The Role of AI in Marketing

AI algorithms analyse large data plots to estimate future tendencies, trends and consumer behaviour, which allows marketers to make data-driven decisions. Furthermore, AI allows hyper-personalisation of marketing communication campaigns, based on individual customers by creating a relevant consumer experience, ultimately enhancing engagement and conversion rates.

As well as scanning customer conversations to draw systematic insights about customer needs or to automatically respond to initial comments, thereby increasing reaction times, NLP also improves the efficiency of real-time advertising budget and campaign optimisation.

Predictive Analytics

Predictive analytics is a data-driven method of forecasting future results via statistical methods, algorithms and machine learning to discover patterns in existing or historical consumer behaviour, product performance or general business operations to identify trends that enable marketers to optimise marketing campaigns, improve ROI and gain market-share advantage.

Predictive analytics can potentially provide a huge boost to marketing departments by identifying customers with a propensity to buy, or to buy particular products or services; at what time they might be most receptive to contact or to a specific message; or how a campaign should be tailored to them.

Real-time predictions are important because marketers must be able to move quickly on their predictions. Predictive models can be used to automate many of the most tedious tasks of marketers, especially ‘blind’ analytics such as A/B testing and content personalisation. In fact, there are a number of off-the-shelf predictive analytics software solutions that companies can implement, and these can be easily hooked up to customer relationship management systems, email automation platforms or ad delivery networks, as some of these example systems from various companies show:

Campaign Optimization

Campaign optimisation is the modification and alteration of digital marketing campaigns to be more effective in fulfilling corresponding goals (eg, website clicks, engagement, reach, conversions). It typically entails examining performance indicators, setting key performance indicators, identifying growth opportunities, making changes and testing.

With incredibly powerful AI-powered ads and campaign-optimisation tools at their fingertips – AppsFlyer’s Creative Optimization feature is fuelled by AI – marketers, armed with hard data, can improve ad impression crafting and drive return on campaign spend.

The success of campaign optimisation relies on data gathering and analyzing of campaign performance to make decisions and maximise return. Specifically, with a clear goal or target and good practice in accordance with, the campaign optimisation will make the campaign to be more efficient, and improve sales of eCommerce brand to have more exposure via channels.


Artificial Intelligence (AI) is today one of the most extensively used emerging technologies with applications ranging from recommendation algorithms to shape clinical decisions, to chatbots or voice and handwriting recognition software to support autonomous vehicles to understand texts, images and audio data, and to undertake unsupervised analysis of massive datasets to highlight patterns and trends – all examples that go well beyond the stereotype of robots replacing human labour.

The term we now use to describe their rudimentary capacity is machine learning, a computer program that learns how to accomplish a task through increasingly sophisticated applications of logic to observed data. In the past decade, scores of digital machines have gone on to perform astounding feats of artificial intelligence (AI), from identifying cancers in mammograms to beating world Go champions.

Other types of AI encompass the ability to enable machine vision (the ability to recognise and interpret digital images or video for semantic understanding – applications include tagging of pictures and videos on social media, or radiology imaging in health care). Generative AI can help enable the computer creating various types of media on a text prompt (the AI may output photos looking like real pictures, or letters in the form of text trying to imitate human writing ability). The sky may be the limit here, but we are still a ways off from being able to see this technology in widespread use. It may assist in allowing automation of the software coding process, or in IT processes themselves.


Combined, marketing automation provides tactics to automate repetitive tasks and workflows in order to save on operating costs and human errors, as well as become more agile, competitive and future-proof – with increased growth for the long term.

Reactive machine intelligence is good at narrowly defined things like chess-playing programs or chatbots that answer online customers with pre-scripted statements. But it can’t change its behaviour when it’s presented with new data or experiences. And it can’t be told what was done how and why, which presents a problem for financial institutions and other highly regulated industries where rules require people to see how a decision was reached when denying them credit.

More sophisticated AI will understand and compose natural language, create music or images, converse with humans, converse with each other; more intimate interactions with consumers are possible – think of automated customer service or self-driving cars; more efficient business management – think of inventory needs predicted by analysing sales data, or real-time product suggestions offered to consumers in stores or online.

Leave a Reply

Your email address will not be published. Required fields are marked *