We talk a lot about data at Spin Sucks.
Data is increasingly a huge differentiator when it comes to PR and marketing metrics.
The more information you can gather about your audience, their activities, and how they engage with your brand, the better, right?
But data can be a bit… unwieldy.
Collecting it is one thing. Filtering and cleaning it is another. Actually using it still another.
Many organizations have great data goals and practices.
And like the best-laid plans, the best data intentions don’t always net the results you were hoping for.
So the next Big Question focuses on that:
Where have your data-driven efforts gone most wrong?
Data-driven Mistakes: Keep an Open Mind
Have you ever started working on a campaign, or began researching an article or story, knowing what you want to accomplish, find, or say?
Knowing who you want to target, and the message you want them to take away?
And then, have you ever found out that you were, all along, going down the wrong path? :raises hand:
That’s where Jennifer Horne gives pause when it comes to her data-driven efforts:
One of the hiccups that we’ve run into when using data to make decisions is confirmation bias.
This is when someone goes through the data to find metrics that only serve to confirm their existing opinion.
It’s human nature to want to support your feelings, but when you don’t approach data with an open mind, you can get yourself into trouble.
After all, data is only as good as the questions you ask of it.
If your question is only “am I right?” the chances that you’ll learn from your data are slim to none.
This is why it’s important that multiple people within a department have access to the numbers and can run reports and view KPIs without it being an enormous resource drain.
Having a central source of truth like a dashboard keeps everyone on the same page, as well as keeping people accountable.
Data literacy is critical, and not just for management.
Similarly, Jomel Alos warns against making assumptions:
We work across various sectors and sometimes when we see similar patterns among a couple of verticals, we would assume that the strategies we have used for a different client will work. But it doesn’t always work.
When treating and interpreting data, you also have to consider the uniqueness of the industry and its audiences.
Data-driven Mistakes: The Big Picture
From Phil Svitek:
Tip #1: When gathering data you must start backwards – that is with the end in mind.
What is the objective your business needs to achieve for success?
Too often companies gather data in search of interesting aspects then attempt to turn this data into something meaningful.
Without clear goals the data won’t help you. In fact, it’ll harm you.
Tip #2: Data is great but can’t be the sole driver for decision making.
Any business deals with people and there must be some connection and understanding of the people you’re doing business with that any data will inherently overlook.
Data-driven Mistakes: You Can Say that Again
Devin Pickell says we need to watch out for information overload, usually in the form of duplicate data. (See what I did there? Duplicate data. “You can say that again.” I’m on fire!)
Take a step back and consider a few questions. What is the business objective you plan on applying this data towards?
Which metrics are most important right now, and how will data help us measure success?
Aside from setting these goals, it’s important to keep your data organized.
Track data flows and where data is being stored. Collaborate on these efforts!
Seriously, you’d be shocked by how much data is just duplicate information.
As a matter of fact, there is a term ‘infobesity’ which refers to the overload of information nowadays. Don’t fall victim to infobesity.
Data-driven Mistakes: Data for all Seasons
On the other hand, Emily Tanner feels you need to make sure you’re looking at enough data, and that the data you are looking at is timely and relevant.
The two areas where I see people go wrong in making data-driven decisions are:
1) not looking at a statistically significant data set (aka using not enough data can lead to inaccurate insights), and…
2) not accounting for seasonality (industries often have different seasonal trends which impact data, so if you look week over week or month over month at data without being aware of the seasonal impact, the insights you find could be misleading).
Data-driven Mistakes: Too. Much. Data.
Too much data can cause some marketers to get lost in the weeds. They start looking so deeply that they can’t see the patterns forming any longer.
They spend so much time focused on something small that they miss opportunities.
I have found that visualization of the data is usually a plus.
People respond to images and when you can see the data as an image rather than a report, it clicks faster.
VizExplorer is leading this in the casino industry. The first thing they did was show us which machines were performing and the visual impact of a machine move.
To which Katie Robbert responded/rallied:
Oooh, I’ve become a data studio master.
We have one for ourselves to track KPIs and I was able to build an effective dashboard for one of our clients. I think it’s a helpful tool to aid in storytelling.
We typically try to set it up in the why/what/how structure, and tied each metric into those buckets.
Conclusion: Data isn’t the Goal
Katie’s colleague, Christopher Penn, also shared his thoughts on where most data-driven mistakes occur:
Data isn’t the goal. Useful insights are the goal.
Data is an ingredient. Software, tools, people, and processes help you manipulate the ingredients.
At the end of the day, you don’t want a pile of raw, uncracked eggs. You want an omelet.
But people don’t think about the outcome—they spend all their time worried about the tools and the data.
Yes, good data is essential, just as good ingredients are essential.
Good tools bring out the best in the ingredients they’re given.
Good processes help people learn and repeat the outcome you want.
But someone has to start by saying what the goal is.
Selfishly, we named our company BrainTrust Insights, not BrainTrust Data.
Data Visualization: BrainTrust Insights to the Rescue
During our discussion, there were some pleas for help with data visualization.
As ever, in the spirit of lifelong learning and shared knowledge, Chris Penn provided a tutorial on Google Data Studios best practices:
Not to be outdone (kidding, they weren’t competing, just being super helpful as usual), Katie Robbert followed with a similar, albeit “more comprehensive” Q&A for Google Data Studio and data visualization:
You’re welcome (on behalf of Katie and Chris)!
What you tweet (not to mention say, do, or whatever in public or on social media) matters.
We can also point to the Justine Sacco story.
Both scenarios resulted in disastrous after-effects for the people involved. All due to what they likely thought was a harmless message on social media.
We aren’t (or weren’t) taught how to speak on the phone or what not to communicate in email, but many people seem to think that we can do or say whatever we want on “social” media.
Sure, we were taught how to speak to our elders (oh boy, am I dating myself, or what?), and we learned what was polite and acceptable in our social circles.
But generally, our comportment was learned behavior over time. Right?
All I can say is, thank goodness social media didn’t exist when I was in high school…
Should we be accountable for our behavior on social media? Absolutely.
The punishment doesn’t always fit the crime, it seems, but our actions do have consequences.
As a result, in order to safeguard future generations from foot-in-mouth syndrome, does this have to be taught?
The Big Question:
Should social media ethics and guidelines be taught in school?
You can answer here, in our free Spin Sucks Community, or on the socials (use #SpinSucksQuestion so we can find you).