Artificial Intelligence in Digital Marketing

Xin Li

Artificial Intelligence (AI) and big data are commonly discussed topics nowadays as they help businesses to understand market needs and trends through analysing marketing data. From this, businesses could then make real-time and well-informed decisions faster.

Analytics, personalisation, automation and optimisation can be considered to be the pillars of success for digital marketing campaigns. Adopting AI can multiply the effectiveness of all these aspects of marketing manifolds which will result in gaining more high-value customers, enhancing the experience of existing customers, and mastering your growth strategies.

Subfields of AI


Implementing AI requires utilising a computer to mimic human behaviour in various ways.  This includes many subfields, such as neural networks, evolutionary computation, vision, robotic, natural language processing and machine learning (source). In this blog, we will focus on introducing the three most commonly used methods: machine learning, deep learning, and natural language processing.


1. Machine Learning

Machine learning is the computer’s ability to learn from the inputs to gain its experiences or learn implicit patterns from data. With machine learning, it is easier to handle massive amounts of data coming from multiple sources (e.g., website visit flow, purchase behaviour and responses to previous campaigns). As a subset of AI, it utilises methods like neural networks, statistics, operations research and physics to find hidden insights in data and deliver AI applications (source).

2. Deep Learning

Deep Learning is a subfield of machine learning. It enables computers to solve more complex problems by using huge neural networks with many layers of processing units. This takes advantage of advances in computing power and improved training techniques, to learn complex patterns in large amounts of data. Common applications include image and speech recognition (source)

3. Natural Language Processing (NLP)

NLP refers to any interaction between computers and human languages (an example of this would be Apple’s Siri). It also refers to the ability of computers to analyse, understand and generate human language, including speech. The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal, everyday language to perform tasks (source).


The Application of AI on Digital Marketing


Modern marketing has high requirements and is very fast-paced. Customers benefit from AI-enhanced customisation from the brands they interact with daily. Four marketing automation applications are introduced below:


1. Customized emails: It is well-known that attracting the attention of customers would be difficult without personalized services or products. The system utilises machine learning to generate, analyse data and then deliver insights. This helps businesses to adjust and refine the content sent to different customers based on their past behaviours.

2. Live chat: Two types of chatbots are, standard rule-based bot whose scripted actions are based on keywords, an AI-powered chatbot, which utilises machine learning to talk more naturally.  Machine learning chatbot can help users find what they are looking for, answer FAQ, and provide them with the same level of attention they would get in a physical store.

3. Targeted and personalized content: Placing ads based on consumer buying history and shopping behaviours, including click on, keywords searching, viewing or past purchases. For instance, when Spotify recommends users a playlist, this playlist is created based on the songs the user has been listening to for the last few days. 

4. Customer lifetime value forecasting: The customer lifetime of existing customers can be predicted through the customer relationship management (CRM) system. Customer lifetime value is a strong tool for measuring the future value of a business and predicting growth.


More Autonomous Marketing


Before implementing an AI-powered solution in your marketing, some basic information should be reviewed for optimal performance which can be addressed using the following questions:


Are the ways of data collection, storing, managements suitable for developing a centralised, interactive point of view of the leads and sales?

- Do I have a database for leads and sales?
- What features of leads should be gathered? For example, name, email, phone number, city, state, industry, department, etc.
- What online activities of browsers should be collected? For instance, click, purchase, view, etc. 
- Where can I find extra information to fill up the blank between the data gathered and insight needs?
- What are the options available for AI tools through existing platforms, which have high data security and privacy?


► What repetitive manual tasks should be replaced by intelligent automated capabilities

- Do I have any factors to consider before authorizing tasks to AI? For example, motivation, difficulty, trust, and risk (source).
- Do I have the governance in place to ensure that the choices made by AI comply with the business laws, rules, regulations and ethical value?

- What would be the switching cost of this AI tool?


► Do intelligent capabilities say the right thing to the right client?

- Do customers with different loyalty levels have specialized royalty benefit programs?
- What leads should I spend more time on or should I make fast adjustments and improvements?
- Which KPIs can be used to track the performance of automated capabilities, except conversion rate?




Digital marketing is an industry ripe with opportunities and challenges, with the direction of marketing technologies being unpredictable. There is no doubt integrating AI products into a daily workflow routine can help prepare your business for future opportunities. Embrace the power of  AI methods, such as machine learning, deep learning and NLP, to govern your customer data deeply and broadly. Consequently, it will help level-up the capabilities of the digital marketers, and start making a future-forward impact on customer loyalty, engagement and conversion rates.