- Marketing analytics is the process of digitally tracking and analyzing customer behavior and the impact of marketing efforts.
- There are three types of data: first party, second party, and third party. They help find different information about customers.
- Analytics can be descriptive, diagnostic, predictive, or prescriptive, depending on whether you are evaluating the past, present, or potential future actions.
- Marketing analytics benefits your company by helping you make data-backed decisions, giving you real-time feedback, and communicating your successes outside the marketing team.
- You use marketing analytics on your website, across your digital presence, on social media, and in competitive analysis.
- The major challenges in marketing analytics are getting high-quality data, managing large volumes of data, integrating data from different channels, and discerning what actions to take informed by your insights.
- Your marketing analytics software should manage data easily and communicate results clearly via data visualization and customizable reports and dashboards.
- To analyze your marketing data, you need to set goals, decide on metrics to track, segment your audience, set a baseline, and, finally, collect data and run tests. Your marketing analytics insights should result in concrete actions that impact your business goals.
What is marketing analytics?
Marketing analytics is how a company digitally tracks and analyzes the impact of its marketing efforts. Marketing analytics uses software that gathers data about user behavior and extracts patterns that give marketing teams insight into their customer base. With this data, marketers can optimize their marketing approach to increase conversion and revenue to achieve business goals.
You can use marketing analytics to understand how well current marketing efforts are working and decide what you could do to improve your approach. Marketing analytics is just one part of your business’s digital optimization, which gives your teams access to high-quality digital data in real time, so they can make informed decisions quickly and confidently.
Marketing analytics gathers data
The core of marketing analytics is the data you gather to then drive your marketing efforts. There are three types of data you can collect.
- First-party data: The data your company collects directly from your customers. It can be the most useful type of data because you are in control of it, and it is more complete and specific. It is also directly and specifically relevant to your product because it comes from customers’ interactions with your own channels, such as your product interface, website, sign-up forms, or sales team. Marketing will need to collaborate with product teams to get complete data for your entire customer lifecycle.
- Second-party data: Information that is collected from a trusted partner. A good example would be data collected from Google Ads. You didn’t collect it directly, but you know its source, and the information is directly relevant to your business.
- Third-party data: Data that is purchased from a source that doesn’t have a direct relationship with customers. Companies specialize in collecting, aggregating, and selling information from many sources across the web. Its primary advantage is scale. This data gives you a huge base to draw from and gives you a much broader insight into the market and your target customers. You need to be careful about privacy when working with third-party data.
Marketing analytics draws insight from data
With marketing analytics, you gather information and then use it to draw insights that help your company make business decisions. When evaluating your marketing analytics, you will look at the full lifetime of your marketing strategy. Analytics can be sorted into four types. All four are useful, but you should choose where to focus based on what you’re measuring and what insight you are trying to gain.
- Descriptive analytics: Measure the past. Gain insight from past campaigns about what worked and what didn’t. Gather long-term information on customer lifetime value and return on investment.
- Diagnostic analytics: Why did a particular thing happen or have a specific result? Delve deeper into your analysis and seek more specific data in order to understand what it is saying about your past marketing efforts more thoroughly.
- Predictive analytics: What might happen in the future? This form of analytics tries to predict how a potential campaign might perform, what results a particular action might have, or how a specific cohort might behave. This is a good way to evaluate future campaigns based on past performance.
- Prescriptive analytics: Recommends a course of action based on your predictions. This is the most complex form of analytics to perform and requires solid data and experienced people to perform it. It is also the one that can have the biggest business impact since it can help you find better, more profitable marketing approaches.
The importance of marketing analytics
Marketing analytics are important because they give you insight into the performance of your marketing efforts based on real data. With this information, you can tailor your marketing approach to support your business goals more effectively.
It ensures your decisions are backed by data
Marketing analytics isn’t just about gathering data; it is about analyzing data in order to draw out trends and gain deeper insights into customer behavior. Those insights help your team make better strategic decisions. Data democratization is about building a culture of data fluency across your team. If your team can work with data more comfortably, your marketing efforts will be more informed and more effective.
It gives you real-time feedback on marketing campaigns
Marketing analytics will help your team tweak your marketing approach to be more effective immediately based on the behavior of customers. If you are running an email campaign and conversion is low, analytics will help you understand why, identify the problem, and improve it. For example, a high click-through rate but an equally high bounce rate might show the email campaign was effective, but the CTA on your landing page wasn’t effective. Alternately, a low click-through rate shows your email campaign could use a second look.
It demonstrates the value of your marketing efforts
Marketing analytics also helps marketing teams communicate the impact of their efforts and prove the return on investment (ROI) of their marketing strategy. Give leadership solid numbers to back up your team’s approach. In a recent McKinsey study, 83% of CEOs globally saw marketing as a major growth driver. Backing up your successes with data will show leadership how much the marketing team is contributing to that growth.
Where is marketing analytics used?
