Monina asks, "Where do our current analytics tools lack, and how could a data scientist help?"
If we think about what we expect of our tools, we ask them to tell us what happened, why, and what we should do about it. Almost every analytics tool only does the first part. A data scientist has to help complete the rest of the hierarchy of analytics - descriptive, diagnostic, predictive, prescriptive, and proactive.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today's episode menina asks, Where do our current analytics tools lack? And how could a data scientist help? This is an excellent question because it relies on a bit of understanding of what analytics is, and what we are and are not getting.
So there's a hierarchy to analytics, there's a structure to it that indicates sort of what we want to have happen.
That's called the hierarchy of analytics on surprisingly, let's actually bring it up here.
So the lower portions of the left side of from Gartner incorporated and then the right side of the top or additions I made, the hierarchy of analytics begins at the bottom, you have descriptive analytics answering the question of what happened, and this is exactly what every single analytics tool does today.
Go to Google Analytics, Facebook, anywhere you go.
You In digital marketing, you're going to get a data dump of what happened.
What you don't get is the rest of the hierarchy, right? You don't get diagnostic analytics Why Did something happen? website traffic was up 40% yesterday, why you have to go hunt for it the tool is not going to tell you why something happened.
And in some cases, you may not be able to even know why.
There are tons of different marketing data points where you need qualitative research to essentially asks ask someone asked a customer, why did you do that thing? And only then will you start to answer those white to happen current analytics tools.
Can't do that.
Of course, predictive analytics third on hierarchy if you have a data point, what's going to happen next.
This is for things like time series forecasting, what's going to be the the trend for our topic or our term or our email numbers in the next six to 12 months after You have prescriptive analytics, what should we do about it? What? What's the logical next step to take? And this is, remember, in data science, you have those four key skill areas, business skills, scientific skills, technical skills and mathematical skills.
This is where those business skills come into play, where you need to have a sense of what the business is doing in order to be able to offer that prescription like here's what we should do.
Now we know what we know.
We know why we know what's going to happen next, here's what we should do.
And finally, where really it's the almost nothing in the market, except for custom built solutions.
And maybe the biggest tech companies is proactive analytics, which is starting to use machine learning artificial intelligence to essentially say, Okay, what can we get this tool to do for us? What can we take our hands off the wheel and let you know self driving marketing If you will happen and it just doesn't there's there isn't that a lot of vendors will say there is a lot of vendors will promise you the moon.
But realistically, there is no such thing as self driving marketing.
And so if we think about what we asked them our tools, what we expect of our tools, we are expecting, at the very least the first three rungs on the ladder and we're only getting the lowest rung.
But what happened and so that's where a data scientist and data science team will help us fix, fix, but help us augment what our analytics tools are capable of doing.
There's so much in the data that, frankly, is very difficult to parse out.
Simple example if if you're doing regression analysis where you want to figure out okay, I have all these different data points, time on site bounce rate, traffic from Facebook traffic from email, thanks.
And I have an outcome, like goal completions on my website.
One of all of this data actually matters, what has a relationship to this outcome that I could test.
And if you have all these different data points, again, how looks tools today can't do that, even though mathematically, it's actually not that hard to do.
It is very computationally intensive.
And it requires some experience interpreting the results.
So a data scientists in that case would take all that data out of Google Analytics or wherever, run it through a regression model of some kind, everything from straight, simple linear regression to gradient descent gradient, boosting extreme gradient boosting, you name it, there's so many different techniques to use.
The data scientist look at the data, look at the the distributions and things make a decision about which method to choose, maybe try a few of them and come to a conclusion.
Okay, here is the method we're going to use is the the mathematical technique, we're going to use the code, run it and say now, we have this outcome, we care about conversions.
And we have these 500 data points in Google Analytics, here, the three in combination that have a relationship to the outcome that we care about.
And now we can start the science part of Okay, now let's set up a hypothesis that these three matter most to conversion, and start running tests.
Okay, if we increase, you know, time on site by 10%, we just see a 10% increase in conversion.
Good hypothesis right can be provably true or false.
We then go and build some test, run some tests, optimize the site, using, you know, Google Optimize, or whatever, to focus on a goal of increased time on site, and then look at our conversions and see, did we see a proportional increase in time on site that was matched Is the proportional increase in conversions? If so, our hypothesis is true, if not, our hypothesis is false.
And that's where a data scientist can really take these analytics and turn them into meaningful results that guide our business.
Now, as we've talked about in this series of questions, it's not going to be cheap.
And it's not going to be fast.
Because this is science.
This is experimentation.
This is testing as not something you can just, you know, snap your fingers or buy a piece of software and do the most important value add that a data scientist offers, in addition to just being able to do the thing is to look at the data and tell you where it's going to go wrong or tell you that the data is just not good enough to reach the conclusion that you want to reach.
So again, that's where data scientists can help fill in the gaps for these these current analytics tools lack, they just can't get there yet.
Will they get there someday? Maybe would depend on how accessible the results are to an end user.
Because a company like Google is not going to make these crazy additions to Google Analytics for the benefit of data scientists only they need it to benefit everybody.
And they have the API and the programming interface, so that a skilled data scientist, a skilled data science team can extract the data for themselves and do those computations without them needing to add and clutter up the interface for end users.
So really good question, because there's a ton that data scientists can do in these other rungs of the ladder that today's tools can't do and that's okay.
doesn't need to be in there.
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