Marketing Applications Of AI

In my latest blog, I discuss the importance of AI and how this growing trend can be applied to marketing.

Artificial Intelligence is one of the biggest trends in technology in marketing, and across business. Many have asked; does AI spell the end for marketing jobs? For me, it's an opportunity for marketers to learn new skills and techniques, and in this article we'll be taking a look at the marketing applications of AI.

I'm in the 'business as usual' camp in this chart. For now, AI is just a new form of tech for marketers to understand and manage to get an edge. 

How can AI be used in marketing?

There are so many ways in which AI can be used in marketing that this is a difficult question to answer! Since I like to think in a visual way, I have worked with Rob Allen to create this infographic covering the potential uses of AI in marketing.

By thinking through the applications of marketing through the customer lifecycle, businesses can structure a plan of what is most valuable. Here's a summary of the applications:

• AI generated content - this is a really interesting area for AI and you could see that copywriters and creatives may eventually come under threat, although not just yet. You may know the case of the agency who devised an AI powered creative director, but humans often preferred human engagement ideas.

However, if you look at Wordsmith from Automated Insights, you will see writing programs which can pick elements from a dataset and structure a ‘human sounding’ article.

• Smart Content Curation for Personalisation - this is an extension of the 'collaborative filtering' personalisation techniques used by Amazon and other retailers for many years already. Since then, many personalisation services have mainly functioned by rules-based, IF audience X visits this page, THEN show them this this offer. Managing such rule sets quickly becomes unwieldy with hundreds of potential variables about visitors, so many of the high-end personalisation systems are now offering this and in future they may become affordable for more businesses. 
  
  
• Voice Search - you will certainly have heard of the growth of digital assistants and voice search on mobile. It has been estimated that more than 50% of searches will be voice based by 2020, but this estimate sounds like too much too soon. 

When it comes to using it for marketing, this is about utilising the technology developed by the major players (Google, Amazon, Apple) rather than developing your own capability.

• Programmatic Media Buying - here, propensity models generated by machine learning algorithms are used to more effectively target ads at the most relevant customers. 
  
  
• Propensity Modelling - propensity models can be applied more widely across the lifecycle using machine learning. This is a key technique. It's worthwhile marketers learning more about it, since much marketing use of AI is based on this. The general principle is that a machine learning algorithm is fed large amounts of historical data, and it uses this data to create a propensity model which is able to make accurate predictions about how people will respond to future promotions, so that messages can be better targeted.
  
  
• Predictive Analysis - propensity modelling can be also be applied to a number of different areas, such as predicting the likely hood of a given customer to convert, predicting what price a customer is likely to convert at, or what customers are most likely to make repeat purchases. 
  
  
• Lead Scoring - propensity models generated by machine learning can be trained to score leads based on certain criteria, so that your sales team can establish how 'hot' a given lead is, and if they are worth devoting time to. Many marketing automation providers are working on these techniques.
  
  
• Ad Targeting - we have seen that machine learning algorithms can run through vast amounts of historical data to establish which ads perform best on which people and at what stage in the buying process. Using this data, they can serve them with the most effective content at the right time. Google AdWords has included options within AdWords for this for some time. 
  
  
• Dynamic Pricing - dynamic pricing can target special offer prices only at those likely to need them in order to convert. Although, there are legal and ethical problems that businesses using this approach have run into. 
  
  
• Web and App Personalisation - using a propensity model to predict a customer's stage in the buyer's journey can let you serve that customer, either on an app or on a web page, with the most relevant content. If someone is still new to a site, content that informs them and keeps them interested will be most effective, while if they have visited many times and are clearly interested in the product, then more in-depth content about a product's benefits will perform better.
  
  
• Chatbots - chatbots mimic human intelligence by being able to interpret consumer’s queries and complete orders for them. Many chatbots are being developed in Facebook's Messenger.
  
  
• Re-targeting - much like with ad targeting, machine learning can be used to establish what content is most likely to bring customers back to the site based on historical data.
  
  
• Predictive Customer Service - here, AI is used to work out which customers are most likely to unsubscribe from a service, by assessing what features are most common in customers who do unsubscribe. It's then possible to reach out to these customers with offers, prompts or assistance to prevent them from churning.
  
  
• Marketing Automation - marketing automation techniques generally involve defining a series of rules, which when then trigger interactions with the customer. But who decided these rules? Generally, a marketer who is essentially guessing what will be most effective. Machine learning can run through billions of points of customer data and establish when are the most effective times to make contact, which offers and words in subject lines are most effective.
  
  
• Email Personalisation - in a similar fashion to web-based personalisation, applying insights generated from machine learning can create extremely effective 1:1 dynamic emails.

You can see from these 15 applications, that almost all areas of marketing will be touched by AI and machine learning. However, deploying these systems will need to be selected, customised and their effectiveness reviewed.

Many of these techniques we have reviewed, like personalisation and marketing automation, are not new. Research shows that businesses often fail to set up the more advanced features. Although AI potentially requires fewer human rules, such systems are far from being 'plug and play'. So I believe our jobs our safe for the foreseeable future.

Now we have reviewed applications of AI at a top-level, we will drill down in my next article to review how AI can be used within search marketing.

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