those you’d see in a newspaper or magazine, except maybe for some animation and light interactivity. Now, thanks to responsive design and predictive marketing technology, consumer experiences – and the highly-personalised ads that support them – are assembled in real time using vast amounts of data, all in the milliseconds between clicking a link and loading a page.

We are now seeing this same transformation of the intelligence and insights that power those experiences – the emergence of dynamic intelligence for marketers.

Traditional marketing research relied on focus groups and surveys collecting quantitative or qualitative information from small but statistically significant panels of consumers. This process yields valuable segmentation and insights into the hearts and minds of those consumers. However, the output is typically static in the sense that it is a snapshot in time, and updated infrequently (eg. monthly or quarterly).

What makes dynamic intelligence different is that it brings together the best of traditional research methodologies with real-time machine learning to create a continuous feed of actionable and precise insights, allowing marketers to better serve audiences in the present moment and to predict their next need and behaviour.

 

A is for AI

As the volume and velocity of data increases at exponential rates, reflecting the trillions of data signals generated by interactions between consumers and brands, the capacity for marketers to process and analyse reaches impractical levels. The application of AI allows marketers to run thousands of goal-oriented experiments in real-time in order to discover the moments that lead to conversion.

The result is a model of continuous optimisation that no human could achieve on their own, enabling marketers to activate their data and create more meaningful experiences that drive real consumer results.

 

B is for business intelligence

Business intelligence involves the analysis of historical financial data and current business operations using modelling techniques to predict the performance and outcome of business decisions.

The process of testing new attributes within these models creates insights that allows executives to understand their business better, set KPIs that will actually move the company forward, and put in place modelling for ongoing product profitability. These models lead to significant improvements in predictive confidence levels, consumer targeting accuracy and business performance.

 

C is for customer intelligence

Customer intelligence teams use research methodologies to identify segments of consumers that have the highest likelihood to love a brand or buy a product. This analysis comes from a variety of techniques including CRM data analysis, historical sales data, offline focus groups, research surveys, panel studies and more.

AI eliminates the need for traditional audience segmentation methods, using dynamic learning to understand the individual in real-time and continually improve the efficacy of audience targeting and drive business success. We understand that marketers have operated within these parameters for many years, and segmentation will ultimately always be present – but now is the time to take our experience and knowledge of this space and apply that learning to a new, predictive approach.

In addition, customer intelligence segment hypotheses can be leveraged to accelerate the machine learning process, helping to accelerate conversion models.

 

Continuous optimisation of customer interactions 

The A, B and C of dynamic intelligence therefore bring together business outcome-oriented machine learning, the segmentation of consumer research and business intelligence modelling.

As a result, we get trillions of opportunities to observe and optimise profitable customer actions. We also get the opportunity to better understand customer behaviour over time, learning within the moment of consumer and brand interaction to create a continuous feed of actionable and precise insight.

As with the 24-hour news cycle, dynamic intelligence is always on; it’s updating and rendering optimisation decisions and insights in real-time. By transforming tried and true research methods through artificial intelligence, we see a meaningful positive brand lift and economic impact in programs run across the business’s enterprise.

 

To understand more about data, and delivering on the demands of consumers and your CEOs alike, read your free copy of ‘Nine Ways to Put your Data to Work’.

 

 

Mailee Creacy is ANZ country manager at Rocket Fuel.

Image copyright: adam121 / 123RF Stock Photo

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BY Partner ON 29 May 2017
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