The re-emergence of a 17-year-old phone may have captured a nostalgic sentiment at last week’s Mobile World Congress, but the event should more correctly be remembered as a landmark for how artificial intelligence is fuelling software innovation. Announcements from companies such as Motorola, Samsung, Telefonica and Roborace provide more than a glimpse for how artificial intelligence will become embedded in our everyday lives, in everything from personal assistants to autonomous cars and online customer support.
Our evolving relationship with technology is bringing a fundamental change to our habits and behaviours, shifting us from static interactions to a constant state of flux. This is evident in how we’re consuming media. The news no longer consists of current event snapshots delivered a couple of times each day. Today, we live in a dynamic, perpetual 24/7 news cycle. Mobile devices allow us to capture our every moment for broadcast, and increasingly our social feeds like Facebook, Twitter, and Reddit are becoming our real-time news sources.
Such media uses machine learning algorithms to analyse the massive amount of content, and deliver it to consumers based on our interests, meaning the information is more relevant and accurate. We have moved from static news delivery to a dynamic news feed.
This development is also true for advertising. Online marketing was once based on static display ads largely similar to what 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 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 very 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 (e.g., monthly, 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 not only to better serve audiences in the present moment but also to predict their next need and behaviour.
These are all grounded in the Ai, Bi, and Ci of intelligence:
As the volume and velocity of data increase 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 artificial intelligence 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.
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.
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.
Artificial intelligence 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 Ai, Bi, and Ci’s of dynamic intelligence, therefore bring together business outcome-oriented machine learning, the segmentation hypotheses 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.
Like 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 programmes run across the business’s enterprise.
Dynamic intelligence is based on unconstrained thinking, and requires a shift in all of our mindsets to capitalise on this powerful and unprecedented opportunity to understand what really predicts and drives the customer, and delivers improved ROI.
By David Gosen, international senior vice president & general manager of platform solutions at Rocket Fuel
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