The Definitive Guide to Machine Learning for Marketers in 2017

machine learning for marketing

Machine learning is the future of marketing, but what can you do today to apply it and get an edge over your competitors?

There is a lot of buzz around machine learning and artificial intelligence.

It seems everyday writers and experts are publishing articles and talking on the radio about how artificial intelligence and machine learning are going to change the world.

Likewise, trending topics – such as self-driving cars and big data– have our piqued our collective interest in the potential of artificial intelligence and machine learning.

The truth is, however, that we are in the early inception stages of machine learning and artificial intelligence. We have yet to fully realize how these technologies will affect our daily lives.

According to researchers at RMIT, the future of work will be radically disrupted by ubiquitous automation. Imagine having an army of intelligent assistants doing all your menial tasks – freeing up your time to think creatively and strategically.

This future might sound far off, but this scale of automation might only be a decade away according to Forbes magazine. Similarly, a joint study conducted by WARC and Deloitte Digital, has shown that 58% of CMOs believe that artificial intelligence will be a competitive factor for most businesses within the next four years.

Machine learning is being used by a variety of organisations to achieve innovative and exciting things. IBM and Sesame Workshop have teamed together to create toys for early childhood that adapt to learning styles and change according to each child’s aptitude.

The Imperial College of London is studying the applications of deep learning to analyse and predict crime. And, universities, such as the University of Southampton, are now teaching cognitive computing modules in various disciplines such as chemistry, medicine, arts and business.

Before we find out how marketers are applying machine learning in 2017, let us, first of all, dispel some myths and misunderstandings regarding artificial intelligence, machine learning and deep learning.

What is machine learning? How does it differ from artificial intelligence?

machine learning

‘Machine learning’ and ‘artificial intelligence’ are used interchangeably, often leading to confusion. Let us refer very briefly to business and technology expert Bernard Marr to get our terminology right before we go on.

Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”.

Machine Learning is a current application of artificial intelligence based around the idea that we should really just be able to give machines access to data and let them learn for themselves.

Deep Learning focuses even more narrowly on a subset of machine learning tools and techniques and applies them to solving just about any problem, which requires “thought” – human or artificial.

Given each of these concepts are inter-related we will briefly touch upon artificial intelligence and deep learning throughout the article. Howwever, we will mainly be discussing the impact of machine learning on marketing.

Why is machine learning important to marketers?

In the near future, a lot of day-to-day marketing activities, such as keyword research, Ad Words campaign management, copywriting and online monitoring, could be delegated to smart machine learning software.

This type of automation is the early stages of programmatic marketing – the algorithmic purchase and sale of advertising space in real time. According to research and advisory firm Forrester , programmatic marketing is expected to account for 50% of all advertising by 2019.

By 2027 all you might have to do is instruct your interface whom your client is, what type of campaign you are running, and the purpose of the campaign and the rest will be done for you.

Assuming Ad Words is still around in a decade, your computer could find optimised keywords, generate content, monitor the campaign in real-time and then send you updates and strategic suggestions.

Similar to how trading software has become an indispensable tool for stockbrokers, machine-learning-driven marketing technology has the potential to become universal in the workplace.

All this speculation is fine and dandy, but what you probably want to know is – how available is this technology today?

Well, machine learning is everywhere. If you have used a search engine or social media today, you have probably already used it. There are also plenty of third-party software developers utilising the power of IBM’s Watson super-computer to create next level analytic and marketing automation software.

In the next section, we are going to uncover some examples of how marketers are using machine learning powered technology to gain an edge over their competitors.

How are marketers applying machine learning in 2017?

In the world of machine learning, there are four major organisations pushing the technology into the future. They are Google, Facebook, IBM and Amazon. In addition to these four businesses, there are hundreds of third party software developers competing to make ever more powerful and accurate marketing tools.

Below are four ways that machine learning technology is being implemented to aid marketers. With each topic, we have included some case studies and examples.

1. Sentiment analysis and client monitoring

Online monitoring is nothing new. Marketers have been using change detection and notification services like Google Alerts and Mention since 2003. However, the advent of machine-based learning has led to more advanced analytics and monitoring.

