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(bright inquisitive music)
- Welcome back.
Let's talk about success.
People love success because it makes them feel good.
If you work on a successful marketing campaign,
you'll feel good about it.
But success is more than a good feeling.
A common way to measure a campaign's success
is by how well potential customers
moved forward in the customer journeys.
Ultimate success is measured
by the number of completed sales.
Metrics can help determine the success
of marketing initiatives and campaigns.
Return on investment is one metric,
and you'll probably hear it more often as an acronym
formed from each of the first letters: ROI.
For ad campaigns specifically there's return on ad spend,
which is often referred to as another acronym: ROAS.
A business always has to make a profit to stay in business.
That's why these metrics on returns are so critical.
You'll learn how to use these metrics
to measure marketing success.
To help increase returns,
tests are sometimes performed
to determine which of two variants
generates better performing results.
These tests are called A/B tests.
They're also called split tests or bucket tests.
They help marketers make decisions
that impact the success of a campaign.
You'll learn more about these tests in upcoming lessons.
And finally, you'll examine case studies
of marketing campaigns with varying levels of success.
You can learn a lot from a case study
that describes how best practices were applied.
You can also learn a lot from case studies
describing what to avoid or what went wrong.
Success feels good,
so I wish you success in this part of the course
and for the rest of the program.
(bright inquisitive music)
How do you measure marketing success?
You were just introduced to the terms
return on investment, or ROI,
and return on ad spend, or ROAS.
Marketers use these calculations
to help them determine the success of marketing campaigns.
Now it's your opportunity to learn how these are calculated.
Let's start with ROI.
ROI is a measure of the profit generated
from a marketing campaign.
More specifically,
it's the ratio of net income or money made to investment,
or money spent.
ROI can be calculated one of two ways.
In the first method, to calculate ROI
subtract the marketing cost from the total sales growth
during the period the campaign was run
and then divide the result by the marketing cost.
Sales growth is a positive increase in sales
when compared to prior sales.
In other words,
it's the positive change in sales or sales lift.
For example, if sales growth was $200,000
and the marketing cost was $55,000,
the marketing ROI is 200,000 minus 55,000,
or 145,000 divided by 55,000.
Their ROI is 2.6.
If you consider an ROI of one as the break even point,
or when sales growth equals marketing costs,
an ROI greater than one is on the upside.
The higher the ROI, the greater the upside.
A second method to measure ROI
uses something called customer lifetime value, or LTV.
Customer lifetime value is the average revenue
generated by customers over a certain period of time.
Software analytics tools can help with LTV measurement.
Google Analytics 4 reports LTV
for users acquired through different channels
over an acquisition date range you specify,
up to 120 days.
This allows you to monitor customer behavior
over several months
to observe the effect of a marketing campaign.
You can find LTV in the Acquisition Overview report.
An upward LTV trend indicates an increasing ROI.
Now let's shift from ROI to return on ad spend, or ROAS.
ROAS is a number calculated as the revenue generated
divided by the amount spent on advertising.
Performance goals are created from and tie into
higher level marketing and business goals.
ROAS targets for digital channels are usually set
relative to an overall campaign level ROAS
across a mix of media.
A marketing goal of 5:1 ROAS
usually produces related ROAS performance goals
for each channel,
such as 3:1 ROAS for search and 4:1 ROAS for display.
The per-channel ROAS results
contribute to the overall ROAS across all media.
If you're monitoring the analytics for a campaign,
you might wonder when your intervention is needed.
If the ROAS isn't meeting your target, what can you do?
This isn't a complete list,
but here are a few things you can consider doing.
You can lengthen the campaign duration.
Measuring conversions is a fluctuating or noisy process.
ROAS can temporarily be lower than a target
due to fluctuations in the marketplace.
These fluctuations may be outside of your control,
so be patient.
A good practice is to evaluate ROAS
after a minimum of 50 conversions have been reported.
You can set ROAS targets by product groups
instead of using an overall ROAS.
Suppose a clothing store sells
evening gowns, dresses, tuxedos, and suits,
and wants a 4:1 overall ROAS,
but isn't able to achieve that goal initially.
Instead of sticking with the ROAS as an overall target,
they can split out the targets by product groups,
like formal and more casual wear.
ROAS by product groups
may provide a more accurate measurement
for the campaign's success.
Review how the ROAS target was set.
If the ROAS wasn't based on
the ROAS from previous campaigns,
or was based on margins that were far too optimistic,
you could reset the ROAS target to a more realistic number
based on the data you now have.
