Throughout this guide, we’ve emphasized how important it is to measure the results of digital marketing tactics, campaigns, and strategies. These activities are so important that there is a special term to describe them: performance marketing.
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In this article, 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 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. 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 behaviours 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. 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 15 years.
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.
Common metrics for success
Typically, when you set goals, you track your progress to see how close you are to reaching those goals. If you set a goal to finish a book every month for 12 months, you will probably check on your progress now and then to see if you are accomplishing that goal. You may count pages with excitement as you go from book to book, or keep a checklist of book titles. The same practices can be applied to measuring marketing campaign effectiveness.
In this reading, you will learn the importance of measuring success. You will get an understanding of what you may want to track to measure success, and you will be reminded of what a metric is. Then, you will see where various metrics fit into your marketing funnel.
Introduction to metrics
As you go through these digital marketing articles, you will learn a lot about metrics. For now, know that a metric is a quantifiable measurement that is used to track and assess a business objective. Metrics help determine the success of marketing initiatives and campaigns.
Why track metrics?
Tracking metrics helps digital marketers gauge how close they are to meeting goals. Each metric measures something specific, and therefore each metric tells a marketer something different about their campaign. Metrics can reveal important information about marketing campaigns, such as return on investment (ROI), return on ad spend (ROAS), cost per sale, and online and sales revenue.
Metrics in the marketing funnel
You will apply different tactics to track metrics based on which stage of the marketing funnel you are operating in. For instance, in the awareness stage, you’ll gather audience data and develop user personas. This helps you get to know who your customers are. During the consideration stage, you will consider metrics like the cost of acquisitions and click-through rates. During the conversion stage, you will track and analyze sales conversion rates, average order values, and cart abandonment rates. And finally, during the loyalty phase, you’ll want to consider customer retention rate and customer lifetime value.
There are other factors to consider throughout the marketing funnel process, and these may not be familiar terms yet, but for now, this is a good place to start.
Tracking metrics is critical to a campaign’s success. Metrics help digital marketers gauge effectiveness and audience contentment while a campaign is happening. They also help marketers gain information and insights they can use for future campaigns.
Working with data in Performance Marketing
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 sale 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 concretely by drawing on real customer behaviours and interactions. The insights are useful for planning campaigns, predicting future behaviours, 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. 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 and 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 to conclude, make predictions, and drive informed decision-making. If reporting is the what, then the analysis is the why. It helps you develop insights that explain the reported results and make suggestions for the next steps, like shifting your budget or prioritizing different KPIs.
You’ll learn lots more about working with the data later in this series. 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 a 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 behaviour, and make plans for the future. Coming up, we’ll get into some ways to interpret data and present it to others.
In a previous article, you learned that performance marketing requires a lot of data. Data can contain information about user interests and behaviours and even individual customer purchases. This reading introduces you to data ethics. Knowing how to work with user data responsibly and legally is critical to the integrity of your organization, role, and projects.
Data ethics is the study and evaluation of moral challenges related to data collection and analysis. When it comes to data, businesses apply ethical practices so they can:
- Follow regulations
- Demonstrate trustworthiness in protecting customer data
- Ensure the use of customer data is fair and without bias
- Follow regulations
Many countries have laws regarding the generation, recording, curating, processing, sharing, and use of personally identifiable data. Personally identifiable data (PII) is information that can be used to directly identify, contact, or locate an individual. Make sure you are aware of your organization’s data security and privacy protocols. Data privacy refers to the proper handling of data. How you collect, process, analyze, share, archive, and delete data should be by the data privacy laws of the countries where your customers reside.
Protect customer data
One important way to protect customer data is data anonymization. Data anonymization refers to one or more techniques to mask or remove personal information from data to protect the identities of people. Data anonymization is often performed on data coming from multiple sources. After the data has been anonymized, it can be more widely and freely shared in an organization. Types of data often anonymized are names, telephone numbers, email addresses, photographs, account numbers, and purchase transactions.
Use data fairly and without bias
Another ethical data practice is making sure that the data you collect and use is for legitimate business purposes. Fair and reasonable use of data also means that you don’t use the data in a biased manner. Data bias is a type of human error that skews results in a certain direction. Note that data bias isn’t the same as selecting data from a target audience.
For example, let’s say you want to review historical data from customers between the ages of 21 and 45. That’s not data bias. What would be considered data bias is if you exclude the data from customers who returned products because you don’t consider them loyal to your brand. However, even when including all available data, you’re not always free of bias. This is possible if historical data was from an audience that wasn’t representative of all potential customers. If you create future ad campaigns based on previous customer behaviours, you could unknowingly perpetuate a bias.
Pro tip: To minimize the risk of data bias, ask for a peer review of critical data that you intend to use so you can incorporate different perspectives right away.
Data ethics is important because it promotes the responsible use of customer data. Always be careful to follow the data privacy laws in your country and the countries where your customers live, protect customer data, and avoid data bias.
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