SPEAKER: Welcome back.
Earlier in the course, you learned
about the relationship between digital marketing and branding.
You also explored the different parts
of a digital marketing and e-commerce strategy,
including research, goal setting, and selecting
the right channels and tactics.
Lastly, you found out a little about some
of the channels and tactics like SEO, SEM, social media,
and email marketing.
This part of the course is all about data--
how you measure it, how you interpret it,
and how it can make your strategy better.
We'll explore the concept of performance marketing
and examine some ways you might be working
with data in an entry-level digital marketing
or e-commerce role.
We'll discuss how data can help you find out
what's working well and how to adapt to necessary change.
We'll also go over some ways to demonstrate those findings
through data storytelling.
Measuring the success of your marketing efforts
may be the most important thing you
learn in your digital marketing or e-commerce career.
No matter how carefully you plan your strategy,
measuring your results can always
help you make it better from building
your brand to engaging customers and maximizing sales.
The first time I really worked with data and digital marketing
was when I began running paid advertising
for my own e-commerce store.
I wanted to understand if the effort I was making
was worth the extra cost.
This made me realize that there was
a whole world of measurement beyond the paid campaigns
I was running.
I could measure the health of my whole store
through different dashboards and analytics resources.
This is where e-commerce and digital marketing
really gets fun because you can actually
prove that your tactics are helping you reach your goals.
You're almost to the end of the course.
And I'm so excited for you to cross the finish line.
Let's get back into it.
[MUSIC PLAYING]
Throughout this course, we've emphasized how important
it is to measure the results of digital marketing tactics,
campaigns, and strategies.
In fact, these activities are so important that there
is a special term to describe them--
performance marketing.
In this video, we'll explore what's
involved in performance marketing
and how it allows businesses to set goals, track results,
and improve on their work.
Let's get started.
Imagine this.
It's 1985.
And you work for a marketing agency
that's promoting a new breakfast cereal.
You run some focus groups to learn about your target
audience and what they like.
You use those interviews and your past experiences
as a marketer to create memorable ads for newspapers,
billboards, and televisions.
And then you wait.
Your sales go up.
The campaign works.
But what you don't know is which ads were most effective
and how many of the new customers
found the product through your campaign.
That doesn't mean your ad strategy is all guesswork,
just that there's limited information you can gather.
So you can keep going with that same successful strategy.
Now, imagine you're running that same campaign today, only
online.
With digital marketing, there are
dozens of ways to measure the success of your tactics
and campaigns.
So if you place an ad for cereal online,
you can track the things that just
aren't possible with billboards like how many people encounter
and engage with your ad each week.
Collecting and evaluating all of that information
can help you rethink a weak strategy
or make a good one even stronger.
That's performance marketing.
It's the process of using concrete information
about customer behaviors to plan and refine marketing and sales
strategies.
It focuses on measurable results like clicks and conversions.
Performance marketers set specific goals and use metrics
to find out if they've reached them.
You're already familiar with some performance metrics
like the number of impressions or cost per click on paid ads.
Another performance metric is customer lifetime value,
which refers to the average revenue generated by customers
over a certain period of time.
There's also ROAS, or Return On Ad Spend.
ROAS is how much revenue is gained
versus how much was spent.
So if you spend $100 on an ad but made $150
as a result of that ad, the ROAS would be 150%.
So for the cereal example, if you
were to set a goal of increasing overall revenue,
ROAS might be one of the metrics used to measure success.
There are so many ways to measure performance
at every stage of the marketing funnel.
And those measurements are critical
because the average customer journey
takes about six touchpoints.
That number has doubled more than twice
over a 15-year period.
Performance marketing lets us gauge
how each of those touchpoints contributes to our goals, which
helps us reach and engage with customers more effectively.
Time to review.
Measuring results with performance marketing
is one of the most important things
you can do to ensure success.
By tracking metrics like ROAS and customer lifetime value,
digital marketers can reach their goals
and refine their strategies over time.
Up next, you'll learn more about performance metrics
and working with the data they produce.
[MUSIC PLAYING]
Performance marketing generates a lot
of data from impressions and clicks
at the top of the funnel to conversion and sales
numbers at the bottom.
Data is critical throughout the whole marketing and sales
cycle.
Data is a collection of facts or information.
Your company's total number of social media followers,
how many hours a team spends on a project,
or total year in revenue.
All of those numbers are data.
Marketing data can help you answer questions
in a concrete way by drawing on real customer behaviors
and interactions.
The insights are useful for planning campaigns, predicting
future behaviors, and finding out whether your activities are
helping you reach your KPIs.
You'll recall that a KPI, or Key Performance Indicator,
is a measurement used to gauge how successful a business is
in its effort to reach a business or marketing goal.
