How AI radically changed marketers jobs


How AI radically changed marketers jobs


As we emerge from a dark age of data scrubbing, spreadsheet sifting and rampant guesswork,

let’s take a moment to look back at the world of work we left behind so we can fully appreciate our AI-augmented present.

Not too long ago (and for far too many marketers, still to this day) tedious and time-consuming tasks filled the timesheets of all but the most senior executives.

But as new, affordable AI-powered tools take their place in the trenches, marketers are finally moving to man the command center, reshaping and elevating their role and potential impact.

We talked to AI-savvy marketers at the Bank of Montreal (BMO) about a few of the most profound shifts. Apologies in advance for the PTSD.

Then: A deluge of data

Will Duong, senior manager of BMO’s leads management engine and data strategy, has been with the bank for nine years –

long enough to remember when customer data collection was like trying to keep grains of sand from slipping between his fingers.

The fact that BMO was collecting data from scores of channels made things especially tough.

Duong found himself manually collecting information from a vast array of data streams,

dropping it into multiple spreadsheets and struggling to merge it all before using the insights to design campaigns.

Now: User-friendly insights

A process that used to take weeks or months is now instantaneous.

Entering a few key inputs related to demographics or user behavior can generate detailed suggestions around who to target, at what time and on which platform.

Resulting campaigns can then be deployed and coordinated in real time across channels as varied as email, web, SMS, social media or mobile apps.

Then: One for all 
For lots of marketers, personalized targeting was something of a white whale. They did have customer data – mountains of it, actually, ranging from demographics to browsing habits and spending patterns. But parsing all of it to identify fleshed-out, specific customers required a Herculean manual effort. For many, the only realistic solution was to craft broad messaging that appealed to anyone and everyone, then blast those messages across every conceivable platform.

Now: To each their own
AI engines have stepped up to take in and synthesize data across channels and devices – customer signals, behavior, profiles, environments, contexts and other variables. Armed with a deeper understanding of their consumers, marketers can now determine their ideal targets and tailor personalized messaging that plays to customers’ interests, locations and much more.

resources: digiday