This sponsored post is produced by Boomtrain.
Whether we realize it or not, machine learning is already a part of our everyday lives. Think about a simple Google search, a quick query to Apple’s Siri, an afternoon visit to Facebook, or, of course, a great product recommendation on Amazon.
[aditude-amp id="flyingcarpet" targeting='{"env":"staging","page_type":"article","post_id":1911501,"post_type":"sponsored","post_chan":"sponsored","tags":null,"ai":false,"category":"none","all_categories":"marketing,","session":"A"}']As the prevalence of high-performance computing continues to grow, the thought of computers doing things we could only dream of is exhilarating and, for many, almost magical. This is especially true when it comes to marketing.
Yet, as we apply machine learning to marketing and unique business goals, oftentimes all of that mystery and intrigue turn to disillusion and struggle with the practical applications. We’re left wondering, how can we bridge this gap and make machine learning work for marketers everywhere?
Machine learning and relevance-based marketing
Marketers need high-performance computing for the same reasons other industries need it: data is growing at an impressive rate. By 2020, the digital universe will grow by a factor of 300, from 130exabytes to 40,000exabytes, or 40tn gigabytes. We all know humans can’t process this much information (in fact, the human brain can only hold the equivalent of 1m gigabytes of memory).
Machines help us process all that data and produce insights without us having to tell them what to do all the time. In turn, we have the ability to learn faster from a larger set of data, and make better decisions that drive business forward.
Marketers are no stranger to data. We’ve been relying on data like Google Analytics and campaign metrics for years. Yet these tools alone can’t keep up with our data needs. Marketing analytics can tell you which campaigns brought in the highest average revenue and even the number of high-value customers, but we need to know more.
We need to know what elements are attracting the high-value (eg, the big spenders or power users) —- so we can concentrate on doing more of that, and less of the other stuff. Machine learning can help us do this, and take it a step further. For example, machine learning can automate and predict the best message based on multiple variables, not just on demographics.
It’s the cornerstone of marketing — relevance — and powerful machine learning can help us deliver relevance at incredible scale.
But with new technology comes new challenges: there’s a lot going on behind the scenes to make ‘simple’ recommendations to users. Machine learning takes a lot of expertise to get up and running, and with so much data, it can be hard to determine where to start.
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3 ways to bridge the gap
Fortunately, as innovation in machine learning continues to grow, so does the ease and range of its practical application in marketing. Let’s take a look at 3 practical ways to make advanced machine learning personalization work for everyday marketing:
1. Simplify your system
You could do all of this in-house, or even go out and run predictive models on IBM’s Watson or Google’s TensorFlow. But if you don’t have a team of engineers on staff, you won’t be able to use it for your own marketing needs. Take comfort in outsourcing this process to the data science professionals. There are tons of companies out there to meet your unique needs — from e-commerce product optimization, to predictive content for media and content companies, to B2B business intelligence systems. Do some research to find out which algorithms would best fit your business model, and ask plenty of questions on how it works.
2. Simplify your integration
It’s true, even if you outsource to experts, you will still need to get up and running with some integration. Integrations can range from full-on, tear-out, month-long, to a couple of weeks and only hours of an engineer’s time. As we advance this technology, new tools are being launched more frequently to solve integration challenges with more user-friendly tools, such as drag-and-drop editors or messenger applications. Look to leverage APIs and tools that help you forget all the noise behind the scenes, and allow you to implement predictive recommendations across channels seamlessly.
3. Simplify your data analytics
One thing we as society haven’t gotten quite used to is putting our full trust into the machines. That needs to change. In order to receive the best insights and best predictive content for users, we need to give over as much control as possible in this one area. In turn, we can take control of new areas that will drive business forward.
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For example, ditch A/B testing across campaigns. Let machine learning insert predictive content tailored to each individual in email or mobile, and simply A/B test this new personalization vs old content to verify success. With that time you could take advantage of new insights, as the beauty of machine learning is the ability to surface patterns from data that would be hidden to the human eye, revealing deep insights into customer needs and trends and new opportunities for your business.
When you can successfully apply machine learning to your marketing, you’ll find the benefits are endless. We’re in a new wave of data-driven marketing, allowing your brand to “simply” be there — at the right time, on the right device, with the right message for customers.
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