Your KPIs could be certain metrics like ROAS.
But if you find that you aren't reaching your goals,
you might need to prioritize different KPIs instead.
To know if you're meeting your KPIs,
you'll need to collect and interpret the relevant data.
The process of monitoring and evaluating
data to gain actionable insights is called data analytics.
And it's one of the most important skill
sets you can develop for a career in digital marketing
or e-commerce.
That doesn't mean you need to be a statistics expert
to work in these fields.
What it does mean is that most entry-level roles
you'll encounter involve working with data in some way.
Let's go over a few of the main data analytics
responsibilities you might have.
Pulling, reporting, and analyzing data.
Data pulling is the process of collecting data
from analytics tools and putting it
into a spreadsheet or database, making it
easy to access and work with.
For example, you might have campaigns
with similar goals running on different platforms
like Facebook, Bing, and Google.
To make it easier to compare and analyze your data,
you'll need to bring it all together.
One way to do that is by pulling the data from each source,
organizing it into a spreadsheet.
Data reporting, also called performance reporting,
involves organizing and summarizing
data to track performance across marketing and sales efforts.
This process makes it easier to identify trends and spot
unexpected results more quickly.
For example, if you've pulled data from multiple sources,
reporting makes it easier to tell if one
has a higher ROAS than another.
Quality reporting provides a clear picture
of the raw numbers.
It should help you shape questions that can
be answered through analysis.
Data analysis is the process of examining data in order
to draw conclusions, make predictions, and drive
informed decision making.
If reporting is what, then analysis is the why.
It helps you develop insights that explain the reported
results and make suggestions for next steps
like shifting your budget or prioritizing different KPIs.
You'll learn lots more about working
with data later in the program.
But I hope you feel like you've got a better
understanding of what data is and why
it's so important for marketing and sales success.
Now let's recap.
The data produced by performance marketing
is an incredibly valuable resource
for understanding how well your strategy is meeting its goals.
In an entry-level role, you may find
yourself pulling, reporting, or analyzing performance data.
Through data analytics, you can find out
if you're meeting your goals, anticipate customer behavior,
and make plans for the future.
Coming up, we'll get into some ways
to interpret data and present it to others.
[MUSIC PLAYING]
All right.
You've set your strategy, picked your channels,
planned your content, and measured your results.
Now what?
You know that all data should tell you
which channels and content are performing well.
But what does good performance actually mean?
Is it when an ad gets a lot of clicks,
when a tweet goes viral?
What about increased website traffic?
With so many different touchpoints and channels
shaping customer brand interactions,
how do you know where to start?
In this video, we'll explore how businesses use reporting data
to find out which of their marketing and sales efforts
are the most successful.
Success can mean different things
depending on your particular marketing and business goals.
But every digital marketer wants to know
which touchpoints are getting customers to take action.
Say you're running a campaign for a company that
sells art supplies.
If tons of people are clicking on an ad for a new line
of paints, but only a few of them
eventually make a purchase, that ad
might not be so successful after all.
In order to optimize a strategy, you
need to know which touchpoints are influencing
customer decisions the most.
Of course, you can't know exactly what
your customers are thinking.
All you have to go on is what they do.
But those customer behaviors can tell you
a lot about where your efforts are succeeding
and where they're falling short.
The process of determining which content and channels are
responsible for generating leads, conversions, or sign-ups
is called attribution.
This isn't something you need to determine on your own.
Most analytics tools include features
that can use your data to find out
which touchpoints and keywords customers interact with before
taking action.
You'll get some practice with these tools later
in the program.
By accurately attributing success to specific marketing
and sales efforts, businesses can make informed decisions
about where to invest their time, budget, and resources.
Now, some people assume that the last touchpoint
should get all the credit.
And it makes sense, right?
People often think that the touchpoint right
before purchase must be the one that convinces a customer
to take action.
But that isn't always the case.
You know that the average customer encounters
six touchpoints on their purchase journey
and that the path isn't always straightforward.
Say someone is shopping for a new computer,
and they decide which model to buy
after the second touchpoint.
But then they put off actually making the purchase.
Maybe they're waiting for a holiday
or for the computer to go on sale.
Maybe they just get distracted.
It might take another touchpoint to remind them
of the purchase they already plan to make.
Both of those touchpoints deserve some credit.
And attribution is how they get it.
Businesses have some choices when
it comes to attribution models.
We won't go over all of them here,
just a few to give you a sense of how they attribute success.
Data driven, first click, last click, and linear.
Data driven attribution measures customer engagement
with marketing content across channels to understand what's
motivating them to take action.
It assigns credit to each touchpoint
based on statistics like which ads or keywords most often lead
to conversions.
