There are many skills in digital marketing and e-commerce contribute to marketing and ad strategies such as creating search, display, and social media ads, running e-mail campaigns, expanding customer reach, raising brand awareness, and engaging customers with content.
Some additional skills can be classified under the category of analytics. These include how to set and monitor campaign performance, analyze metrics, identify trends, optimize customer engagement, and gather insights for future campaigns.
Marketing analytics skills
Marketing analytics can be applied to a customer journey, website, application, or marketing campaign. They all depend on a process to monitor desired KPIs and performance goals. During a campaign, your team might set goals, run tests, monitor metrics, make adjustments, and then repeat the process until the desired goals are met.
Software tools are required for marketing analytics – Google Analytics and Google Ads. These tools will help monitor metrics and measure campaign performance. Google Ads can be used to run tests on pages, ads, and target groups.
Google Optimize can plug into a website to test content options. These kinds of tests are called A/B or split tests. An A/B test, also known as a split or bucket test, is an online test of two variants that determine the better-performing option.
Suppose you have two versions of a direct response. One goal of an A/B test might be to test which page and which response performs better based on the number of clicks. During the test, traffic is equally split between the two pages. In other words, 50% of traffic is randomly directed to one page and 50% of traffic is randomly directed to the other page.
One direct response outperforms the other by receiving more clicks. As a result of the test, you deploy the direct response ad that got more clicks. Teams choose their tools based o capabilities, features, and cost. Some tools are designed for monitoring of events, like click analysis, monitoring of visuals and graphics, or displaying dashboards.
Other tools are designed for more sophisticated analytics. The tools you use will often depend on a combination of organization, team, and project needs. Be open to using tools that you haven’t used before. You will also want to be aware of what the tools can or can’t do before you use them. Understanding the capabilities of the tools you use will enable you to choose the metrics that work best for your project.
Introduction to Google Analytics
There is a Google Analytics demo available to everyone with a Google account. The demo contains live data from the Google merchandise store and floods it, a gaming app in Google Play. When you access the demo, you must choose a property to view. Click here for the demo account.
A Google Analytics account contains one or multiple properties. A single property can contain combined metrics for a website and app, but multiple properties are useful if a business has multiple websites and apps or has very distinct user segments on a single website or app.
When you create a new property, you specify the website, app or page, so that a new measurement ID can be established and metrics collected.
Let’s open up the Google Analytics 4 property for the Google merchandise store. Select Google Merchandise Property from the drop-down menu.
There are UA properties and GA4 properties. UA properties are for an older version of Google Analytics that collects website metrics only. Google Analytics 4 collects metrics from both websites and mobile apps, new accounts should use GA4 properties.
There is also an attribution project for them, the Google merchandise store in the demo.
A conversion can be a macro conversion or a micro conversion. A macro conversion is typically a completed purchase transaction. A micro conversion is a completed response that indicates that a potential customer is moving towards a macro conversion. Micro conversions are referred to as other touch points in the previous definition for attribution. Attribution projects provide organization for both macro and micro conversions.
Take time to explore the whole Google Analytics 4 interface. The real-time menu displays current user activity on the website. Then, the life cycle menu displays information about the customer’s life cycle. The acquisition sub-menu has details about user and traffic acquisition.
The engagements sub-menu has details about events, conversions, pages, and screens. Next, the monetization sub-menu has details about the website and in-app purchases. The retention sub-menu has information about user retention and lifetime value over 120 days.
User retention measures how many new users returned to the website over some time, while customer lifetime value is the average revenue generated by customers over a certain time. The user menu breaks down demographics and devices for engaged users on the website.
Within these menus, there are sub-menus that you can explore on your own for a better understanding of GA4.
Free online training
- Google Analytics Academy:: (Universal Analytics only) Google Analytics courses for beginners, advanced users, and power users
- Google Analytics YouTube Channel: Videos and product help for Google Analytics
- Google Analytics Blog: Current articles about Google Analytics
- Google Marketing Platform Academy (Google Analytics 360): Live and on-demand webinars and tutorials for Google Analytics
- Skillshop training for Google Analytics: Learning paths for Google Analytics 4 or Universal Analytics
- Google Analytics Individual Qualification: the qualification track covers basic and advanced Google Analytics concepts
- Udemy Google Analytics Courses
- LinkedIn Learning
Introduction to Google Ads
Google Ads is Google’s online advertising platform. Google Ads; a marketer can create online ads to reach specific audiences interested in the products or services their business offers.
When you sign into Google Ads, you’ll see a dashboard or overview of all campaigns. You should be able to find your campaign listed in the draft campaigns card or the campaigns card in the dashboard.
Active campaigns are listed with links. Click an individual link to view the settings for a single campaign. The overview page is a high-level view of how your campaigns are performing. You can also click to start a new campaign.
One important column check is the budget column. This is where you monitor the budget spent, and any campaign limitations you have in the budget.
On the recommendation page, you’ll see a percentage that serves as an optimization score. The closer the score is to 100 percent, the better your advertising is.
You can review the recommendations to potentially take action on one, or more of them to help improve the optimization score of your advertising. If you have more than one campaign, the optimization score is cumulative for all campaigns. Each recommendation is shown as a scorecard with a predicted impact that would benefit a campaign.
The report page is where you can pull reports for a campaign performance. You can use predefined templates for reports, or build a custom report by choosing the metrics you want to include.
For example, the landing page report shows performance metrics, conversions, days to conversion, and more for each landing page. These performance metrics are extremely helpful for ad placement.
Resources to learn more about Google Ads
- Google Ads Tutorial Series: Search
- Google Ads Tutorial Series: Display
- Google Ads Tutorial Series: Video
- Google Ads Tutorial Series: Shopping
Other tools for marketing and analytics
|Tools for marketing analytics||Marketing suites with analytics||Open source tools||Advertising platforms|
. Visitor Analytics
|. Adobe Analytics|
. Adobe Marketo Engage
. Open Web Analytics
Big data for marketing analytics and automation
Marketing is changing with the rise of Big data, predictive analytics, and AI. Marketing professionals are noticing serval trends. Two are related to analytics and two are for automation. Big data makes these trends possible.
Big data can also refer to the large datasets themselves. Financial companies use big data for risk analysis. Manufacturers use big data to optimize supply chains.
Here’s how marketing organizations are using big data.
The first trend is real-time analytics. Real-time analytics monitors immediate data to gain insights to respond to events more quickly. If you think about it, marketers can adjust a marketing campaign only as fast as they can monitor the data. And the more detailed the data, the better. If big data is pulled together and filtered with greater speed, marketers can respond to underperforming aspects of a campaign immediately, or in real-time. So, if real-time analytics tells them a target audience isn’t responding, the message for that audience can be adjusted right away.
Big data also plays a role in what is called predictive analytics.
So, if predictive analytics is applied to models created from collective browsing histories, marketers might be able to identify the right audience for a successful campaign early on. Predictive analytics can also help marketers choose an optimal page or an ad without performing an AB test, saving both time and money.
For example, autonomous marketing can adjust an underperforming message automatically. This can increase the impact of multi-channel marketing campaigns.
Autonomous marketing can also be highly effective to promote and maintain customer loyalty programs. Artificial intelligence, or AI for short, is a field of developing intelligent machines and software that simulate human thought or work. Multi-channel campaigns are often difficult to manage because of the amount of content that needs to be created for each channel. If AI can be used to help create and personalize content, marketers can offer context-specific experiences for users. And optimized experiences in e-commerce can turn more browsers into buyers.
These trends are finding their way into platforms and systems. Automation and AI are new standards. These trends continue to grow new roles will open up in marketing.
Happy learning and good luck!