The most advantageous of these, especially for public relations coordinators, is the growing development of sentiment analysis. By applying natural language processingtext analysiscomputational linguistics, and biometrics, marketers are able to determine whether content is positive, negative or neutral.

This new form of metric is helpful for public relations practitioners looking for quantitative ways to measure and evaluate campaigns and general public perception. There are a variety of free and paid online tools available you can use today; follow this link to find out more about them.

Example 1: Crenshaw Communications’ smart client monitoring

Crenshaw Communications are a New York-based PR agency that use AI sentiment analysis tools – such as Hootsuite Insights and Google Alerts regularly. As director Chris Harihar says, “You increasingly need AI for both the tracking and analysis, especially for larger clients with high coverage volume. Although there is still progress to be made in terms of accuracy, Sentiment analysis is the holy grail of analytics.”

 

2. Natural Language Processing and Speech Recognition

Speech recognition and voice-to-text technology has sprouted in the last few years due to the power of machine-based learning. Natural language processing tools such as IBM Watson Speech to Text and Google Cloud Speech API are being used to unlock searchable audio content.

Example 2: Shift Communication’s utilisation of natural language processing

As Chris Penn of Shift Communications says in conversation with digital marketing magazine Digiday, “Think of everything we listen to in marketing: Conference calls, speeches and presentations, et cetera, and how much of that knowledge is locked away from search. We use speech recognition to turn client calls into transcripts, speeches into blog posts and so much more.”

Natural language processing also helps marketers distil immense amounts of content into bite-size pieces depending on what information you are looking for.

Yet again Chris Penn illustrates how he applies the technology at work, “So much media is created every day, and we humans can’t read it all, but the machine can. For instance, I was analysing content for a client’s trade show this morning, and there were around 2,000 pieces of content around the show. I couldn’t read every article on my own, but I ran natural language processing on those articles in 15 minutes.”

 

3. Predictive analysis and lead generation

Analytics is everywhere – from Google to SEMRUSH, marketers use analytics to inform everything they do. Machine learning is pushing the boundaries, allowing analytic software to compile smart observations from large data sets at lightning speed and in real time.

One of the most powerful and commercially available analytics engines employing machine learning is IBM Watson. Watson is the AI supercomputer behind a lot of groundbreaking software being developed by marketers. Depending what you need Watson for, you can have access to this powerful data analysis and visualization software for a small monthly subscription fee.

Below are some examples of companies using IBM Watson to inform their marketing decisions.

Example 3: Condé Nast using IBM Watson to find brand ambassadors

In 2016, Condé Nast, an American mass media company that owns brands ranging from Vanity Fair and to GQ, partnered with IBM Watson. The intent was to make ‘data-first influencer platform’ software that could give insights about who to target their campaigns towards and what celebrities to approach as a brand ambassador.

Say they were looking for an ‘empathetic’ brand ambassador, this machine learning powered software could be used to analyse the content of potential ‘influencers’ to find the right fit.

Example 4: Rocket Fuel using deep learning to check for appropriate ad placement

Rocket Fuel is an AI-powered marketing business founded by ex-Yahoo employees in California in 2008. They are currently working with IBM Watson to use machine learning to find ways to prevent inappropriate ad placement.

If you recall, earlier this year Google’s Display Network skirted controversy when high-profile brands were inadvertently linked to extremist content. Some of the brands that were affected included Audi, L’Oreal and Marks & Spencer’s.

Rocket Fuel’s new software could be the solution to this problem. Allowing your business to mitigate the risks of inadvertent ad placement.

4. Improving customer service touch points

Improving customer service

 

Many companies nowadays are strengthening their customer service by integrating chatbots on their mobile and digital platforms. According to data from the 2016 Aspect Consumer Experience Index, 44% of U.S. consumers prefer chatbots to human consultants, and that number is growing as the technology improves.

Companies like Digital Genius (above) are using natural language processing (NLP), a branch of AI, to augment existing customer support.

