Did you know that 3 out of 4 organizations implementing machine learning increase sales of new products and services by more than 10%? And are you curious about how to apply Machine Learning for Digital Marketing? If your answer is yes, just keep reading our fresh blogpost!
The topic of using machine learning in digital marketing is highly underrated. There are thousands of articles, speeches, and best practices out there promising more effective digital strategies, an increased number of leads, and higher customer satisfaction. It all sounds so intangible and rather vague.
Yet there is something evolving in the field of technology that will change the game, but no one is talking about it. Something that’s not just a theory, but an entire branch of science supported by facts. Something that’s able to tell you exactly what you should plan with, the results you can expect, and the changes you have to make.
Yes, it’s machine learning. There’s even an old joke about it:
‘Machine learning is a lot like teenage sex. Everybody talks about it. Only some really know how to do it. Everyone thinks everyone else is doing it. So, everyone claims they’re doing it, too.’
When it comes to machine learning, everyone immediately thinks about futuristic-type products like self-driving cars or Siri. Still, it’s not just those cutting-edge companies with huge R&D budgets who are doing it. Techcrunch suggests that nearly every Fortune 500 company must be using it already.
But what really is machine learning? How can we differentiate it from artificial intelligence and deep learning? And how can we use machine learning in digital marketing?
Let’s begin with the broadest concept: artificial intelligence
Artificial intelligence refers to the use of computers to mimic the cognitive functions of humans. Machines carry out tasks based on algorithms in an “intelligent” manner.
Machine learning is a subset of artificial intelligence
Machine learning focuses on the ability of machines to receive a set of data and learn for themselves, changing algorithms as they learn more about the information they’re processing.
Deep learning goes yet another level deeper as a subset of machine learning
Deep learning is a method of replicating the dense network of neurons in the human brain. With deep learning, computers are able to handle a massive amount of data at once, just like the brain does. As all the neural pathways are interconnected in our brain, our reaction time is fast, and we are able to make connections between things. The same can be applied to computers with deep learning. By handling this huge amount of data at once, computers significantly increase their precision.
All things considered, we can think about artificial intelligence as the concept of human-style intelligence in machines, machine learning as an approach to mimic artificial intelligence, and deep learning as a successful technique within the realms of machine learning. Now that we understand these concepts and recognize the difference between them, we can move onto discussing their applications.
How can we apply machine learning in digital marketing?
The topic of content marketing has long been interesting to marketers, yet most of them are unaware of the benefits machine learning could bring through it.
User-generated content is highly valued by marketers yet it takes a lot of work to curate it… or not, if they’re using machine learning. Today’s machine learning models are able to filter out misspellings, vulgarity, spam comments, or misinformation. And they can do all this without the need of a real person to tag each piece of content.
We all know how well-informed most online businesses are about our interests, online behavior, shopping patterns, and history. By using all this data in machine learning models, most of the biggest brands are already helping customers find relevant content. Ever wondered how Pinterest, Google, AppStore, and Spotify always know what’s interesting to the user? Whether it’s about images, search results, mobile applications, or music, machine learning models are always there to recommend what’s best for your customers.
Customer support is one of the few business fields that machine learning is already able to almost fully automate. Today, machine learning models are able to recognize the substance of a customer request and route it to the right place. This, in turn, saves companies significant time and money.
According to Forbes, “57% of enterprise executives believe the most significant growth benefit of AI and machine learning will be improving customer experiences and support.”
We shouldn’t forget about chatbots either. Most of the biggest brands are already using them. They automate their customer service processes either fully or partially. Try texting Spotify, McDonald’s, Starbucks, or Wholefoods and you’ll see that they’re all using it. With the help of chatbots, companies are able to
- automate routine tasks,
- answer frequently asked questions, and
- create personalized customer care experiences without human interaction.
When was the last time you wondered why so many of your visitors don’t convert to customers? That’s a question machine learning can answer. Machine learning models can, in fact, help you predict human buying behavior.
Marketers already know that most buying decisions are not based on well-defined logic. Emotions, trust, communication skills, culture, and intuition play a big role in them. Customers often buy the same things, behave in a similar way, and follow similar intuitions.
Machine learning models can learn the customer’s shopping pattern and help create automation to prevent churn or increase conversion rates.
By analyzing customer data, machine learning models help predict future user paths. These insight help marketers run targeted promotions that yield a better return on investment (ROI).
Today, marketers collect large amounts of data, turn them into insights, and draw conclusions based on them. These conclusions are then used for decision making, and these decisions are made by humans. But what if we let machines do the job?
The amount of data marketers are faced with today is so immense that there is no human being who could analyze it all. Not to mention that humans are naturally prone to making errors. Wouldn’t it be easier if machines just did this all instead of us?
Machine learning models can help you…
- decide which segments to target. Who knows? Maybe this month, you should focus on 60-year-old grandmas instead of the usual 20-year-old football players.
- determine the channels on which your ads are most effective. What if different segments prefer different channels?
- optimize the timing of your ads. For example, sending John your push notification right when he’s on Twitter because he’s more likely to open it then.
- make ads responsive by measuring which creative works best for a given segment or search query.
Machine learning models can also help reduce customer churn with the help of streamline risk prediction and intervention models. According to the Harvard Business Review, a new customer typically costs 5 to 25 times as much as it does to retain an existing customer. That’s why having a proactive approach is so important when it comes to the topic of customer churn.
Machine learning models help you understand what’s specifically causing churn. By uncovering customer attributes such as age, gender, income, or the campaigns the customers came from, you’ll be better able to predict which kind of customer is likely to churn.
How much time do you spend each year developing your pricing structure? What if machine learning took over this burden, too?
Today’s machine learning models can help with price optimization by taking into account many significant factors such as price elasticity, customer segment, sales period, and the product’s position in an overall product line.
Machine learning can help you forecast product demand and optimize inventory, something that’s especially valuable for seasonal or trend-based supply decisions.
By using complex machine learning models, you’ll never have to worry about supply shortages or demand prediction miscalculations. This, in turn, will result in an overall improvement in your earnings, a significant reduction in excess stock, and much fewer product returns.
Customers today are faced with information overload and have shorter attention spans. They expect to immediately find what they’re looking for. Whether it’s a piece of clothing in an online store or a movie on Netflix, they want immediate results.
With the help of machine learning, Netflix provides personalized movie recommendations to its 100 million subscribers worldwide. According to their insights, most users will give up if it takes longer than 90 seconds to find a movie or TV show they want to watch. Through improving search results using machine learning, Netflix estimates that it avoids canceled subscriptions worth $1 billion annually.
Machine learning is widely adopted by businesses: 49% of organizations are looking into deploying machine learning, while 51% claim to use it already. Still, there is a wide range of issues that make deployment of machine learning capabilities an ongoing challenge. These issues include a lack of skilled people and ongoing challenges with lack of timely access to data.
“The process of deploying machine learning is roughly 20% about developing machine learning models and 80% about sorting out data. What we find is that most companies don’t have an easily accessible, scalable, clearly structured data warehouse which makes it difficult to build machine learning models for them,” points out Tamas, CTO at Aliz.
The promise of machine learning solutions is clearly fascinating; however, you first have to think about the data that lies behind it all. Don’t let your competitors leave you behind. Start building your data-driven future today!
Interested in learning more about machine learning in digital marketing?
- Watch our APAC CEO, Balazs Molnar talking about it at Tech In Asia Jakarta 2018.
- Read his latest blog post about Machine Learning in Business.
- Check out our article 5 Good Reasons to Move to a Cloud-based Data Warehouse for more on data warehousing and its importance.