Using Yesterday’s Diary Entries for Today’s Data Visualizations

Data visualizations are made from all kinds of data sources. If data exists, chances are someone is collecting and analyzing it.

Specific examples of data visualizations include rat squeak visualizations, mapped mortality causes, deducing the source of infection, selfie styles, depths of pants pockets, levels of corruption in countries spanning the globe – really, you name it, someone is visualizing it.

It came as no surprise that someone had mapped out the activities and behavior of famous creative people using the information in the book “Daily Rituals: How Artists Work” by Mason Currey, the contents of which are informed by specific periods of famous creatives’ lives as recorded in diaries, letters, and other documentation.

The Podio data visualization The Daily Routines of Famous Creative People charts and illustrates how renowned artists, writers, and musicians structured their days.

Very consistent vodka, amphetamines, pipe / cigarettes, and black coffee consumption habits aside, the famous creatives in this data visualization all had structure in common. They did not all sup at the same time, but each individual had supper at the same time each day. And these famous folk kept their own consistent sleeping, waking, and working hours, too.

The visualization of their activities, personalized to each famous creative person, got me wondering how long it might be until the personalization and hyper-personalization of targeted marketing campaigns gets so granular that martech can spit out the right time to engage the right exec. on a buying committee because it knows when her lunchtime walk with her spouse ends and when her afternoon work session picks back up, or when automated marketing engagement tech times the first outreach email with nurture-specific content to match that sweet spot between when a decision maker’s commute ends and they first log on to their work computer.

It might seem like a martech fever dream, but I don’t know how long it will be until data about an individual’s routine is processed like first- and third-party data is for marketing operations. People track their own health and number of footsteps with health wearables, others track their heart rates with EKG integrations for Apple Watches, and we all know our Android phones talk with Google even when our data is off, because Google follows up with us every time!

Personalization and hyper-personalization of websites, customer experience, and marketing activities is now the status quo, and marketing operations with budgets that allow for automated or smart marketing tech know that timing the right engagements for the right targeted audience member is a luxury they’ll never be able to do without. First parties can already monitor when a user lands on the site, and marketing organizations can time their reactive engagements anywhere from an immediate response to a 72-hour wait period.

As such, sales and marketing leads today are not the straightforward action items of yesterday; a lead today that’s based off intent signals requires a bit of homework from the BDRs or sales reps investigating the prospect. This can include looking at a prospect’s LinkedIn profile and finding out personal information about them before following up on an interaction with their website.

I attended a recent webinar presented by marketing leaders, and Trish Bertuzzi of The Bridge Group said it’s not only critical to determine the details of the reporting structure at a prospect’s organization, but also to know when it is and isn’t appropriate to use information gathered on LinkedIn or corporate bios such as alma mater or fraternal groups.

I’m no data scientist, and I wouldn’t call myself a citizen data scientist or a marketing data scientist, but if marketing activities are already fueled by personal, corporate, and behavioral data, I imagine it won’t be long until you can target your content engagements to the exact minute your prospect logs in at work with his first morning coffee and opens his email for the 15 minutes of routine clicking and browsing you know he’ll do. Whether we will find this information out via cookies and tags embedded within a website’s code or via the commoditization of wearable device or biometric data, I don’t know. But we shall find out.