Lastly, if applicable,
you can adjust the automated bidding strategy
to see if that helps you meet the target ROAS.
This is done for automated campaigns only.
As you have now learned,
ROAS is an effective measurement for performance.
Measuring ROAS helps you evaluate
the performance and success of a campaign.
It may take more practice,
but in time measuring both marketing ROI and ROAS
will become a natural part of your job.
They're both key metrics in marketing analytics.
(bright inquisitive music)
In marketing, there are a lot of choices
about content and strategy.
Choices supported by the data to prove it
are the best kinds of decisions.
An A/B test can help you choose
the best content or marketing strategy
for an online business.
An A/B test, also known as a split test or bucket test,
is an online experiment with two variants
and a random 50% split of users between the variants
to determine the better performing option.
Almost all A/B tests randomly send
50% of users to one variant
and 50% of users to the other variant.
For example, an A/B test can randomly direct web traffic
to two different versions of the same webpage.
Responses from those pages are monitored,
and the version that achieves higher performance
based on the chosen metrics wins.
An A/B test is typically performed on live webpages.
It's possible to perform an A/B test
on a website that isn't live
as long as the number of users tested
is large enough to produce
a statistically meaningful result.
Let's say you wanna test two versions
of a direct response ad to join a loyalty program.
One version appears alongside a celebrity endorsement
and the other appears with a savings offer.
Which version will result in more conversions
or people signing up for the program?
The A/B test will tell you which version is the better one
using a sample population
tested during a short period of time.
In this particular test,
it appears that a greater number of conversions occurred
with the savings offer.
An A/B test relies on statistical tests
to determine which of the two options being tested
is more effective.
The statistical tests used during an A/B test
depend on whether discrete or continuous metrics
are used for comparison.
Discrete metrics have specific values,
can be counted, or are binary,
like on/off or true/false settings.
Examples of specific values are click through rate or CTR,
conversion rate, and bounce rate.
Examples of counts are new and returning user counts.
And finally, a binary response could be if a user clicked
or didn't click on a direct response.
Continuous metrics are measured and change over time.
Examples of continuous metrics are revenue per user,
average session duration, and average order value.
The data is continuous because the measurement changes
with each additional session or order.
The key takeaway is that statistical tests
are critical for A/B testing.
The A/B testing software you use
will perform the statistical analysis
on a large enough sample of users to be informative.
When you conduct an A/B test,
you're usually testing if a new version
will improve a metric compared to the original version.
When you plan for an A/B test,
it's helpful to document past performance,
desired improvement,
and the performance metric you'll use for the test.
Your organization might even have a template
to help record this information for each test.
There are many software tools available for A/B tests.
You can conduct A/B tests on ads with Google Ads.
Performance marketers also rely on A/B testing
to identify pages with the best performance.
Google Optimize lets you run A/B tests
on your website content
to understand what works best for your visitors.
HubSpot performs A/B testing
on email messages and landing pages.
Optimizely offers A/B testing for a variety of touchpoints
in the customer journey.
Intellimize helps personalize webpages with machine learning
and what could be considered as continuous A/B testing.
These tools aren't being recommended above others,
but are mentioned to point out
that a wide variety of solutions
are available from different vendors.
Whichever tools you use if you conduct A/B tests,
you'll have greater confidence
that you'll meet performance goals,
increase the number of conversions,
and benefit from improved customer experiences
through redesigned webpages that have been tested.
Your choices do make a difference.
(bright inquisitive music)
Door number one or door number two?
Sometimes surprises are fun,
but if you're running a campaign
you want to minimize surprises when changing an ad.
You'll learn that an A/B test
typically tests whether a new version of an ad or webpage
will improve metrics compared to the original version.
This video will demonstrate
how to set up an A/B test in Google Ads
to compare the performance of two ad variations.
An ad variation can test changes to ads,
like a change to a URL, headline, or call to action.
For example, you can test how changing a headline
from "Act now while supplies last"
to "Huge savings, limited time offer"
impacts sales across ads in multiple campaigns.
Even a small change to a call to action
from "Buy today" to "Buy now" can be tested.
But not all changes require a test.
It's certainly reasonable to make minor text changes to ads
without fully testing them first.
Using the first example of alternate headlines,
here is how you would set up an ad variation test,
a type of A/B test in Google Ads, to test them.
Assume that the current or existing headline
is "Act now while supplies last."
In the Campaigns page in Google Ads, click All Campaigns.
Then click on Experiments in the Navigation panel.
Then create a new ad variation by selecting the plus icon.
Select whether this ad variation applies to all campaigns
or select which campaigns this variation affects.
