Jesse Stein

I hope you enjoy reading this blog post. If you want our handwritten notes to drive more sales for you, click here.

Data-driven decision-making is crucial for today’s digital marketers. While marketing campaigns are developed through a creative process, data informs every aspect, from identifying the target audience to refining the final version of the advertisement.

To maximize the impact of your marketing efforts, you must learn how to access, interpret, and apply analytics data. Although collecting data is simple, utilizing it to make informed decisions requires practice, dedication, and expertise.

Understanding Metrics and Analytics

Even the most feature-inclusive customer data platform won’t be useful unless you can read the data. Luckily, almost all analytics platforms present the same kind of data in a similar format. If you know how to read a pie chart or make sense of a table, you’ll be able to understand almost any metric you’re presented with.

What Is a Marketing Metric?

A metric is any value that you can measure and track. A metric might be the visitors to your site, the people who have followed you on Facebook, or the number of purchases made at one of your retail branch locations.

Most metrics are understood based on individual traits. For example, you may have a metric that represents your total site traffic in the last month. This data can be categorized by where the traffic comes from. Once broken up, you’ll have a table with different numbers for your traffic from Google, social media, guest posts, and your latest advertising campaign.

A key performance indicator, or KPI, is a metric that is extremely valuable to your decision-making process. If you’re in charge of your own marketing, you get to decide which metrics are worth classifying as KPIs. Common KPIs include site traffic, sales, and customer engagements.

Where Analytics Data Comes From

The main attraction of digital marketing is that nearly every channel can provide analytics data. Customer movements are tracked during site visits, on social media, and even across the internet through the use of browser cookies.

The reason customer data is so ubiquitous is that programs already translate interactions into data to function. Asking a piece of software to keep a record of queries and engagements requires little more than an extra bit of code and some storage space.

The ubiquity of customer data has given rise to questions about the ethics of data tracking. The current standard is to ask for a customer’s permission before gathering data on their behavior. If data tracking is inherent to your platform, you need to notify the customer before they use your program or site.

What Can You Track?

Analytic data can typically be broken into two categories: behavioral data and demographic information.

Behavioral data is gathered based on a customer’s use of your platform. Every time a customer engages with an ad clicks on a different page of your site or hovers over a certain image, your data tracking software can make a note of their behavior.

Behavioral data is extremely valuable for crafting the user experience, and it’s one of the strongest indicators of a channel’s overall success. Some behavioral data is easy to track, but gathering detailed behavioral information usually requires a software extension.

Demographic information identifies a user based on their individual traits. This can include anything from where the user is located and what device they’re using to what their age, gender, and occupation are.

Demographic data is usually provided by users when they make an account on social media or with a service like Google. The companies that gather demographic information are legally required to protect the privacy of their customers, so this data is almost always completely anonymous. Demographic information is most useful for researching and understanding your target audience.

The Customer Journey

The customer journey represents the path of your audience members as they learn about your brand and begin to engage with your product. A large part of analytics is mapping each part of the customer journey and understanding which factors influence customer decisions.

A touchpoint is a trackable moment in the customer journey. Common touchpoints include viewing an ad, visiting your website, clicking on an internal link, or using a QR code on handwritten notes. Touchpoints can be either physical or digital – the important thing is whether they can be tracked.

A customer journey map is a theoretical model of an average customer’s path. Marketers use analytics data and audience understanding to write down the steps they think a customer might take. Creating a customer journey map will let you identify the points at which you can engage with the customer and positively influence their decision-making process.

Attribution Models

Most analytics platforms use attribution models to help your data make sense. If you want to accurately read your conversion metrics, you need to understand which attribution model was used and how the data was translated into the graph that you’ve been presented.

Defining Attributions

In the world of marketing, attribution refers to the marketing channel that is credited for a successful conversion or sale. Attributions are determined by tracking the paths visitors took to find your site and the factors that encouraged them to finally click on your CTA.

Marketing campaigns are built from a variety of site elements and communication channels. It’s not always easy to tell which factors influence a customer’s decision to make a purchase. In some cases, multiple factors could all be given credit for the same conversion or sale. That’s why attribution models are used to help you decide where the credit should go so that you can use your data for meaningful conclusions.

Which Attribution Model Should You Use?

An attribution model refers to the way that a marketer or data analyst assigns this credit. There are many different models, but most analytics platforms base their design on a standard attribution template.

