It was once said that software would eat the world. Now it appears that machine learning is poised to eat both software and the world.
But what exactly does this mean for the world of marketing? Will machine learning take our jobs, or simply make us better at them? The Kapost marketing team asked me, as VP of Engineering, to shed some light on this topic and explain how we got here, where we’re going, and why it matters in the world of content.
A Brief History of Software Development
In simpler times, software developers manually encoded the algorithmic decision-making that defines how applications behave. These hardcoded rules determined key points of interaction, such as which products were shown to a potential buyer or what insurance rates was offered to a particular customer.
But the capabilities of our systems and the demands of our customers continue to evolve, and these factors present new challenges for application developers and product owners.
Product owners are now awash with data—both with what we can offer customers and what we know about them. At the same time, customers expect increasingly personalized interactions across every aspect of the digital world.
The challenge of reconciling exponential growth in available data and the pressure to meet growing user expectations can only be met with a product engineering approach that scales alongside its data. This is where machine learning (ML) comes in.
What’s Next: Machine Learning
First, let’s get on the same page. What exactly is machine learning and where does it operate today?
ML is a technique for developing systems that dynamically learn and adapt based on the data they are fed. The more users interact with applications that incorporate ML, the better those applications can support those users and beyond.
As digital consumers, we can’t escape the influence of ML in our daily lives. Whether you know it or not, the influence of machine learning is all around us—sometimes in ways we quickly take for granted. Next time you listen to a song recommended by Spotify, speed up an email reply with Gmail autocomplete, or ask a question to a customer service chatbot, you can thank machine learning.
And ML will only get bigger. Looming on the horizon are self-driving cars that get better with every mile they drive. But I digress.
ML & You
How does this relate to marketing in general and content operations in particular? It’s not news to marketers that we’re inundated with data. We have mountains of it spread across our CRMs and analytics systems—not to mention the mass of content we’re continuously generating.
But for all the numbers and trend lines we pour over, we’ve yet to crack the fundamental question we’ve all been wondering about: What does our content actually say?
From our position as the focal point of a well-constructed marketing-technology stack, my engineering team at Kapost recognized that we were uniquely poised to apply ML to this deceptively difficult question.
Last year, we invested in building a data science team with the skills needed for the monumental task—no small feat given data scientists are some of the most sought-after talents in the market today. Leading the charge is Dr. Elliot Cohen, an experienced data scientist and lecturer in statistics and machine learning. Prior to Kapost, Dr. Cohen applied ML to the domain of real-time energy demand management.
Now, he’s set his sights on the world of marketing.
Dr. Cohen and his team have immersed themselves in the world of content operations, working with Kapost customers to examine what ML brings to the challenge of message consistency and personalization.
What exactly does the Kapost Data Science team have in store? Stayed tuned to find out! For more insight into the question of consistency (and how we’re working to answer it with ML) watch this webinar with Kapost’s CMO Christine Bottagaro and VP of Product Riley Gibson.