Source: Deep Learning on Medium
Digital marketing requires managing various channels including: digital direct, search, email, social and websites. Company media managers are always looking for better tools to improve their marketing efficiency. A now familiar, but often not well understood tool, is machine learning (ML). ML is gaining traction as a top tool for realizing cost optimization, trend recognition, and task automation.
However, adopting an ML solution does not guarantee immediate improvement to your CPQL ROAS or CPC. You need to understand how machine learning can help your digital marketing efforts and come up with a strategy that works for your objectives.
Understanding the Basics of AI, ML, and DL
When you are dealing with machine learning, you’ll frequently run across the terms — artificial intelligence, machine learning, and deep learning. Let’s take a quick look at what AI, ML and DL entail:
- Artificial Intelligence (AI): The goal of artificial intelligence is to create intelligent machines. It is a broad discipline that encompasses many areas of research. One example is:
◦ eCommerce Fraud detection and prevention systems
- Machine Learning (ML): Machine learning is a sub-discipline of AI. It uses prior organized data to train machines to create models. Then, it uses those models to autonomously provide solutions for new problems. Twitters curated timeline is an example.
- Deep Learning (DL): Deep learning can be considered a subset of machine learning. In deep learning, machines use artificial neural networks to create models from raw data and provide solutions for new problems. A good example is Netfilx’s content recommendations.
In the digital marketing arena, machine learning and deep learning can get used interchangeably. In machine learning, data scientists have to spend time classifying the input data to help the machines learn. In deep learning, the process of learning is more automated. Some problems are better solved with machine learning, while others work better with deep learning. In this article I will use the term machine learning for both disciplines.
Machine Learning and Digital Marketing
Now that you have a basic understanding of ML and its relationship to AI and DL, let’s look into how machine learning is helping digital marketing.
Marketers have to spend a lot of time analyzing data to run effective ad campaigns on digital platforms like Google, Facebook or Amazon. They have to collect metrics for pay per click (PPC) campaigns and calculate the return on ad spend (ROAS). Today machine learning algorithms are helping analyze data and provide better quality suggestions for digital marketing. The advertising platforms themselves are promoting effective ways to use machine learning. For example, Google is encouraging digital marketers to use ML-enabled Dynamic Search Ads for AdWords.
SEO and Content Optimization
Marketing directors need to master search engine optimization (SEO) and content management to show up on search results. But A/B testing SEO, content, and website layouts are resource-intensive. Machine learning algorithms are helping digital marketers with these tasks. As a result, you can personalize and optimize the content faster. It also makes your content more scalable. The tools allow for personalize for larger audience segments.
Customer Segmentation and Discovery
Today organizations of all sizes collect data about their customers. But current customer segmentation analysis or new customer discovery processes are expensive. They require a lot of human labor. Machine learning platforms are automating the processes and creating new opportunities for digital marketing.
Chatbots and Improved Customer Service
Chatbots are not a new phenomenon. Websites have previously used chatbots to answer simple customer questions. But machine learning is helping chatbots learn and answer more sophisticated queries. Also, these bots can analyze customer data and provide better recommendations. They are available 24×7 to help customers. It’s leading to better user experience (UX) and customer service.
Brand Enhancement through Augmented Reality (AR) and Computer Vision
Machine learning combined with augmented reality is helping digital marketing leap into the real world. Marketers are incorporating ML-powered AR to display price and product details through a customer device in real-time. Businesses are creating standalone AR apps to increase brand awareness. Brands managers are also using computer vision and machine learning algorithms to identify their own products on various social platforms to provide marketing promotional content recommendations.
The right price point can help product managers sell more products. But setting price points has always been difficult due to the myriad factors that need to be considered. Machine learning is making it easier. In fact, now you can take advantage of ML-based applications to forecast demands more accurately and use dynamic pricing for your products. Data scientists are building predictive models using ML to find incremental margin and identify the next trend.
Social Media and Email Marketing Automation
Today social and email campaigns are essential for digital marketing. Personalized social and email replies can create long-lasting relationships with customers. But manually answering every social post or email is difficult and labor intense. Machine learning is helping these marketers automate the process. The algorithms can learn about customer preferences and customize various aspects of the marketing effort. For example, an ML-enabled platform can send an email to a customer early in the morning because that particular customer is more likely to open emails during that time period.
Working with Voice Search
As voice-based search platforms like Amazon Echo and Google Home gain prominence, digital marketers need tools that can help with the complexity of voice applications. Natural Language Processing (NLP) uses machine learning to understand human speech. ML-based tools are more appropriate for dealing with the complexities of voice-based content. The raise of Alexa certainly support the broad and affective application of well deployed ML.
Recognizing Trends and User Behavior Changes
Digital marketers can use machine learning to notice trends and user behavior changes in digital content. They can find niche markets through ML-based analysis. Machine learning can also be used to predict customer attrition and adjust marketing budgets accordingly.
Reaching Global Markets
Localization of marketing materials has always been a challenge. Translation of content into various languages take time and money. But ML-based translation engines are getting better. Now digital marketers can translate for a global market without significant upfront investment.
Machine Learning Approach Needs to be Practical
ML-based applications can help digital marketing. But it’s also important to understand that implementing a machine learning solution requires various resources:
- Data scientists need to prepare training data for machine learning algorithms.
- Hardware and software have to be set up for the applications.
Even if you use third-party solutions, the cost can be high. So you need to take a look at the application and figure out if it will provide enough benefits for your business to justify the costs. For some things, humans are still cheaper. But for other tasks, machine learning can help you leap way ahead of your competition.