While it is straightforward and easy for one to Ace AI, we must be conscious of how this is done – what going end points it must accomplish – or what can be applied, how it is going to be applied, and which KPIs will be used as a measure of its success.
Marketing AI may fundamentally change the interaction between companies and the public. In order to be compatible with tools and workflows already in use, ethical quandaries need to be resolved even before this new tool can be implemented.
Predictive Analytics
Predictive analytics employs statistical techniques, such as predictive data modeling, to forecast future behaviour and events with near certainty (removing crystal balls and tea leaves from the decision making process in favour of the most probable outcomes), for use in the foreseeable future. This will help marketers optimise campaign strategy to hit behavioural, event-based, or revenue goals.
Examples are ordinary linear and logistic regression models, decision trees and neural nets, the latter in particular helpful to disentangle nonlinearity.
This allows marketing teams to use predictive insights to turn leads into sales, and to retain and upsell or cross-sell customers based on their past buying patterns, or to fine-tune the time at which to send an email to a prospect.
AI-based predictive analytics is becoming increasingly efficient in automating the steps of predictive modelling, which helps to make the predictions accessible to business users faster and easier. Predictions can then be routed to other parts of the organisation for their utilisation: they can be used for enhancing business decisions; they can be published for use company-wide; or they can have decisions applied directly to them in real-time in business processes. In other words, they can free people to handle more strategic work.
Attribution Modeling
Attribution modelling, finding out which marketing channels and touchpoints are driving sales and conversions, might offer the digital marketer a useful way to spot ailing campaigns and allocate resources accordingly.
There are several different attribution models, each with their own benefits and limitations. For example, first-touch attribution lets marketers see which channels or campaigns are driving brand recognition and initial customer engagement; multi-touch models assign credit for different impressions or tactics along the consumer journey.
Machine learning attribution models take a more data-intensive approach by letting the rules of machine learning disaggregate a consumer journey and measure each marketing channel’s contribution to each step along a sales cycle. Machine-learning attribution models consequently lend a greater degree of automation and provide more accurate measures than single-touch models. Unlike single-touch attribution, they reflect a more holistic view of your endeavours. However, these models do require regular updates to reflect changes to marketing strategy or user journey data.
Content Creation
Content marketing is essential for online campaigns of any size: be it blog posts, social media updates, e-books, white papers, or emails – or even images and videos, which can convey that messaging which you just couldn’t articulate elsewhere.
Good stuff should be clear and concise, useful but not preachy, and should fulfil current and future content needs of your audience through adherence to accessibility best practices, including alt text for audio/video files, and transcripts for audio/video files.
But, with the right application of AI tools, you can leverage your writers and editors’ ability to do things that human beings still do better than machines: research, outline, write the initial draft, elevate the tone, seize opportunities, snip away the chaff, and deliver what you need – content that produces a great return on investment, whether by aligning with strategic objectives or ‘just’ by working like a good content design human.
Real-Time Campaign Optimization
Marketing teams can make better decisions, since AI analyses existing data patterns in real time and allows the optimisation of campaigns, thereby maximising ROI. AI uses predictive models to deliver messages to the right audience at the right time, making the message more relevant, while reducing expenditure on wasted audiences.
Personalisation improves a customer’s experience. A customer feels more understood and valued when he/she sees recommendations from a brand after making a purchase in the past. A smart chat-client could recommend relevant products based on past purchases or other preferences for products or services the brand sells.
Furthermore, using AI for optimisation can help marketers to keep pace with their competition by analysing campaign performance or real-time bidding strategies aiming at maximum visibility and reach. The reason is simply that AI can analyse data and identify changes in market trends or consumer behaviour that keeps them relevant in an ever-changing digital market environment – it is digital marketing on steroids! This truly is groundbreaking technology.