  • Linear attribution assigns a point of credit to every point of engagement. This model works well for tracking customer interactions, but it doesn’t help you determine which channels had the most influence on the final decision.
  • Time decay attribution works like linear attribution, but the amount of credit is adjusted to reflect how recently the customer engaged with each touchpoint. Recent touchpoints get more credit, and old touchpoints get less. This model does a good job of mapping the customer’s total experience with your brand.
  • Last-interaction attribution assigns credit to the last action a customer takes before they decide to convert. This model is simple and easy to track, but it doesn’t take a customer’s entire journey into account. In many cases, credit goes to a clickable sidebar ad instead of the high-budget commercial that drove traffic to your site in the first place.
  • The first-interaction attribution assigns credit to whichever ad or channel caught a customer’s interest. This model is better for understanding how customers learn about your brand. However, it doesn’t explain which features encouraged the customer to finally convert.
  • U-shaped attribution assigns equal credit to both the first and last interactions. A lesser amount of credit is also assigned to any engagements that happen between these interactions, thus creating a relatively accurate map of the customer journey.

Depending on the platform you use, you may actually be able to craft a custom attribution model that reflects a personal understanding of your customer base. These designs take time to map, and the data they provide will only be helpful if you understand how your model works.

Attributions are used to make decisions about future allocations of your marketing budget. If a channel receives more credit, it’s more likely to be worth the time and money you funnel into that part of your campaign. The actual model you use doesn’t matter nearly as much as your interpretation of the data after it’s presented.

Choosing a Marketing Dashboard

A marketing dashboard is the place where your analytics data is gathered, translated, and reproduced as readable charts or graphs. Every platform that offers analytics data has its own dashboard. Also, many marketers like to use aggregate dashboards that pull data from multiple different sources.

If you work for a large-scale company, your marketing dashboard may actually be part of your customer relationship management system. But even if you don’t use a CRM, you can benefit from a customer data platform or a business intelligence application.

Although there are a few free marketing dashboards, most of these platforms require a monthly subscription. Before you make a purchase, make sure to ask questions about the quality of the data and the overall function of the software.

  • Where does the data come from? Most dashboards will let you import data from Google Analytics, social media, and other marketing channels. Always check to see how the data is imported and whether the dashboard covers all of your analytic needs.
  • How is the data attributed? Every software uses different rules to decide the way that data is categorized. If you don’t understand these rules, the data won’t actually make sense.
  • Which KPIs are focused on? Some dashboards provide the data and let you make the decisions. Other platforms will highlight certain KPIs. Make sure those KPIs match the statistics you care about before you make a decision.
  • Is the interface accessible? A dashboard is only as good as the marketer’s ability to read and interact with the data. Play around with the interface; if you’re still confused, choose different software.
  • How much can you customize the reports? You might know your way around a platform, but that doesn’t mean everyone who needs to see the data will have time to learn. Look for a dashboard that will let you create custom reports with your most relevant data.

Google Analytics

Google Analytics is a fully-functional dashboard that can track every aspect of the customer journey that relates to either your website or a Google product. Google Analytics works great as a stand-alone resource for beginning marketers. Most advanced dashboards also integrate Google data into their final report.

Google Analytics is mostly focused on on-site traffic. Use this dashboard to understand who is using your site, where they are coming from, and what devices they used to get there. Once you’re comfortable with the platform, create audience segments, and set trackable conversion goals to get an even more detailed picture of audience behavior.


Cyfe is a business intelligence tool that lets you create a custom dashboard for all of your favorite analytics channels. Cyfe works with any platform that has API integration and comes with a wide range of customizable features.

Creating a Cyfe account is free, and most of the paid features involve cosmetic changes or additional user accounts. One of the best features of this platform is that you can create unique dashboards for each of your clients that include campaign-specific data and company branding.


GoodData is another business intelligence tool that imports data through APIs and other data streams. You can use GoodData to create custom dashboards and explore your data from a wide variety of different angles.

What truly separates GoodData from the competitors is the platform’s machine learning algorithms. GoodData will use internal rules to help create actionable insights for you and anyone else on your marketing team. As you feed the program more data, the insights become more accurate; just remember to keep tabs on the rules and algorithms that are used to make decisions.


Grow is an efficient business intelligence tool with a simple interface and customizable reports. If you’re looking for a no-nonsense data aggregate, this dashboard will do everything you need and more.

