A Talk with the Country's Deepest Data-based Organization
Today marked a special day in the history of Insight DCS, when I was asked to speak at the United States Postal Service’s AIM conference, held in Golden, CO on September 18, 2019. For approximately 18 minutes, I delivered the following words to USPS professionals from all over the country. While we are still awaiting the video of the presentation, this was just too exciting not to share! You can read through the transcription of the topic in the paragraphs below.
If you have an event coming up, or require training in marketing, business technology or data management for your team, reach out to info@insightdcs.com and let us know!
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[TRANSCRIPT]:
My name is Kristy LaPlante, and today, I was asked to talk to you about marketing. But to do that, I feel like I would be doing you a big disservice, if I didn’t talk to you about data. Because I don’t think you can have one without the other anymore. To do good marketing today, you need data.
To some people, Data is a scary concept.
Who here has ever felt personally intimidated by the concept of, “BIG DATA”?
You aren’t alone. We’ve all felt that.
I had a friend in grade school, in Buffalo, New York, whose father -- in the early 1990s, at least -- refused, absolutely and on the principle of the mistrust of data, to get two things: a Library Card, and a Grocery Store loyalty card. He thought that by signing up for those things, he was giving the government his address and asking to be followed.
Data has come a LONG way since the 1990s.
The amount of data we not only have today, but that we continue to create on a daily basis is truly mind-boggling. Think about this: Internet users today create 2.5 QUINTILLION bytes of data each day. That means that each individual Internet user, like you and me, creates 1.7 megabytes of data every second.
Don’t believe me? Have you checked your email today? From your phone? From this location?
Then, my friends, you have created the following: geolocation data, browser data, device data, email data, open data, click data, behavioral data, response data, screen-time data. The list does go on.
I wish I could call up my friend’s dad, right now, on speaker phone, from this stage, to ask him how he is doing today. How he’s hanging in there.
Big Data gets a pretty bad rap in the media. When we hear stories about Big Data, it’s typically either a story about incomprehensible growth; or about an equally incomprehensible infringement of our personal privacy.
The last article here caused a big stir in 2018, Cambridge Analytica. Cambridge Analytica is now the subject of a documentary on Netflix called The Great Hack. But let’s talk about what really happened in the Cambridge scandal.
Founded in 1993 by a behaviorist, who believed that data could and should be used to foment certain political outcomes. This was problem #1, malintent.
The company described itself as being in the business of “psychological warfare” and “influence operations”
Separately, Cambridge University built an app on Facebook called MyPersonality, with survey questions about behaviors and human instincts. When they wouldn’t sell this data to CA, CA partnered with another individual to build a similar data set.
320,000 people filled out this new survey - providing a significant data set, not only of the folks who filled out the survey, but also of those whom they were friends with. Then, that data was used to CA’s advantage. It then sold that data to parties who would pay them money in exchange for help, say, winning elections.
Nowhere in this “scandal” was data the problem. The problem, was that technology evolved more quickly than our ability to understand it.
I’ll say that one again: technology evolved more quickly than our ability to understand it, or the implications of it.
This is not the first time in our history this kind of rapid evolution has happened. We saw this with cigarette smoking, before we knew that was bad for our health. We see it frequently, actually, and from completely different industries.
Back to Cambridge: while we as individuals had gotten used to those Facebook screens saying that we consent to give the data we were providing; we were just trying to make our own ends meet, and we were trusting that folks like Cambridge Analytica weren’t going to use our data without telling us. You know what I’m talking about! You know you took the Harry Potter test...anyone else a Gryffindor?? And we likely all made that assumption. That maybe the test app would also ask my friends to see what house they would be sorted into; and we didn’t assume that we could be sharing that data with a third external party.
You see, data is not the problem. Lately, though, it may have felt like one.
I happen to believe that the best way to navigate through something that feels that big, that heavy, and that challenging -- like data -- is to go through it.
So today, I ask you all to be Buffaloes of Data.
I believe strongly that the best way to overcome any fear, is to understand it.
This is me.
On the right, are associated Data Points about myself. [READS SLIDE.]
Now, this data is DETERMINISTIC. Who here is familiar with this term “Deterministic data”?
Deterministic data is defined as data that more “accurate”. It’s polar opposite is “probablaistic data,” data that has been produced due to calculations or algorithms, and can be trusted within certain degrees of certainty. .
...I personally don’t use these terms, but I do think it’s important to understand them. Because even “Deterministic” data can be wrong. For instance, have you ever filled out a form online and intentionally lied about something? I do it all the time! I will make up a phone number, just so people don’t call me., Or, I’ll say I’m 21 when I browse a website about alcohol, because I like to feel young sometimes. Technically, these white lies would be considered deterministic data, because I claimed them to be facts about myself.
I recommend using “Declared” data -- it’s the “that’s what she said!” of data -- or its polar opposite, “Inferred data.” In both cases, certain probabilities can be associated with how factual each piece of data really is.
Okay, so, these are all “DECLARED” data elements about me. Because I made this slide about myself. Now, let’s take a look at who Google thinks I am.
You can open your browser, right now, and go to adsettings.google.com and Google will tell you who they think you are, based on all sorts of data points it has observed about you.
Google observed this from the things I search for, what I read, where I shop, all sorts of ways I use the internet to search and explore things.
So, here’s what Google thinks of me. For the most part, it’s pretty accurate. But, who can find something on here that is really, glaringly incorrect?
Yes! Right? Google thinks I’m a man! I guess Google doesn’t believe that women could possibly be business owners, home owners, and athletes. Now let’s look at Facebook.
Similarly, if you have a Facebook account and you look at an ad in your main newsfeed, it will look like an ad and will have the words “Sponsored Content” written on it. If you click on the little three-dot option on the upper right corner of a Facebook ad, a few options will pop up. One of them is a link that says “Why am I seeing this ad?” If you click on that, just like Google, Facebook will tell you who they think you are. But here’s what’s interesting: Facebook also has a feature for marketers called Custom Audiences.
With Custom Audiences, you can take a list of email addresses from YOUR customer database, upload it, encrypted, of course, into Facebook, and message your customers directly within the Facebook platform. What’s cool as an individual, is now you can see what companies have uploaded your data, and you can opt out of messages from them on Facebook.
By knowing how companies are using your data, you are empowered to take control back of your personal data. Data offers a similar benefit to companies too, and marketers: there is a huge amount of tangible, calculable value associated with corporate responsibility: in this case, with transparency. Tell your customers, and your website visitors, exactly what data you have and how you are using it, and you can build an enormous amount of trust with them … trust that translates into revenue dollars.
Okay, so: now that we understand our own data a bit better, let’s look at a few other ways businesses & marketers today can, and should, use data in really valuable ways to drive revenue and improve marketing performance.
[READS SLIDES]
I could talk about data all day long, but frankly, they only gave me a few minutes here. So I’m going to leave you with the one singular piece of advice that you will ever need, no matter what data problem -- or life problem -- you are trying to solve.
I used to tell myself -- above all else -- to live by this one singular piece of advice:
Do one thing every day that scares you.
In the case of data, and since we are all Data Buffaloes now, I’d like to modify it to this for you:
Do the one thing, every day, that makes you the most proud.
Especially if you’re working with marketing data. Because if you do, you will always land on the correct side of the equation.
Thank you again so much for having me everyone. I’m Kristy, and it was my pleasure to be here.