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It's time for marketers to become AI native by incorporating Artificial Intelligence (AI)*, Machine Learning (ML)* and Deep Learning (DL)* into their role. Let's see how marketers can use this new technology to help them to stay credible, accountable and current.

By 2023 businesses are expected to be spending over £87 billion on AI platforms*. This will require organisation-wide adoption of AI and necessitate that marketing roles become AI tech-based.

Becoming AI native marketer
No matter your marketing field or seniority – now is the time to build your competency around AI. Here are some examples of how AI can be deployed in marketing.
 
Build marketing strategy
 
To create effective marketing strategy, marketers need to analyse various internal and external data sources to understand how customers perceive brand and what they are looking for. AI coupled with ML can be used to find patterns and understand trends in large volume of data to gain meaningful insights to build marketing strategy
 
Create intelligent content
 
AI tools can help to curate content for social media or display blogs based on recently read articles
AI can also generate content like emails, ad copy or personalised messages. Some examples of content automation tools are Word.ai, Articoolo, Persado and Scoop.it. 
AI-powered chatbots can spot recurring problems, and predict what’s causing issues for a user faster and better than humans. They not only provide assistance but also come up with suggestions on product/service customer needs to solve their problem.
 

Achieve personalisation and optimal customer experiences

You can use AI to automate personalisation. As a result, your website visitor could see the most relevant content and offers based on their location, demographics and browsing history.
Brands are using predictive analytics to process customer data and serve personalised marketing messages to increase customer engagement and spend.
 
Manage cross-device and cross-channel promotions
 
AI tech are enabling companies to identify and target customer on different devices so that the message can be read or sign up process completed at the right time and the right place for each customer.
Brands are using augmented reality to show their products  in 3D and bridge the gap between offline and online experience.
 
Real time forecasting 
 
AI-enabled real time forecasting relies on algorithms that take massive volume of data from various sources and create optimal  forecasting. For example data from internal CRM, ERP, IoT and other systems, as well as external information such as partners, market intelligence, social media to name a few. AI adapts in real time and takes into account daily, weekly, yearly or seasonal variances and learns to minimise risks and capitalise on opportunities.
 
Upskill to enhance your career
 
Marketers need to leverage AI in their marketing activities. It doesn’t mean that you need to become data expert. Instead be sure to speak the language of DL, ML, and AI to collaborate with data scientists.  Data science is largely centered around coding, so you might give it a go or get your junior team members or even your kids inspired. Please read the article: 8 Reasons Coding for Kids is Not Just Another Fad here
Alternatively, consider upskilling and
learn to do advanced data analysis, and some visual machine learning to become marketing analyst. The opportunities are out there, so make the most of them. 
 
Do you have examples of using and implementing AI in your marketing activities? Let’s talk on LinkedIn or Twitter
 

*According to the recently updated International Data Corporation (IDC) Worldwide Artificial Intelligence Systems Spending Guide, spending on AI systems will reach $97.9 billion in 2023.

*DL is subset of ML where systems can learn hidden patterns from data by themselves, combine them together, and build much more efficient decision rules. 
*ML is subset of AI that involves programming systems to perform specific task without having to code rule -based instructions.

*AI – Any system that leverages human capacities for learning perception and interaction at a level of complexity that ultimately supersedes our own abilities.