Good Data vs Bad Data
Does it even matter?
Pat yourselves on the back, have a cold one, or whatever your favorite beverage is. You deserve it. You’re one of the few brave souls subscribed to this newsletter.
I'm going to kick this article off and all others with a praise and a joke.
Oh and do check out the podcast link I added at the end of this issue. It wraps up what we’re talking about today perfectly.
Praise
Thank you to the brave followers taking the plunge with me to better use data to drive our businesses and decision making power forward.
Joke
An offer owner walks into a bar and orders a beer. A few seconds later, and without original intent agrees to order a chaser, 2 more beers at a discount, and a monthly subscription to craft beer guru.
Good Data Vs Bad Data
Data is a subject I think about a lot. If you're in the direct response or performance marketing space, data is king. We use it to plan our next order bump, funnel optimization, and we use it to guide us on adspend performance before dishing out more dough.
So after about 10 years working on and off in DR, here's what I've noticed. There are 2 types of data-decision makers:
Those who scrutinize every data point and second guess every report.
Those who have (almost) full trust in the stats, say a Hale Mary and pull the trigger.
The difference?
Speed.
So which one are you and which one “should” you be?
Both.
Here's my take on it.
It's common to compare the performance of a particular creative or landing page across multiple tracking and reporting platforms.
E.g. comparing Facebook conversions (CVR) natively (through fbs ads manager) vs comparing it in Google Analytics.
Often times those two metrics will differ in both platforms. They'll differ because there method of tracking conversions and measuring attribution are different.
Its less about how much they're off by and more about the relative error between them.
E.g, If you're used to seeing a 5-10% difference between FB and GA, then that’s your baseline for relative error.
I would start worrying once those differences become larger and fluctuations become more erratic.
Where data quality can turn into a more serious discussion is when it comes to revenue metrics; LTV, ROAS, Net profit, etc.
Usually the decisions made based off of these metrics hold a lot more weight both in cost and in time.
Spending some time validating those stats either by analyzing it yourself or getting a second pair of eyes to review could be the difference between doubling down on a losing offer or making the call on a winning one.
As promised, do check out this great ClickBank podcast episode titled “How to Combat rising Facebook Ad Costs“. The real hidden gem in here is about not putting all your eggs in one basket and tracking the relative difference in stats across your reporting platforms.
Till next time remember…
“If you can’t measure it, you can’t improve it” - Peter Drucker
Dimitri