June 25, 2019
How artificial intelligence work
A few months ago, we covered a “Think with Google” event in Sydney where Google’s Fellow & Vice President of Search Pandu Nayak explored the future of search. At the event, Nayak discussed how search has grown since the early days of Google — and how it will continue to evolve as new users come online and find unique ways to use search.
Nayak also talked about how AI and machine learning (ML) will all play significant roles in the future of search. Thanks to rapid advances in technology, AI and ML are providing capabilities that have been seemingly impossible in the past. And while AI and ML algorithms have been around for a long time now, the ability to automatically generate valuable insights by applying complex calculations to big data has only been a recent development. You can find out more by watching the behind-the-scenes video below.
Today, we’ll focus on machine learning — and how it can help marketers work smarter. Widely referred to as ML, machine learning is a discipline that aims to make predictions based on data patterns using science, statistics, and coding. ML algorithms are designed to analyze data and discover patterns that people wouldn’t otherwise be able to find by themselves — in contrast, rule-based decision systems follow a specific set of instructions known by the developers in advance. Essentially, ML allows computers to learn and adapt without being explicitly programmed to do so. ML also leverages the massive power and objectivity of machines to see things in big data that humans are unable to do — and it can help marketers in many ways.
How machine learning can help marketers today
Big data is serious business. And without ML, it will be increasingly difficult for today’s marketers to compile, absorb, and analyze the vast streams of data coming from multiple sources, let alone predict what marketing message will work for each customer.
Today, the best companies are using ML to understand, anticipate, and act on the problems their customers are trying to solve — and they are doing it faster and with more clarity than their competitors. According to Forbes, 84% of marketing organizations implemented or expanded AI and ML in 2018 and of those organizations, 75% saw customer satisfaction increase by 10%. These companies understand that having the insight to personalize content while qualifying leads to close more efficiently is largely thanks to ML-based programs capable of learning what’s most effective for each lead. Essentially, ML is taking personalized marketing, marketing automation, lead scoring, and sales forecasting to a whole new level.
Here are a few more ways machine learning can help marketers:
- Customer segmentation: ML customer segmentation models can be extremely effective at extracting small, homogenous customer groups with similar behaviors and preferences;
- Customer churn prediction: by discovering patterns in the data generated by many customers who churned in the past, churn prediction machine learning forecasting can accurately predict which current customers are at a high risk of churning. This allows marketers to engage in proactive churn prevention, an important way to increase revenues;
- Customer lifetime value (CLV) forecasting: identifying your CLV will enable you to segment your customers more effectively, measure the future value of your business, and predict growth more accurately.
Machine learning best practices for marketers
The idea of getting acquainted with ML sounds scary, but it doesn’t have to be that way. Today, ML is everywhere in today’s marketing technology landscape — and there’s no shortage of opportunities to test ML-powered tools and technology. To get you started, we’ve outlined 3 ML best practices to set your business up for success:
Set clear goals
First things first. Whether you’re building your own ML model, incorporating elements of ML into your best-of-breed marketing stack, or setting up Smart Bidding in Google Ads, it is crucial that you’re clear about the aim you’re trying to solve. Is your goal to decrease the cost per conversion? Or is it to increase conversions, no matter the cost?
It doesn’t matter what your goal is; what really matters in this context is that it will naturally dictate the ML solution you’ll use, so don’t treat this step too lightly. More importantly, make this the first thing you do.
Both data quality and quantity are equally important
ML requires data to learn from. The more data you can feed your ML solution, the better it will perform. If there’s not enough data, it is very likely to underperform. Poor quality or non-specific data are also contributing factors to underperforming ML solutions; there’s not much point having lots of data at your fingertips if the data sets are inaccurate, inconsistent, and incomplete.
Like most complex systems, ML needs time to learn especially if you’ll be feeding large volumes of data. We also suggest resisting the urge to make changes too often — every time you make a change, it may take some time for the ML system to readjust as they re-learn. Patience is key — and so is adopting a long-term approach.
Power up your marketing with machine learning
Big data and technology have already transformed the way businesses communicate with their customers. More importantly, the future of digital marketing is closely aligned with AI and ML-based marketing. Given that a large number of big corporations are already benefiting from AI and ML and many SMBs are extensively pursuing a similar path, it would be worth thinking about integrating aspects of it in your marketing.