05/20/2022 | Press release | Distributed by Public on 05/20/2022 16:39
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.
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.
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.
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.
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.
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.
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.
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.
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:
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:
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.
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.
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.
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.
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.
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.
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:
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.
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.
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:
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:
Behavioral segments might include:
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.
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:
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.
Attribution 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.
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.