What do stock trading, online fraud detection, chatbots, self-driving vehicles, targeted emails, and HELLO Barbie have in common? They all rely on machine learning to continuously improve their functionality.
It started in the 1950s when IBM tech, Arthur Samuel, developed a checkers playing program and coined “machine learning” in 1952. It used a “minimax” strategy which minimized the possibility of loss for each move. Eventually, the program “learned” the right moves to beat a human checkers champion.
Today, machine learning has improved how we live and work in almost every industry. For example, Amazon reports that personalized product recommendations derived from machine learning are responsible for generating 35% of their total revenue. Their cloud service, Amazon Web Services (AWS), uses data collected from ML. They use it to customize services to their customers, who also benefit from the marketing insights.
This post will highlight where digital marketers and eCommerce companies are taking advantage of machine learning in content, social media, PPC, email marketing, and SEO. If your marketing has not yet incorporated ML, discover how it can help your optimization, personalization, and analytics. To learn more about AI and machine learning in eCommerce, check out this resource on how it can improve site search.
What is Machine Learning?
Machine learning is computer science, a section of artificial intelligence (AI) that uses data and algorithms to mimic humans’ learning. ML gradually improves its accuracy through training and practice as the human brain.
ML computer algorithms can improve automatically through experience and by applying data. Machine learning algorithms start by building a model based on sample data, called training data. Algorithms use the data combined with more experiences to make predictions or decisions without being explicitly programmed. In other words, it’s self-taught.
Why is Machine Learning Important to Marketers?
ML helps solve digital marketing problems by sifting through customer data to find new insights and streamline marketing processes. One of the essential uses of ML in marketing is that it reveals trends in customer behavior. Companies can develop new products, target audiences more effectively, and make relevant offers that drive more business using this data.
Machine learning has become an essential part of retail supply chain logistics, helping managers optimize inventory for better cash flow. Other valuable insights include resource planning, risk mitigation, customer satisfaction, determining transaction costs, and transportation costs.
Marketers’ most common use for machine learning is finding patterns and predicting future customer behavior. They rely on it for audience segmentation, cost-effective media buying, process automation, optimization, and personalization. So let’s look at specific areas where marketers can leverage this kind of data.
6 Ways Machine Learning Can Boost Your Online Marketing
With machine learning algorithms, you can identify patterns and create predictive models. The information helps marketers provide relevant content, products, and offers to the right audience. Here are some of the ways ML enables you to stay ahead:
1. Predictive Analysis and Recommendation Systems
Predictive analytics uses historical data and analysis to predict future outcomes. It can provide reasonably accurate insights into shopping patterns for the general population or individuals.
For example, email service provider Klaviyo has integrated machine learning predictive analytics into their platform. Once marketers have a minimum data set from 500 shoppers, they can see individual profile data such as:
- Historic CLV (Customer Lifetime Value)
- Predicted CLV
- Total CLV
- Churn Risk
- Average Time Between Orders
- Predicted Gender
The Expected Date of Next Order is remarkably accurate, made possible by machine learning.
Predictive analytics is data-backed information that business leaders need to reduce decision-making risk. Machine learning can identify early trends in customer behavior that lead to better products and improve marketing choices.
2. ML Improves Chatbots and Customer Experience
82% of consumers state that it’s “very important” they receive an instant response when contacting a company.
While speed is essential, delivering the correct information is critical. ML chatbots improve with every new query. When set up and programmed correctly, chatbots provide the correct answers and eliminate human error. An additional advantage is that chatbots are always friendly, upbeat, and don’t get emotional. It’s beneficial when the customer is agitated.
Natural Language Understanding (NLU) is essential for chatbots to understand and respond correctly. Chatbot developers use a Bot Training Program to expedite the progress of getting bots up to speed understanding a company’s products.
For instance, if the customer asks, “Can I get a compatible battery for my Dell Inspiron 15.6 Laptop?” The bot must understand the question and the products. Multiply this by the number of products or components in the catalog, and you can see that it takes time to get the bot trained. Once set up and trained, chatbots speed up customer service and other processes.
