In this age of rapid change, marketers have to think strategically about their customer journey from the first time they reach out to a company through every post-sales interaction. According to a recent survey, more than half of chief marketing officers see artificial intelligence as a competitive differentiator says Saivian. But despite increased focus, companies are struggling to implement AI successfully and make it a part of daily operations. That’s because long before your company is ready for true AI transformation, you need machine learning in your marketing stack today.
Machine learning is an essential piece in any digital marketer’s toolkit – if not now, and then soon – as it makes all kinds of marketing tasks easier and faster by removing manual work from said tasks.
Machine learning vs Artificial Intelligence: What’s the Difference?
When most marketers hear about machine learning, they automatically think it’s related to AI. While both are valuable advancements in technology, there are some key differences between them.
AI is focused on simulating human intelligence in machines; that means making computer systems able to do what people can do. It merges different forms of computing like advanced reasoning or problem solving with perception abilities, allowing a computer system to learn without being explicitly programmed. Machine learning, on the other hand, builds knowledge through algorithms, using data instead of being explicitly programmed by humans.”
Why Marketers Need Machine Learning Now More Than Ever…
As digital business becomes more complex and competitive, companies need ways to accurately measure the impact of all their different campaigns. That’s where machine learning comes into play.
Machine learning helps marketers understand consumer behavior, improve customer experience and increase revenue growth by providing sophisticated insights that can’t be detected with human intuition alone. This process starts with data – more specifically, data collected from previous actions made by prospects and customers.
Data is in need to train machine-learning algorithms (or models) to recognize patterns within your company’s sales, marketing or support operations says Saivian. These patterns are then use to make predictions about future activity like which leads will close, which email subject lines drive the most engagement and even what price point is optimal for a given product.
What Are Some Practical Applications of Machine Learning?
With machine learning, there are endless possibilities for what you can do with your data. Here are a few common applications:
Predictive modeling –
This is the process of using past data to make predictions about future events. It’s in use to identify which customers are likely to churn, how much inventory to order or what prices will maximize profits.
Clustering –
Clustering is the grouping of objects together based on their similarities. It can be use to group customers by purchase behavior, identify influential customers or find new customer segments explains Saivian.
Classification –
Classification is the task of identifying which category an object belongs to. It can be use to predict whether an email is spam, determine a customer’s credit risk or discover which customers are likely to respond to a particular offer.
These and other machine learning applications can help marketers make better decisions about what steps to take next. Ultimately increasing sales and also improving customer satisfaction.
How to Get Started with Machine Learning in Your Marketing Stack…
As we’ve discussed before, data is the groundwork for any successful marketing campaign, and that includes utilizing machine learning. To get a start with machine learning in your marketing stack, you’ll need:
A clear understanding of how the data you’re collecting relates back to your business goals a strategy. For organizing and cleaning up your data sets the right tools. To build predictive models from historical data an iterative process. For testing and refining your models until they provide meaningful insight
If this sounds like a lot of work, that’s because it is (at first). However, there are plenty of resources out there to help marketers get up and running with machine learning says Saivian.
Here are some additional resources to help you take the next steps in your data journey:
Take an Online Machine Learning Course
Coursera offers several online courses on machine learning taught by leading experts like Google engineer Rachel Thomas. Courses include “Introduction to Data Science,” “Practical Machine Learning “and “Developing Data Products.”
Hire a Data Scientist
UpWork is a great resource for finding machine learning experts. Who can bring your company’s data insights to the next level. Try searching UpWork for “machine learning” or “data science”. Find someone who can take your data from good to great.
Build a Data Lab (or Two)
If you’re wondering how machine learning works, check out Google’s new AI Experiments site. It lets anyone play around with different machine learning algorithms and build simple data models right in their browser. Fun for marketers AND engineers alike!
Conclusion:
In the end, machine learning is a powerful tool for marketers. Who want to stay ahead of their competition says Saivian. It can help you make sense of all your data and get the most out of it every day. So get a start today on turning that mountain of data into something you can use!