How AI Helps in Marketing Technology Transitions

 More than 30 years ago, when Artificial Intelligence (AI) was more of a new concept, it was during the time of an increase in the demand for reasoning and expert systems. Universities were shifting towards study of machine learning, neural networks, computer vision, robotics and the likes. The topics of discussion during lunch breaks were around game theory and non-zero-sum games. It sounded like the tremors towards something big. This was 40 years after the Turing Test (a test to determine a computer’s capacity to think like a human) and Ray Kurzweil publishing the thought that humans can build more intelligible products than themselves.

Fast forward a few years, the field of marketing automation technology had taken all by surprise. There were terabytes, and even petabytes of data available, which could express human behaviour and their predictions. There was linear and non-linear regression, cluster analysis, factor analysis and much more. While the work was progressive, it was good and helped make a difference in marketing.

 AI – Just A Marketing Automation Service?

While AI did play a role in marketing, it was at a slow pace. Data-driven marketing agencies preferred a tacit assumption about where their marketing money could be spent and what their customers chose. They followed rules with discipline and created machines that automated those rules. Outsourcing of marketing cognition to machine intelligence was a risk that few dared to take.

However, with flexibility and accessibility of cloud based platforms and Big Data architectures soaring, things started changing at a fast pace. With computational power available at reasonable costs, analytics discussed their ideas and developed techniques that could be applied on these platforms. This paved way towards experimentation in different corners. Marketing now started to let AI in.

AI In Marketing – Promising Developments

In the past five years, there has been an incredible development. While streaming and analytics was initially limited to Twitter Storm, four or five different frameworks soon followed, such as Spark Streaming, Flink and Kafka Streams. This created diversity which was promoted by open source and contributions from large companies such as IBM, Cloudera, LinkedIn and Intel. The frequency of open source updates helped increase use and further contributed to these frameworks.

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A typical approach would be with a Data Scientist, who defines a problem, crafts an approach to solve the problem and then translates the approach helping engineers to understand its implementation. The engineering team automates data transformation and collaborates on the machine learning technology stack. For example, Scala and Apache Spark. The Data Scientist input may include, for example, applying a model ensemble approach, combining clusters with lifetime value models, or attrition risk models, or a future potential value modeling (or a combination of all three) and then optimizing for real-time streaming data.

Currently, we have vast amounts of data for the machine learning to ingest. In the times to come, new events can be aggregated and passed to a k-medoids clustering algorithm and then determine if the individual migrates between clusters before applying different models.

In other words, AI provides data-driven marketing companies the ability to determine customer audience based on previous behaviors, current behaviors (in the millisecond) and immediately apply a prediction on the estimated value or potential for attrition from a brand. This happens in real time. The relevancy of marketing depends on the recency of decisions made, especially when it comes to new customers with chances of new events taking place.

AI In Marketing – What Does The Future Hold

The future has a lot more to hold. It is not a single institution quickly applying the multitude of available AI techniques and marketing automation technologies. While this has been done narrowly, the positive results, whether financially or otherwise, will only precipitate increased adoption and greater comfort for marketers. AI will not only help with predictions, but will also enhance promotions to customers, while similarly helping with fraud, marketing investment decisions and real-time services for the customer. That is the sole key – to focus on the customer and their needs. If machines can help in predicting and solving customers’ needs and desires, it can establish the right guard rails and enhance customer experience for brands that are being served, on a continuous basis.

While we may not be at the edge of a general intelligence event, marketing is surely at the edge of a dramatic revolution, which will be fuelled by AI.