Machine learning takes content marketing personalisation to the next level

February 5th, 2018

Machine learning, artificial intelligence (AI), marketing AI, behavioural monitoring, marketing automation, rules-based solutions, algorithmic personalisation, real-time personalisation… are you dizzy yet? Technical terms and buzzwords can have the effect of making our heads spin rather than introduce an interesting concept or new technology.

Machine learning for content marketing personalisation, however, is a technology you will want to wrap your head around. Learn how it outperforms rules-based automation and can take your marketing efforts to the next level.


The basics of machine learning

Machine learning is a subset of AI. Whereas AI is the science of programming machines to do things a smart human would be able to do, machine learning consists of algorithms that can access data, learn autonomously without human input and adjust actions based on that learning.

AI is creating a virtual computerised assistance to handle your day-to-day tasks. Machine learning for personalisation is adding algorithms to make it a whip-smart personal assistant that works 24/7 and keeps getting smarter and more efficient – all on its own.

Rules-based marketing cannot compete. It cannot scale efficiently, make independent real-time predictions free of human bias or learn autonomously to provide increasingly valuable insights.

Machine learning for content marketing personalisation

Machine learning can process massive amounts of data and make predictions from that data in microseconds; all day long, every second of the day. For 2018 and beyond, this is becoming a non-negotiable part of successful marketing strategies.

Personalisation expectations have escalated and marketers are trying to keep up. The 2017 Salesforce report The AI Revolution describes it this way:

“In their quest to deepen customer relationships, many marketers have moved from simple segmentation to dynamic content strategies, powered by machine learning, to tailor communications and offers to individuals.”

Thirty-three per cent of marketers surveyed in the 2017 Real-Time Personalization Survey by Evergage use AI to deliver personalised web experiences. When asked about the benefits, 63% mentioned increased conversion rates and 61% noted improved customer experiences.

The technology is here and the time to implement is now. The question is, how?

Machine learning tools for small and mid-sized businesses

Large corporations always have it easier when it comes to the ability to implement costly new technology solutions. They have the skilled labour and budgets to create their own products and services. Apple has Siri, Microsoft has Microsoft Azure, IBM has Watson, Amazon has Alexa and Google has RankBrain.

For companies with annual revenues capping at around $7 million and less than 500 employees it can be more challenging. Is this you? If so, you can create machine learning applications with an in-house IT team working with skilled data scientists, partner with a solutions provider or outsource tasks.

Do you need information about machine learning services? Below are four examples of how machine learning is used for content marketing personalisation and eight ready-made tools a company can use to automate the tasks.

1. Automated personalised emails

For the State of AI-Powered Email Marketing Report 2017 by Boomtrain, more than 235 million emails were analysed. The report found the open rate for AI-personalised emails was 63% and increase in click-to-open rate was 100%. Those are impressive stats.

And like all forms of content, your personalised, automated emails need to be mobile-friendly. According to Campaign Monitor: “Fifty-three per cent of emails are opened on mobile devices.”

Available machine learning tools:
Boomtrain Editor automatically send emails to the right person at the right time, i.e. when they are most likely to click and open the message. Existing email newsletters can be converted into a personalised template for each customer.

Rare.IO selects products it predicts a customer will love, then sends this information in an email with images when there is a high probability they will read it and purchase. It integrates with major e-commerce platforms such as Shopify, BigCommerce and Enterprise Solutions.

2. Personalised web content

Manual tracking of data and analytics and time-consuming trial and error approaches to creating blog posts are becoming a thing of the past. In fact, content may not even be produced by a real person. In 2016, Gartnerpredicted that: “By 2018, 20% of all business content will be authored by machines.”

Automated personalisation entails delivering the right content to the right person at the right time. Machine learning algorithms improve upon that insight continuously with increasing amounts of historical data.

This gets results. In January 2017 OneSpot surveyed 1,500 consumers and found “Sixty per cent of consumers feel a stronger connection with the brand as a result of content relevance” and 86% were more interested in a company’s products and services if they delivered personally relevant content.

Available machine learning tools:
CaliberMind, a customer data platform, collects and joins data from all sources such as analytics, customer databases and marketing automation. It analyses and predicts what content will work for what audience and when.

Atomic Reach gives real-time information on what type of content to provide for what channels. Predicts the best times to publish and how often plus when to share articles on social media.

3. Personalised sales offers

Machine learning is used to analyse user intent then generate predictive sales offers along the shopper’s journey. The following is from the Digitalist Magazine article, How Machine Learning and Artificial Intelligence Can Transform Your Sales Team:

“With machine learning, your advanced cloud CRM solution can learn over time to forecast and score deals with greater accuracy, freeing up sales team members to focus on building and nurturing relationships that add value to the business.”

Algorithms are also used in sales for: inventory forecast, churn prediction, product categorisation, customer segmentation, recommendation, image recognition, trend analysis, sentiment analysis, anticipatory shipping and likely to purchase predictions.

Available machine learning tools:
Apptus eSales creates real-time personalisation across products, content and promotions to match a customer’s need. It optimises navigation on product category pages and has a recommendation algorithm for every stop of a customer’s journey.

RichRelevance, a products recommendation engine, uses machine learning to provide personalised product recommendation in real time for each customer.

4. Precision analytics

Predictive analytic algorithms are used to find patterns in data, interpret the patterns, gain insights and then make decisions. It can be used to deliver targeted content and personalised sales offers to up-to-the-minute advanced buyer personas for each customer.

Available machine learning tools:
Adobe Target syncs activities, insights and segments with Adobe Analytics. Set recommendation testing to deliver best variation in real time to the right segments, and targeted content is then delivered automatically using historical and affinity data.

Mindtree Decision Moments is an agile data analytics platform that can integrate with existing technologies. It uses more than 20 machine learning algorithms that are based on deep learning techniques to apply continuous learning algorithms to large data pools.

So, in conclusion…

Ramp up your content marketing personalisation efforts with machine learning now if you want to outperform your business rivals in the years to come. One algorithm can replace thousands of rules and is a scalable way to provide unique, individualised one-to-one experiences. Businesses are catching on and the use of AI and machine learning is on the rise.

Research by Tractica predicts the AI market will be valued at $36.8 billion by 2025. A September 2017 research report by MarketsandMarkets predicts the machine learning market will grow to $8.81 billion by 2022.

Roger Wilks,Head of Delivery, SevenC3

  Share: Posted in CMA Blog