After training and continuous machine learning, the bot can perform up to the standard of a live agent handling frequently asked questions. They can transfer calls to a live agent at the appropriate point in the conversation.
Other marketing functions they can do include sending follow-up emails or messages.
3. Improve Content Creation and Curation
Think of how the autocorrect feature works when texting on your phone. The more it’s used, the more accurate and helpful it becomes.
Content and curation tools help marketers keep up with the constant demand for new content on a larger scale. Some popular tools include Vestorly, Flipboard, Pocket, and Curata. These tools save users hours coming up with fresh and unique content. However, more development is needed before the software can replace human writers for quality, engaging articles.
The advantage is that these tools can track consumer trends quickly, helping marketers create relevant content for their target audience.
4. Improve Website UI/UX
Web designers have already found machine learning invaluable for providing a better User Interface. While it continues to evolve, there are four primary areas where ML improves UI for retailers. They are:
- Personalization of Content and Other Optimizations – ML can automate the UI process. For example, it can change a homepage based on a shopper’s history, offering products that will most interest that person.
- Scanned Image Recognition – When a customer scans an item using a mobile app, an ML program can identify the image and then offer the same or similar items for purchase.
- Adaptive UI – Adaptive user interfaces can improve web design by hiding irrelevant content and displaying what an individual shopper wants. Examples include ads that appear based on shopper’s search or eCommerce sites displaying products based on previous searches.
- Voice Controlled Interfaces – Voice UI programs like Siri or Alexa are two of the most recognizable ML virtual assistants. They continue to improve and recognize speech patterns the more people use them.
As part of user interface design, machine learning creates a more personalized and efficient user experience. Machine learning technology boosts a brand’s image while catering to the needs of its customers. To learn more about UI/UX design, check out this resource on conversion focused web design.
5. Boosting SEO / PPC
AI and ML tools can distinguish search patterns across thousands of data sets. They deliver insights to the most relevant content and keywords to help brands improve their SEO rankings. SEM Rush, Hubspot, and Moz are some of the most popular tools.
Google adopted ML for its SEO algorithms. For example, their Caffeine update changed the traditional method of indexing content, relying on fresher content instead. To keep up, SEO companies developed ML-powered tools that collaborate with Google’s SEO, providing custom search engine solutions.
PPC campaigns have become more cost-efficient using machine learning software. For example, the PPC advertisers who use the “Smart-Bidding” feature in Google Ads can significantly reduce their CPC (Cost Per Click). It helps predict bid amounts vs. potential conversions for the various targeted keywords by sorting out the most relevant and cost-effective keywords for a PPC campaign.
6. Social Media
ML and AI are valuable tools for marketers in all social media channels since it’s where customers generate the most data by sharing products, purchases, and reviews. Optimizing for social media is what ML-enabled listening tools do. They provide insights into what customers think about the brand from sifting through tremendous amounts of data, keywords, mentions, and posts. Marketers can use the data to provide engaging content, improve lead generation, and manage a brand’s reputation.
Machine learning helps digital marketers understand their target audience better, allowing them to personalize the communications.
Machine learning and AI can transform a company’s digital marketing strategy. However, it’s only one piece of your marketing puzzle. Take time to integrate it in the primary areas that best serve your needs. Some considerations are the cost of the software, time to deploy, training time, and its accuracy. One report stated that 80% of companies that adopted AI or ML models took six months to integrate.
In an experimental training of 14 data sets, Akkio’s AutoML took 1 minute, Google AI Platform took 63 minutes, and Microsoft’s Azure AutoML took 96 minutes. Be sure that the ML solution you choose will improve your marketing enough to offset the time and cost of integrating the solution.
Take your time, shop around and try before you buy. If you consider adopting ML as part of your digital marketing strategy, let us show you how AI and ML will save your company time and money.