Machine learning for marketers: Getting the strategies and processes right

James is editor in chief of TechForge Media, with a passion for how technologies influence business and several Mobile World Congress events under his belt. James has interviewed a variety of leading figures in his career, from former Mafia boss Michael Franzese, to Steve Wozniak, and Jean Michel Jarre. James can be found tweeting at @James_T_Bourne.

Webinar Machine learning has the potential to transform marketers’ operations – but organisations need to overcome various hurdles to achieve success.

Writing for this publication at the end of last year, Phil Midwinter, CTO at Third Foundation, said organisations needed to make a ‘serious commitment to data and digital via ML and AI’ in 2020.

To achieve this, you not only need the right data, but also the right tools to read between the lines. Artificial intelligence and machine learning ‘are improving and expanding at unimaginable rates’, added Midwinter, with enterprises who have ‘the requisite infrastructure… soon enough being able to utilise them for a whole range of creative and practical tasks they might not even realise.’

These can range from initiatives at the highest conceptual level, to automating and enhancing more of a marketers’ grunt work, such as writing laborious emails. Virtually anything can be tweaked, from pay per click campaigns, to SEO, to content management, as the Digital Marketing Institute (DMI) explains.

Yet for those who fear their workload will change beyond recognition as such initiatives are brought in, don’t worry. The DMI notes how ML is there to improve processes rather than target jobs.

“Regardless of the expectations of digital professionals, ML isn’t here to take over the jobs of digital marketers,” the DMI says. “Rather, its main use is to help enhance digital marketing strategies and make the jobs of digital marketers easier. By utilising ML tools and capabilities, you can streamline your digital strategy and align yourself with an AI and ML-dependent future.”

To ensure expectations are not over the top, Alex Igelsbock, CEO of Adverity, advocates a ‘through the looking glass’ approach to implementation. Writing for MarketingTech this time last year, Igelsbock wrote: “This could involve working backwards and identifying what AI can achieve before deciding whether that would benefit your business.

“In some instances, applying simple rules, statistics or machine learning techniques actually best solves what executives want to achieve,” added Igelsbock.

“While AI is an extremely powerful technique, it is important that you employ it for tasks that are fit for purpose,” he continued. “Remember the AI KPI is not always necessary. Although it’s worth considering the specific benefits to keep ahead of the curve, it’s imperative to make informed decisions about your requirements before treating AI-driven technology as a silver bullet to all your business problems.”

This approach can be examined when exploring augmented analytics, defined by Gartner as ‘the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyse data in analytics and BI platforms.’

For Adverity, this means correctly applying augmented analytics into an organisation’s entire analytics process. At DMWF Virtual on September 16-17, Gavin England, senior product marketing manager, will outline how to uncover insights such as performance highs and lows, emerging trends, rogue outliers, and where to focus budgets for maximum ROI. They are all obtainable if you know where to look – in a way that won’t distract or overwhelm your company’s current marketing efforts.

LEARN MORE: Speaker Spotlight – Gavin England, Senior Product Marketing Manager, Adverity

Find out more about DMWF Virtual here.

Interested in hearing leading global brands discuss subjects like this in person?

Find out more about Digital Marketing World Forum (#DMWF) Europe, London, North America, and Singapore.  

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