You can use marketing analytics across all of the channels you use to communicate with customers. The approach to analytics will vary whether you are analyzing behavior via your website, email campaigns, third-party advertising, or social media, but analytics software will help you aggregate that information and draw conclusions from it.
Web marketing analytics
Marketing analytics is used to understand visitor behavior on your website and to evaluate how effectively your website and messaging are capturing and converting customers. Your website is one of your primary marketing assets and a primary source of interaction between your company and your customers, so it is important to have thorough information about the impact of your approach there.
Some of the main metrics web analytics looks at are:
- Page load times
- Effectiveness of calls-to-action
- Time spent on site
- Visitors’ geographic location
- Returning versus new visitors
Digital marketing analytics
Digital marketing analytics focuses on collating your broader marketing efforts on all channels outside of just your website. It is important to get the big picture of where your customers are being reached and how your overall marketing strategy is working. It can be harder to get good data here because it is coming from multiple sources that might collect data differently.
Digital marketing analytics might look at:
- Paid advertising
- Email engagement
- Content marketing
- Search engine marketing (SEM) and search engine optimization
- Lead generation
- Online sales
- Offline sales
- Specific campaign performance
- Social media
Social media analytics
Digital marketing analytics includes social media, but it is an important enough channel to focus on by itself as well. Social media platforms often have their own forms of analytics built-in, which you will need to combine with your own data. Social media analytics may include user interactions with your promotional pages and profile, as well as paid advertising on social channels.
Looking at your own marketing alone doesn’t always give you the full picture—this is where competitor analysis comes in. By analyzing where your competitors’ traffic and customers are coming from, you can gain information about what your company might want to incorporate into its own marketing efforts. It can also reveal where your competitors’ strategies might be positively or negatively affecting your own performance.
The hurdles of analyzing data
Analyzing marketing data can be incredibly challenging. Customer behavior can be unexpected, data can overwhelm you, and not all the tools available will help you achieve the results you need. These are the biggest challenges marketers face when analyzing data.
Getting high-quality data
Not all data is good data. If you are gathering data incorrectly or incompletely, you will draw the wrong conclusions from it and make the wrong business decisions. It can be a challenge to ensure your data is high quality when you are pulling it from multiple channels. You need to put systems in place to make sure you are collecting consistent and complete data that is segmented in useful ways.
Dealing with large quantities of data
Depending on the size of your business and the number of channels it is operating on, you may need to manage large quantities of data. At this volume, it is absolutely critical to have quality software that can process and collate all that data quickly and effectively. While an individual might draw conclusions from small data sets, large volumes of data need analytic software that can categorize and visualize them to make them easier to understand.
Integrating data from multiple sources
When you are collecting data from many channels, you will need to integrate that data into one place. This will allow you to compare channels and draw overarching conclusions about your audience and campaigns. It can be a challenge to integrate data if you are collecting it in inconsistent ways or don’t have software that can effectively incorporate data from all your tools and sources.
Knowing what to do with your data
Another challenge of marketing analytics is drawing useful conclusions from your analytics once you have them and figuring out what actions to take. You aren’t just measuring for measuring’s sake. A team that has great data won’t succeed unless they can actively improve their marketing approach using that data. To do this, you need analytics software that supports your team in pulling useful insights from data. You also need a team that is trained enough to make strategic decisions based on your findings.
Features of marketing analytics software
When selecting the right tools for your marketing analytics, you want to look for features that will help you effectively use your data. Look for the following features in a potential tool:
- Data collection: Your marketing analytics tools need to gather data for you — either directly from your site or campaigns or by making it easy to integrate with or upload data from other sources. This includes having the technical capability to import, process, and manage large quantities of data quickly.
- Data unification: You will have data coming in from many sources. Some platforms, such as social media, may have their own data collection that you will need to export and combine with your other information. Your marketing analytics software should help you unify all of that disparate data, so you can draw overarching trends from it.
- Integration: With digital channels so varied across social media, websites, apps, and mobile devices, software with effective integrations will help you gather broader and more useful marketing analytics data. Using an integration between Adjust and Amplitude, for example, will help you use attribution modeling for your analytics. Your software shouldn’t trap you in a closed ecosystem; it should interface with all the tools your business uses.
- Dashboards: Your dashboard is where you translate raw data, so you can identify trends and draw conclusions from them. It should have an interface that is easy to work with. You should be able to customize it to get the specific insights you need for your business, campaign, or current focus.
- Reporting: You will need to create reports to summarize findings for your own team and to present to others in your organization. Your software should make reports that allow you to communicate results to leadership and findings to the relevant teams at your org. Different businesses have different needs and focus areas, so you want software that allows you to tailor your reports to your needs instead of having rigid formats.
- Data visualization and charts: Software should also support your creation of clear data visualizations in different formats. Data visualizations and charts make it easier for you to understand what the data is showing and communicate your findings. Software should also allow you to download or export those charts for use elsewhere.