Data-driven attribution draws on real customer behaviors
to assign credit.
But if you don't have enough meaningful data for this model,
they have other options.
Here are a few examples.
First click attribution assigns all the credit
to the first touchpoint that eventually
leads to a conversion.
Let's go back to our art supply store.
If a customer's first interaction with the brand
is a social media ad for oil paints,
all the credit for their purchase will go to that ad
even if it takes a few more touchpoints for them
to buy something.
Last click attribution assigns all the credit
to the last known touchpoint before conversion.
If our art store customer makes a purchase
after the fourth or fifth touchpoint, maybe
a promotional email, last click attribution
would give full credit to that email.
Linear attribution assigns equal credit
to each touchpoint along the customer journey.
So for our art store customer's journey, the social ad,
promotional email, and all the touchpoints
in between share credit for the eventual conversion.
Attribution isn't an exact science.
But it can give you a better idea
of how customers are interacting with your content
and what's leading them to take action.
Using models like data driven, first click, last click,
and linear attribution, businesses
can put their resources in places that will
maximize customer conversions.
Up next, you'll learn about communicating
the insights you get from your data insights to other people.
[MUSIC PLAYING]
Working with data isn't just about the numbers.
You know that the process of data pulling, reporting,
analysis, and attribution produces valuable insights that
can change the course of a project or strategy.
But how do you turn those insights into action?
How do you persuade key people that something
needs to be done?
By using your data to tell a story.
Without a story, data is just numbers.
Those numbers can tell you what happened.
But they can't tell you why it happened, why it's important,
and what you can do about it.
That's where data storytelling comes in.
Data storytelling is the practice
of conveying data insights to a specific audience using
a clear and compelling narrative.
Data has a lot of stories to tell.
And no two people will tell exactly the same one.
Everyone brings their own perspectives, experiences,
and biases to data storytelling.
And you'll find out about navigating some
of those challenges in a bit.
For now, let's think about how to create stories with data.
A data story has three main components--
the data itself, a compelling narrative,
and clear visualizations.
Together, these pieces should engage your audience
by explaining what you learned and how
you can use that information to take action.
Let's take them one at a time.
First, data.
Think of your data points like the characters in a play.
If they're all on stage at once, it
can be hard to know where to focus your attention.
So you need to be selective about which
numbers you highlight.
The most important data points are your lead characters,
while others could play supporting roles,
and some may not need to be in the scene at all.
To decide which data points are important,
it's crucial to understand the questions
you're trying to answer.
For example, maybe you work for a toy company
that's measuring the results of a new social media campaign.
Your question could be as simple as asking
whether this new campaign is more
efficient than the last one.
For that question, your main data point
might be return on ad spend.
But remember, you need to understand
the question you want to answer to know what data to use.
Being clear about your questions and the data
you can use to answer them is also
the first step in planning your next piece of your data
story, the narrative.
The narrative is like the plot of a play.
It's what happens in the story.
A well-structured narrative is engaging, memorable,
and persuasive.
Once you've picked the data points and insights that
answer your questions, you can start
building a narrative that conveys them effectively.
In the toy company example, you'd
explain how the ROAS data you've gathered
compares to the previous campaign
and how those results can impact your future efforts.
In other words, your data story should
explain not just what your insights mean
but why they matter to your audience
and what they can do about them.
That doesn't mean you need to have all the answers.
In fact, some of the best stories
create space for discussion.
But appealing to your audience with a strong narrative
can draw attention to your insights
and encourage others to take action.
You'll learn more about how to create an effective narrative
in just a bit.
Now that we've explored a simple narrative structure,
let's get into the last element of data storytelling,
visualizations.
Data visualizations are graphic representations
of data that convey information.
Data visualizations are like the costumes, lightings,
and stage set of a play.
They focus attention and help the audience understand what's
happening in the narrative.
Visualizations can take the form of charts, graphs,
infographics, or other illustrations.
A well-placed visualization clarifies trends
and expresses relationships between data points.
Returning to the toy company example again,
we could choose the represent the ROAS data
as a series of numbers.
But if what we want to do is compare the current campaign
to the last one, it's a lot clearer
to convey that relationship with a bar graph.
It gets the point across quickly and clearly.
You'll learn more about visualizing data and making
presentations accessible to different audiences
in a later course.
Data storytelling is a powerful tool
and one that's in high demand for digital marketing
and e-commerce roles.
Using data, narrative, and visualizations,
we can engage audiences, communicate insights,
and influence big decisions.
Up next, we'll explore more about how
to structure data stories.
MIRIAM: Congratulations on finishing this video
from the Google Digital Marketing and E-commerce
Certificate.
Access the full experience on Coursera including job search
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