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BriannaMersey_V5_highexport.vtt

English

{{00:00:00}}
If you're a marketer and you're
looking to integrate data

{{00:00:03}}
science, here's where to start.

{{00:00:11}}
When you want to harmonise data science,
data engineering with your, your

{{00:00:16}}
marketing analytics, you need to really
stop and, and take a look at what your

{{00:00:21}}
future, what you want your future to be.

{{00:00:23}}
So you do like a strategy session with
a team where you can plan for like six

{{00:00:27}}
to 18 months, and then you can start to
see where the advanced analytics projects

{{00:00:32}}
lie and like where you want to intertwine
your data science team and their work

{{00:00:37}}
because it's more advanced It's not
like your foundational data collection

{{00:00:40}}
and you need to plan for it for budgets
because it's often more expensive.

{{00:00:44}}
Those projects are value based and
you usually save or reduce spend and

{{00:00:48}}
that you you get all the rest of the
pieces ready for that when you get

{{00:00:51}}
to the advanced section where you're
creating like you know, machine learning

{{00:00:55}}
audiences, you've got everything
in place and you're ready for it.

{{00:00:58}}
It's super important to align your
data strategy with your KPIs and

{{00:01:02}}
your, your sort of global marketing
plan or your overall marketing plan.

{{00:01:05}}
Essentially, your data analytics
is the foundational piece of

{{00:01:09}}
your whole data maturity journey
forever, until you redo it.

{{00:01:13}}
So you want to make sure that you've
got all your stakeholders involved

{{00:01:18}}
and that you've talked to them and
you've aligned on your KPIs so that

{{00:01:20}}
you can architect and build that data
foundation so that you can do any type

{{00:01:25}}
of projects later on and that you have
the integrity and trust in your data that

{{00:01:29}}
you need to make important decisions.

{{00:01:31}}
It's helpful to measure.

{{00:01:33}}
It's helpful for audiences.

{{00:01:34}}
It's helpful to give people
great experiences like on

{{00:01:37}}
your websites or your apps.

{{00:01:39}}
It's all that feedback process.

{{00:01:40}}
And so yeah, the data is really
customer centric and decision making

{{00:01:52}}
Automation and, and AI
and machine learning.

{{00:01:55}}
I mean, that's hot topic.

{{00:01:56}}
Companies can use generative AI and that's
more for content,  content building, so

{{00:02:02}}
creative, or text, and scheduling, and
then predictive AI is where you want

{{00:02:06}}
to use like machine learning and,  AI.

{{00:02:09}}
So that can be with audience creation.

{{00:02:11}}
So you can use machine learning to
uncover groups of people, like segments,

{{00:02:16}}
that might be, have a propensity
to do something specific, like buy

{{00:02:20}}
a product, or maybe you're looking
for people who are going to churn.

{{00:02:24}}
And so with machine learning
solutions, you can pull those

{{00:02:28}}
segments out and then retarget them.

{{00:02:30}}
To improve costs or save, save on
costs, save on your marketing spend.

{{00:02:34}}
Also.

{{00:02:35}}
You can use AI for ad fatigue, so if you
are delivering certain creative design

{{00:02:42}}
and ads to, to your customers, your top
level funnel people, and they're seeing

{{00:02:47}}
the same thing, you can use AI to start
figuring out when that fatigue happens.

{{00:02:52}}
And there's usually like, a you
know, specific statistical analysis

{{00:02:55}}
that shows you when to swap that.

{{00:02:57}}
You can do it with personalisation.

{{00:02:58}}
So if you've got this now machine
learning audience that you've identified

{{00:03:02}}
and you know that they all want this
specific product, then you can use

{{00:03:05}}
AI to show them the content that's
specific to them on the website.

{{00:03:09}}
Automation is excellent.

{{00:03:11}}
It's the, it's where you can
incorporate it into your daily  tasks.

{{00:03:16}}
So if you're a marketer and you want
to start to increase time for strategy

{{00:03:20}}
and you want to get rid of some of
those mundane tasks, you can use

{{00:03:23}}
automation for like scheduling emails,
audience creation, content generation.

{{00:03:28}}
I would definitely say it's
super important and it's going

{{00:03:31}}
to be the way of the future.

{{00:03:32}}
It's just picking up those tools
and using them to improve your time.

{{00:03:43}}
For analytics, we really have
two top players and I think

{{00:03:45}}
probably most people know them.

{{00:03:47}}
We've got Google analytics.

{{00:03:48}}
So their new GA4 with the launch
of that there's been some backlash.

{{00:03:52}}
People are not thrilled.

{{00:03:55}}
But it is progressing and there's lots
of alphas coming out and they are taking

{{00:03:58}}
their customers feedback quite seriously.

{{00:04:00}}
On the other side, we
have Adobe Analytics.

{{00:04:03}}
I think they're trying
to keep up with GA4.

{{00:04:05}}
GA4 is now really more about the customer
experience and it encompasses web data,

{{00:04:11}}
app data, and it silos that data so
that you get the full customer journey.

{{00:04:14}}
Whereas Adobe is a bit more fragmented.

{{00:04:17}}
Adobe is an enterprise level product.

