Will AI Marketing be the Next Data Revolution?

Getting the answer to this vital question will prepare marketers for success as they build strategies to incorporate Artificial Intelligence Marketing (AIM) technology into their businesses. Artificial Intelligence (AI) can simply be described as work exhibited by machines, rather than humans. Sophisticated, multi-touch advertising attribution is possible today and marketers are only now beginning to understand its power.

Data, Data, Data  
Thanks to software that can gather everyday surges of user data, 2017 has been the year that digital marketing statistics gain the credit they deserve.

A report by BlueVenn, states 72% of marketers consider data analysis to be the most important skill to acquire in the next two years.

With the correct technology in place actions on websites and apps can be carefully analysed, tested and continually improved. Targeted personalisation should be developed in real time, meaning the continuous modification of app notifications outside of their release cycles. Whilst it’s important to continuously gather insights from website visits and client behaviour, identifying things like the length of time spent and drop-off rate on each field of the application form, as well as the conversion rate on different devices and browsers is necessary. From there, it is possible to modify and re-test different forms to optimise the experience.

Predictive Analytics

Predictive analytics has gained an important seat at the table in 2017 as it identifies and targets visitors who exhibit the same behaviour as each other as well as visitors who are known to have a high lifetime value (LTV). Creating a visitor profile based on arrival data, site footprint, and customer data provides rich information that should not be neglected. Suppose, for example, a high LTV customer has entered the site from London using the keywords ‘best interest rates’ and has had six sessions, 30 page views, and we know from a half completed form that he’s 35 and married. Matches can then be created based on similar visits to prioritize product related messaging and promotions for these those visitors with a predicted high value.

Suppose, for example, a high LTV customer has entered the site from London using the keywords ‘best interest rates’ and has had six sessions, 30 page views, and we know from a half completed form that he’s 35 and married. Matches can then be created based on similar visits to prioritize product related messaging and promotions for those visitors with a predicted high value.

Attribution
Throughout the lifetime of a client’s relationship with a company, they will be involved in many different touch points.

Usually, it’s the ‘last-touch’ (the final stage before a client converts) that has value attributed, although there a many more sophisticated attribution models out there.

In 2017 we’ve seen advances in technology allowing marketers to track multiple data points including social media channels, websites, landing pages, email campaigns, mobile apps and payment methods – all accessed from different devices. On average homes in the UK have between seven and ten devices connected to the internet at any one time. If there is a strong understanding of the overlaps across these platforms, these advances allow for clear demonstration of the ROI of each campaign and the channels used to promote them.

A user should be able to switch from their different devices and still be at the same point where they left off. Correct measurements will help a marketer to understand which channels are most powerful. Ultimately, being able to track an advert served across different devices and justify that it has actually achieved its campaign objective is paramount.