Let's assume this variation applies to all campaigns.
Next, select that you're updating text
and that the edit is for headlines.
Finally, enter "Huge savings, limited time offer"
as the text for the headline in the variation.
Let's name this variation Huge Savings.
Use the default start and end dates for a 30-day run
and keep the default 50% split for the test.
To create the variation, click Create Variation.
After the A/B test for this ad variation runs,
you'll see the results in the Ad Variations table.
Here is an example
of what the results would look like in the table.
You'll still see metrics like clicks and impressions
moving horizontally across the table.
Each metric will show a positive or negative percent,
which tells you the amount of change
there was between the two variations tested.
You'll want to pay attention
to the percentages with blue stars next to them.
These stars indicate
that there was a statistically significant amount of change
between the two variations tested.
If many metrics are displayed with stars,
you might consider making the new variation
a permanent ad change.
This was an introduction to A/B testing
using an ad variation test in Google Ads.
You can also use experiments in Google Ads
to test campaign bidding strategies.
Refer to the Google Ads help or reading for more information
about testing bidding strategies.
Finally, as mentioned earlier,
remember that you don't need to test
every change or variation.
Because A/B tests take time and budget,
it's important to be strategic and selective
about the ads that you and your team decide to test.
These decisions are unique for every team.
(bright inquisitive music)
Many metrics can be monitored during a marketing campaign,
so how can you conclude
whether a campaign was successful or not?
The insights you use to evaluate
the success of a marketing campaign
depend on the marketing goal or goals
and what the campaign was trying to address specifically.
But the main success factor is going to be
whether you met the performance goals that were set.
Let's use two examples to understand success indicators.
In the first example,
an overall marketing goal
was to increase the number of leads.
Leads were measured as micro conversions.
A micro conversion is a completed response
that indicates that a potential customer
is moving towards a completed purchase transaction.
A micro conversion performance goal
of increasing email signups by 20% was set.
From past experience,
people who signed up to be on an email list
had a 50% chance of completing a purchase.
Therefore, a macro conversion performance goal
was set for 60% of people signing up to complete purchases,
a 10% improvement.
Next, similar targets for chat bot conversations
and blog page visits were set.
The chat bot was new,
so the goal was for at least 20 conversations
to occur during the campaign.
Blog page visits spiked by 40% recently,
so a performance goal was set
to increase new visitors to the blog page by 20%.
Macro conversion performance goals were set.
The goals were that 30% of chat bot users
would complete purchases
and 10% of new blog visitors would complete purchases.
For email, the results show that the campaign
increased email signups by 21%,
but it seemed to have no effect on completed purchases,
which remained flat.
For chat bot conversations,
both micro and macro conversion goals were met or exceeded.
For the blog page,
both micro and macro conversion goals
were missed by quite a lot.
So was the campaign a success?
Based on the metrics, there were mixed results.
Some parts of the campaign were underperforming,
but other parts of the campaign were very successful.
Several things did become clearer for future work.
Chat bot engagement should continue
because of the conversion pull through.
Email messages should be reviewed and modified
to possibly provide a better lift in conversions.
And finally, not too much time or energy
should be spent on blog campaigns.
In the second example,
a marketing goal was to increase online sales
by doubling the average order value.
In other words,
the average order value was $50
prior to the marketing initiative,
and the goal was to increase it to $100.
This also served as the performance goal.
The digital marketing campaign included direct responses
so customers could click to view additional products
before and during their checkout.
There was also a promotional ad
for customers to spend $100
and receive free shipping.
Metrics monitored were the online sales revenue,
number of orders, average order value,
and the cost per sale
to keep return on investment, or ROI, in check.
Secondary metrics of interest
were orders by geographic region,
top selling products contributing to larger orders,
and the ratio of orders from new versus returning customers.
Although the average order value increased by 45% to $95,
just under the performance goal to double it,
the overall trend showed a very successful campaign.
Online sales revenue went up by 15%,
the number of orders increased by 170,
the cost per sale went up by 3%,
orders by geography didn't change,
the top selling products that contributed
to the increase in order value
were small kitchen appliances and electronics.
And finally, the ratio of orders
from new versus returning customers
went up from 1 to 1.4.
To summarize, the two examples in this video
showed that metrics of interest
are always dictated by the goals of the marketing campaign.
The metrics used to evaluate success
were completely different.
The benefit of using marketing analytics tools
is the ability to view metrics and gather insights,
and these insights help you to evaluate
and define the success of any campaign.
Campaign success will vary,
but the insights gained always have current or future value.
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