The modern marketing department is increasingly responsible for the entire customer journey, from attracting prospects to converting leads. It’s a heavy load and marketers are under pressure to use data-driven technology more effectively to achieve their targets. Digital marketing is thus stepping further into the realm of IT and employing tools with artificial intelligence (and specifically machine learning) capabilities to derive more return on investment from campaigns.
For some, machine learning is the domain of seemingly incomprehensible algorithms and jargon. Others, however, are channelling their inner data scientist and collaborating closely with their IT colleagues to deliver more successful marketing campaigns. Machine learning strategies require marketing software that can teach itself to ‘grow’ in response to new information.
But these sophisticated automation tools are only as good as the data they’re fed. For your company to truly benefit from machine learning, you need to make sure that you’re using good, clean and relevant data from the get-go. Here are some key areas to focus on when getting started.
Positive and negative goals
A marketing campaign starts with a business priority that needs addressing. Once you have clarified the basic elements of who, what, when, where and why, and fed this information into your software, you need to define positive and negative goals. Machine learning is essentially problem-solving technology that uses data to find patterns that the human mind and eye cannot. The more definitive the information, the better the results.
So, if your business need is to convert more website visitors to customers and increase revenue via the online check-out function, then your goals are quite straightforward: a purchase is positive and no purchase is negative. Machine learning allows for more sophisticated analysis and can direct a far more personalised marketing campaign to achieve specific results. You could, for example, want to understand which elements of your campaign are attracting the biggest spenders and which are putting them off. Goals can, therefore, be defined according to time and engagement: an email opened or a minute or more spent browsing the website can be seen as positive, an email deleted or less than 60 seconds spent browsing is negative.
Establish these metrics within your marketing software and it will learn from how your customers engage with your campaign – and deliver the right results.
Audience segmentation
Machine learning is advancing so rapidly that treating each customer as their own individual segment is not an unlikely possibility in the near future. As it stands today, audience segmentation methods use demographic data, buying behaviours and engagement-based intelligence to group audiences and deliver as personalised a customer experience as possible. Machine learning can enhance this by identifying patterns, problems and future trends within these segments with even greater precision. The more relevant the foundation of existing data, the more exact your marketing efforts will be.
Within one seemingly homogenous segment, machine learning can distinguish loyal customers from fickle buyers, and time-wasters from genuinely interested prospects. The software can then refine its algorithm according to each type. This will allow you to focus your resources on the most lucrative audiences with highly personalised communications that push each interaction closer to a sale.
Through machine learning, marketing teams can not only help drive more revenue but also save money by correlating the amount of resources spent with the return on investment. If a specific segment of your database is taking up a lot of time with no results, you can choose to lose their custom and focus your efforts where it matters most – on your existing customers.
Content and channels
How do you reach your customers? Your company may currently email a monthly newsletter out to its database, but how effective is it if both new and old customers are receiving the same content and only half read their emails?
Machine learning can assess the value of your marketing channels as well as the content you are pushing, and help you tailor both for specific audiences. If, for example, you know that a certain segment of your database prefers to receive marketing information in bite-sized chunks over social media, it’s no good emailing them long brochures. You can distill the intelligence down even further by identifying patterns such as preferred time of contact or change in buying habits and refine your strategy accordingly.
It’s not realistic to expect marketing teams to become IT specialists, but as they adopt a more horizontal role across businesses, they do need to know how to get the most out of their marketing technologies. Ultimately, the cleaner and more relevant your data, the more effectively your marketing software can learn. Regardless of how intelligent the machine is, its results are only as smart as the marketer feeding it.
By Jason Lark, managing director at Celerity
PrivSec Conferences will bring together leading speakers and experts from privacy and security to deliver compelling content via solo presentations, panel discussions, debates, roundtables and workshops.
For more information on upcoming events, visit the website.
comments powered by Disqus