Machine learning has a high profile currently and is riding a wave of exposure in the media that includes articles about subjects from self-driving cars and self-landing rockets, to computers beating the world’s best players at Go, the most computationally complex board game in the world. Is there an opportunity for your organisation, and the marketers within it, to make use of this “new” technology?
The buzz
Machine learning techniques were developed as long ago as the 1950s, but with the advent of big data and large analytical engines, the prevalence and the ease of applying the techniques has increased. 
Additionally, organisations now understand the value that analytics can bring, so are willing to place it front and center in their plans and invest more time and resources in exploring new and better techniques. Segmentation and predictive models, for instance, have proven themselves time and again in the marketing world, but to a certain extent, they require a higher degree of knowledge to understand. In some cases, a machine learning technique unburdens the user of the statistical work, but provides just as good an answer as a traditional technique. More people, with more data, trying to make more decisions lends itself to a technique that requires less manual intervention.
What it means for marketing
Organizations, large and small, can have huge, complex data that can from the latest advances in machine learning – banks have transaction records, telcos have call details, retailers have purchase records.
Take marketing in our omnichannel world as an example. There are huge amounts of customer interactions and there are business problems, such as attribution and optimizing the customer experience, that are perfect for the latest machine learning techniques. For real-time personalization of experience and real-time calculation of recommendations, great benefit can be gained from self-learning algorithms in reinforcement learning.
But it is important to remember that organizations also have many analytically driven challenges that are smaller, simpler and just as important and valuable to the bottom line of the organization. Again, for marketing, more traditional disciplines like segmentation and propensity modeling are still extremely useful, and organizations need to keep using capabilities like these to ensure the continued benefits from their use.
How SAS can help
SAS has embraced machine learning techniques for many years, and recently took a further step forward with the latest release of our SAS Customer Intelligence 360 suite of products. SAS has built a recommendation engine with the best of both worlds – a predictive model built using traditional techniques (logistic regression) and a machine learning algorithm (using naïve Bayes classifiers). Fortunately, your customers don’t need to understand these techniques – they just want your website to make better recommendations!