- Customer segmentation: Software will allow you to segment your customer base in order to draw conclusions about various charts, channels, and demographics.
- Real-time insight: Analytics software should allow you to analyze customer behavior in real time, not just process past data.
- Predictive analytics and optimization: If you are planning future marketing campaigns, your software may help you run predictive analyses and optimize your approach.
Steps to analyzing marketing data
You need to have clear goals in mind and organize your data well in order to get useful results from your marketing analytics process. While software can do a lot of the legwork, it is the way your team approaches your data and draws conclusions from it that will make your marketing analytics successful.
1. Set goals for your marketing analytics
Establish clear goals when you begin the marketing analytics process. What information do you need to get about your customers or marketing campaigns? What insight do you need to guide future campaigns? Your goals might start with a problem that needs to be solved or a question about your audience or marketing campaign that needs to be answered.
Questions should be able to be answered with the specific data you have gathered. Goals need to be measurable in order to track success. Focus your goals on the elements of your marketing campaigns that your team has control over and can influence.
2. Choose which metrics to track
While your data may be extensive, your analytics needs to focus on specific metrics to draw useful conclusions from your data. Choose metrics that you will track consistently over the lifetime of your campaign. You should select metrics that are tied to a specific business goal.
Some important metrics might be:
- Events: Actions taken by users that are important to the business. This could be anything from clicks to adding an item to their shopping cart and viewing time on certain assets.
- Lead generation: A lead is generated when a customer enters their contact or personal information, demonstrating interest in a product that might turn into a sale.
- Conversion: When a customer completes your desired goal. This may be a product purchase, account sign-up, or subscription.
3. Set up audience segmentation
You want to know what your customers are doing but also who is doing what. Set up segments that make sense for your business and give you information about the type of customers you want to target. You can segment your audience by demographics or by behavioral cohorts.
Demographics might include:
- Age group
- Geographical location
- Education level
Behavioral segments might include:
- Time: Time of day or day of the week the user is active. For example, weekdays during working hours or weekend evenings might have very different demographics, needs, or behaviors.
- Activity: Specific activity the user is taking part in. For example, is the visitor reading your blog or online shopping?
- Customer loyalty: Is a visitor a long-time loyal customer or a curious newcomer to the brand? A campaign targeting frequent customers will look very different from one targeting brand new ones. It will help to have data on that specific group.
4. Establish a baseline to measure against and target numbers
Establish your current baseline numbers in order to track the performance of your campaigns. Set targets for yourself, so you can measure the success of your efforts. Segment your goals, establishing minimum, target, and stretch goal numbers. That way, you not only pass/fail but can also work toward continuous improvement.
5. Collect data and run tests
Once you have structures set up, you can gather data. This might look like downloading data from a second party, tracking data passively on your website, or running specific tests to generate information.
Useful tests might include:
- A/B testing: Track multiple variations of a page, feature, or message. It tells you more about what your users prefer and what approaches are more effective. Is your newsletter sign-up more effective at the top or bottom of the page? Which type of email is more effective at getting recipients to click through to a page? Which call-to-action message results in more conversions?
- Split testing: Less granular than A/B testing, split testing splits your audience into cohorts to test entirely distinct designs.
- Visual behavior data: Track the movements of the user’s mouse around the screen and create a heat map that gives you information on what the user is interested in and their behavior on your site. It can tell you where users click and whether they actually scroll down a page. Once you know what areas users are more likely to pay attention to, you can put the features you want more focus on there. This testing is good for improving interfaces and conversion.
6. Decide what model to use to analyze your data
Choose how you will model your marketing data analytics in order to draw useful conclusions from your data. Different models will use different data and give you different insights into customer behavior. There are three main types of modeling.
This approach helps you understand which online touchpoints your marketing traffic can be attributed to. It helps your marketing team understand which channels are seeing the most success in driving traffic and sales. It is an increasingly common way to approach marketing analytics as more and more digital touchpoints appear.
In attribution modeling, you set up rules within your marketing analytics software, such as “last touch” rules, that determine which channel gets the credit for a conversion. It uses various techniques, such as cookie data or statistical modeling, to figure out where that traffic is coming from.
Reach, cost, quality (RCQ) modeling
RCQ modeling breaks touchpoints down into the type of engagement rather than assigning full attribution to one channel. It is useful when you have limited or incomplete data. It gives all touchpoints similar units of measurement, so you can compare them to each other more easily.
Marketing-mix modeling (MMM)
The most advanced approach, marketing-mix modeling links analytics data directly to spending by channel. This is a great way to figure out if your marketing investments are paying off in sales. It can be harder to do because it requires large quantities of high-quality data to get effective results.
7. Implement strategies based on your findings
Finally, you will take action based on the insights gained from your marketing analytics. This could be tweaking an existing campaign to improve a specific metric, or it could be big-picture changes in the direction of your marketing campaigns long term. The actions you might take could be as small as targeting a specific keyword or as large as replicating the structure of a previously successful campaign.