{{00:04:19}}
So a lot of banks, financial
institutions like Adobe.

{{00:04:22}}
So those are your two general ones
that usually clients tend to go for.

{{00:04:35}}
Nowadays I would suggest that marketers
looking for a new job or one's currently

{{00:04:39}}
working on their job, start to explore
AI tools, if you haven't done so.

{{00:04:43}}
I think it will help you
improve your efficiency.

{{00:04:47}}
If you can start to use stuff to take away
the daily mundane tasks or not mundane,

{{00:04:51}}
but even just something you can automate
or do really quickly, like replying to

{{00:04:54}}
emails, run it through, schedule, there's
lots of AI assistance for marketers.

{{00:05:00}}
So I think that is important.

{{00:05:01}}
And I definitely would suggest,
you know, learning more about

{{00:05:04}}
those tools, especially if you
want to apply for a new job.

{{00:05:07}}
If you're in marketing, you might not
know about all the data, the data science

{{00:05:10}}
solutions, you might not know about the
advanced solutions that are out there

{{00:05:13}}
that could really provide benefit.

{{00:05:15}}
And often those tools will help
either increase your return on

{{00:05:18}}
ad spend or decrease your cost.

{{00:05:21}}
You know, there are a lot of
people who are afraid or lazy

{{00:05:24}}
or who haven't looked into it.

{{00:05:25}}
So if you can bring that to the job,
then that's a great bonus for you.

If you're a marketer and you're
looking to integrate data

When you want to harmonise data science,
data engineering with your, your

marketing analytics, you need to really
stop and, and take a look at what your

So you do like a strategy session with
a team where you can plan for like six

to 18 months, and then you can start to
see where the advanced analytics projects

lie and like where you want to intertwine
your data science team and their work

because it's more advanced It's not
like your foundational data collection

and you need to plan for it for budgets
because it's often more expensive.

Those projects are value based and
you usually save or reduce spend and

that you you get all the rest of the
pieces ready for that when you get

to the advanced section where you're
creating like you know, machine learning

audiences, you've got everything
in place and you're ready for it.

It's super important to align your
data strategy with your KPIs and

your, your sort of global marketing
plan or your overall marketing plan.

Essentially, your data analytics
is the foundational piece of

your whole data maturity journey
forever, until you redo it.

So you want to make sure that you've
got all your stakeholders involved

and that you've talked to them and
you've aligned on your KPIs so that

you can architect and build that data
foundation so that you can do any type

of projects later on and that you have
the integrity and trust in your data that

It's helpful to give people
great experiences like on

And so yeah, the data is really
customer centric and decision making

Automation and, and AI
and machine learning.

Companies can use generative AI and that's
more for content,  content building, so

creative, or text, and scheduling, and
then predictive AI is where you want

So you can use machine learning to
uncover groups of people, like segments,

that might be, have a propensity
to do something specific, like buy

a product, or maybe you're looking
for people who are going to churn.

And so with machine learning
solutions, you can pull those

To improve costs or save, save on
costs, save on your marketing spend.

You can use AI for ad fatigue, so if you
are delivering certain creative design

and ads to, to your customers, your top
level funnel people, and they're seeing

the same thing, you can use AI to start
figuring out when that fatigue happens.

And there's usually like, a you
know, specific statistical analysis

So if you've got this now machine
learning audience that you've identified

and you know that they all want this
specific product, then you can use

AI to show them the content that's
specific to them on the website.

It's the, it's where you can
incorporate it into your daily  tasks.

So if you're a marketer and you want
to start to increase time for strategy

and you want to get rid of some of
those mundane tasks, you can use

automation for like scheduling emails,
audience creation, content generation.

I would definitely say it's
super important and it's going

It's just picking up those tools
and using them to improve your time.

For analytics, we really have
two top players and I think

So their new GA4 with the launch
of that there's been some backlash.

But it is progressing and there's lots
of alphas coming out and they are taking

their customers feedback quite seriously.

On the other side, we
have Adobe Analytics.

I think they're trying
to keep up with GA4.

GA4 is now really more about the customer
experience and it encompasses web data,

app data, and it silos that data so
that you get the full customer journey.

So a lot of banks, financial
institutions like Adobe.

So those are your two general ones
that usually clients tend to go for.

Nowadays I would suggest that marketers
looking for a new job or one's currently

working on their job, start to explore
AI tools, if you haven't done so.

I think it will help you
improve your efficiency.

If you can start to use stuff to take away
the daily mundane tasks or not mundane,

but even just something you can automate
or do really quickly, like replying to

emails, run it through, schedule, there's
lots of AI assistance for marketers.

And I definitely would suggest,
you know, learning more about

those tools, especially if you
want to apply for a new job.

If you're in marketing, you might not
know about all the data, the data science

solutions, you might not know about the
advanced solutions that are out there

And often those tools will help
either increase your return on

You know, there are a lot of
people who are afraid or lazy

So if you can bring that to the job,
then that's a great bonus for you.

AI and data science are essential tools for marketers to stay competitive. Brianna Mersey, Director of Data at Media.Monks, discusses the integration of data science into the marketing analytics role, and explains how to get started.