In the second of a three-part series, I'm looking at how machine learning for marketers should focus upon learning from data sets in order to be successful. You can find part one here.
Targeting the right audience is a must for all marketers, but with the introduction of artificial intelligence (AI) and machine learning, how can marketers ensure that they make the most out of this powerful technology? It’s all about the data it has access to.
Before adopting any new technology, organisations want to know that they will see a return on their investment. The key to executing a successful marketing campaign using machine learning is to set appropriate, measurable KPIs against it.
Better data, better results
Machine learning for marketers should focus on learning from data sets in order to be successful. Targeted data can help optimise marketing delivery in order to get the best and most effective messages to the right audiences, in real time.
An online consumer looking to book travel tickets, for example, may have exhibited the right buying behaviour, but if you target them too late, you’ll miss out on that conversion. We, as customers, often jump from one step to another in the booking journey rather than following a linear process. This means that it can be difficult to gauge what stage we are at in our purchasing decision and when is the optimal time to present us with a marketing message.
Machine learning can help marketers to yield better understanding from the available data, and prevent irrelevant advertising. By analysing past data sets, machine learning technology can make more accurate predictions, so marketers can place better, more relevant content in front of their prospective customers.
The modern marketer's role is evolving from simply placing ads in front of as many people as possible, to determining the best time, place and context in which to serve the best ad to an individual. This is where programmatic comes in.
Adopting programmatic media
One key area where machine learning can be highly beneficial is programmatic media buying.
There are multiple factors involved in understanding what drives a conversion, a sale or a website visit, regardless of KPIs. Because of this, it makes sense to apply machine learning to programmatic advertising.
It’s important that marketers don’t forget to take care of the fundamentals in the rush to implement machine learning technologies, however. Companies are often guilty of not connecting their customer data internally across departments. Machine learning serves not as a replacement for traditional marketing methods, but it does enhance them, and that’s where brands need to understand the incremental value. The application of machine learning does not diminish a marketer’s skill-set, rather it is able to provide real-time insights on their customers so they are able to deliver the most relevant marketing messages.
Where is the value?
The more data a machine learning platform has to work with, the better it is able to identify patterns of customer behaviour and engage them in more meaningful and authentic ways. But marketers need to set clear parameters to ensure they are generating the best results from their partners:
- Set targets: First and foremost, it’s important to understand what you want to achieve, so set a list of KPIs at the start of your project. You can then review these after an introductory period and make any adjustments if necessary.
- Consider your content: As you review the data uncovered by machine learning, think about whether you should change your creative content. As the machine learns, you can adapt accordingly.
- Trust the process: Instead of coming with a preconceived notion and applying machine learning to those assumptions, better to allow the machine learning strategies to explore the broadest scope of opportunity. You may learn something new about your audience by doing so!
Marketers need to be prepared for the machine learning process to challenge any previous assumptions about your audience. It presents an opportunity for marketers to create more dynamic work, drive revenue and enhance customer experience.
To learn more about how machine learning will impact marketing, keep a look out for the third part in our series, where we’ll be looking at what the future holds for marketers.
By Paul Wright, CEO of iotec
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