NLP combined with deep learning is also being used to analyse conversations in real time. Potentially, in the near future, you could be sitting at a computer talking over the phone with a client about insurance, and instead of having to bring up individual policies, your system will automatically overhear and put the information in front of you.

Example 5: Macy’s 24/7 customer service hotline

In 2016 Macy’s announced they were testing a new mobile service where shoppers can ask questions about their products, services and facilities. This new service was provided by Satisfi, a software company that accesses Watson from the cloud to provide shopping assistance in multiple languages.

Since 2016, other shopping centres such as the Mall of America in Bloomington, Minnesota have implemented similar chatbots to provide 24/7 customer service to great success.

Example 6: GlaxoSmithKline’s ‘smart ads’

Pharmaceutical giant GlaxoSmithKline (GSK) is producing ‘smart ads’ that respond when asked questions via text or voice.

Senior brand manager of the Cough and Cold division at GSL, Jason Andree told Computerworld, “Watson provides a very personalized experience. If you’re sick, through Watson, you can ask a question and it will provide a personalized response.”

5. Automated design and content generation

Machine learning has the potential to revolutionise content and design by automating the process completely. Adobe has already announced plans to develop AI software that can automate web design.

As well as automated design, machine learning has developed to the point where AI software can automate content. Idio is one of many platforms that allow you to monitor customer behaviour, predict their interests and automate appropriate content.

Example 7: The Grid’s artificially intelligent website design platform

The Grid is using AI technology to make websites that design themselves based on the type of content you feed it. The Grid is available commercially and uses intelligent image recognition and cropping and algorithmic palette and typography selection to create beautiful looking automated web designs that match your content.

Example 8: Word AI’s automated content spinner

Spinning content is nothing new. In the world of SEO, content is like oxygen. However, it requires a tremendous amount of resources to develop new content. With AI-powered spinners like Word AI, you can take existing content and scramble it into unique content.

IBM Watson’s powerful natural language processor allows Word AI to create much more reliable automated content than ever before. The technology is still not perfect; spun content will still need a human eye to check for logic and grammar.

Example 9: Wordsmith Financial PR News Releases

Wordsmith Financial

 

Wordsmith Financial

 

Wordsmith is an AI product developed by tech-company Automated Insights. At the moment the software is capable of turning large amounts of financial data into easily readable press releases.

Over the next few years, it is expected that the technology will be able to automatically generate general press releases. However, as it is still early days, generating that level of personalised content is still out of grasp.

As Chris Penn of Shift Communications says, ““If an agency is claiming to have created some sort of a press release stack that automates release writing, I would call it bullshit.”

6. Automate your marketing and improve customer relationship management

Machine learning and AI solutions offer an opportunity to automate a variety of simple and repetitive marketing tasks. This gives marketers more time to focus on creative ideas.

Third party platforms such as Marketo are already using predictive analytics to cater content to users, as well as sales forecasting to identify potential clients and predict user behaviour.

Example 10: Salesforce Einstein uses artificial intelligence to make customer relationship management smarter

Salesforce’s Einstein is an artificially intelligent platform that adds a smart layer of to the entire Salesforce platform.

Salesforce has compiled a team of 175 data scientists to build their own smart platform.  Using machine learning and data mining, they are able to make predictions about customer behaviour and identify key customers within the sales pipeline.

Summary and key points

Machine learning and artificial intelligence have the potential to revolutionise your work life. You potentially have the power of a marketing team at your fingertips.

Here are our key points to take away with you:

  1. Machine learning is a field of artificial intelligence where algorithms use data to optimize their predictions and performance over time.
  2. Machine learning and artificial intelligence are being used today to automate the workflow of marketers.
  3. There are hundreds of third-party AI platforms available today to optimise your workflow through predictive analysis and automation.

Hopefully, this article has dispelled some myths and clarified your understanding of how machine learning relates to marketing in 2017.

Do you use machine learning optimised tools in your workplace? Let us know about your experiences below. If you enjoyed reading about machine learning and want to find out more about how you can utilise automation, try reading this article – 7 Things You Should Know About Marketing Automation.

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