With Grow, you can create unique datasets that represent information gathered from multiple sources. This lets you view clean graphs and make informed decisions without spending too much time on data research and interpretation.

Performing a Digital Marketing Audit

A digital marketing audit is the process of reviewing your current marketing strategies and checking them for success. Marketing budgets are limited, and analytics data can often help you decide whether a channel is worth the time you’ve put in.

If you’ve just learned how you use analytics data, you should conduct your own digital marketing audit right away. After that, staying on top of your data will mean that you don’t need to conduct audits regularly. Consider re-auditing your channels once a year to make sure your strategy is still performing as expected.

List Your Marketing Channels

First, make a list of every single channel that you currently use to reach your audience. Every company is unique, but you’re probably using one or all of the following channels.

  • Your website: You have more ability to gather data and make changes to this channel than any others, but that doesn’t mean it should be the sole focus of your marketing audit.
  • Social media: Write down every platform that you use. Include Facebook, Twitter, LinkedIn, YouTube, and any small channels that are unique to your audience.
  • Email marketing: If you use an email marketing platform, you should be able to easily track metrics for your monthly newsletter. If you don’t have a newsletter, you might be missing out on an important way to reach your customer base.
  • Paid media: Paid channels include social media advertising, search engine marketing, and any other ad that you have paid to place. These channels take up a lot of your budget, so trust the metrics to see if they work.
  • Handwritten marketing: Direct marketing is coming back strong with handwritten notes. Real estate agents, pest control companies, electric service providers – everyone is using handwritten notes and they are effective!

Gather Audience Data

Next, use your chosen marketing dashboard to gather and review data from every channel that you currently use. Your goal is to create a complete report that shows where customers interact with your brand and whether their interactions lead them to make a purchase or complete a conversion goal on your site.

One of your main reports should be a chart of all traffic to your site; you can easily get this data from Google Analytics. If a channel sends a lot of traffic to your site, it’s high-performing. If it barely sends any traffic, it’s low-performing.

Your next consideration should be whether the traffic from a channel actually results in conversions. Some conversions are worth more than others, so look for data that tells you how much profit each new client actually brought in.

If you use multiple channels, you’ll have a lot of data to review. Rely on your marketing dashboard to help you consolidate information into meaningful tables and graphs. Pay attention to KPIs, but don’t forget that smaller metrics might actually say a lot about audience behavior.

Review Your Top-Performing Channels

Ideally, you’ll want to spend time auditing every marketing channel on your list. Go ahead and start on a good note by reviewing the channels that are already bringing you success.

Draft an individual report for each channel that explains how you and your audience interact. Key questions to ask include how many people use that channel, what demographics they tend to fall in, and how likely they are to convert once they’re on your site.

Review the metrics for your chosen channel, and think about which elements of your strategy seem to be responsible for your success. Is there anything you can do to further optimize this channel? Try devoting more resources to this avenue, and track the data to see if extra attention results in even more traffic and conversions.

Review Under-Performing Channels

A channel is under-performing when the traffic that it brings in doesn’t account for its share of the marketing budget. Sometimes, an ad campaign simply doesn’t pan out; reviewing your analytics can help you figure out what went wrong and how you can make different decisions in the future.

Bounce rate is a KPI that is directly relevant to negative performance reports. If you have a high bounce rate on a certain landing page, it means that customers leave immediately after clicking on the ad. Review both the ad and the landing page to find the discrepancy. Check to see if you can improve ad relevance, content quality, and elements of the page design.

For poorly-performing paid media channels, check to see which audience you’ve been targeting. Are your ads going to the right people? If so, why aren’t they responding to your campaign?

Attribution models are important because they’ll tell you whether a channel was partially influential in a customer’s decision. Even if no one clicked on your YouTube ad, seeing it might have caused them to search for your website after they finished watching a different video. If you use a first-click or last-click attribution model, you might count an ad campaign as a failure when it was actually directly beneficial to your brand. This is why linear, time delay and u-shaped attribution models are currently the most popular choices.

Analytics data is only helpful if you know how to read it. If you’re new to analytics, you might want to brush up on your statistics understanding to make sure you’re reading the graphs the right way.

You should also remember that human behavior can’t always be predicted from previous data. Let analytics inform your decisions, but don’t forget that creativity and audience empathy will always lead you to marketing success.