What is marketing analytics?

Robust marketing analytics unlocks a better understanding of a business’s target market and customer base. Leverage marketing analytics to produce more targeted campaigns and improve the ROI of marketing spend across channels.

Marketing analytics enables marketers to gather, measure, and analyze the performance of various campaigns and marketing initiatives. Gathering and analyzing this data shows trends in consumer behavior, insights about competitors, and other data to solidify marketing efforts or pivot campaigns to improve consumer engagement.

This analysis and evaluation of data empower marketers to implement changes to drive sales upward while producing an improved return on investment. Data can be gathered across various marketing channels. There are many channels including social media, email marketing, and websites as well as more traditional channels such as commercials, billboards, and print advertisements. Gathering data from only one channel will not be sufficient. That’s why marketing analytics is important, allowing data to be analyzed across multiple channels which then, in turn, shows important information about consumer behavior.

Although marketing channels and campaigns vary from company to company, there is an analytics life cycle that many marketers loosely follow. The marketing analytics lifecycle shows how data is generated, collected, processed, used, and analyzed.

How do I use marketing analytics?

Although there is a general life cycle for marketing analytics, there is no universal process for conducting analysis. Companies should choose what is best for their markets as information can be gathered in various ways. Using attribution models helps to gather data based on what the marketers want to research.

Analytics attribution modeling is a type of strategy that helps marketers analyze touchpoints along the customer journey, from the first interaction with a product to buying the product and all the steps in between. There are multiple analytics attribution models to choose from and most models can be classified as a single touch or multi-touch model.

A common touchpoint that single touch models track is the first touch or the first engagement with a potential customer. This model shows the first interaction a person has with a brand before purchase. This allows marketers to know which of their campaigns generate the most clicks and conversions. The qualified lead model or the last touch attribution indicates what a person last interacts with before making a purchase.

Last touch and first touch attribution are some of the most commonly used single touchpoint attribution models; however, there are other models that may be more beneficial to help analyze data.

In addition to single-touch attribution models, there are also multi-touch models. Linear attribution is one of the multi-touch models that equally tracks all the touchpoints in a consumer's journey. So if there are four touch points tracked each touchpoint would get 25% of the overall credit. Similar to linear attribution is time decay attribution. This model gives credit to multiple touchpoints but the most recent touchpoints get a larger percentage while the least recent interactions get less.

There are many different single and multi-touch attribution models that can be used for analyzing data. Overall, each attribution model helps to organize, interpret, and draw conclusions about consumer behavior and marketing initiatives.

Why is marketing analytics important?

Although there is a general life cycle for marketing analytics, there is no universal process for conducting analysis. Companies should choose what is best for their markets as information can be gathered in various ways. Using attribution models helps to gather data based on what the marketers want to research.

Analytics attribution modeling is a type of strategy that helps marketers analyze touchpoints along the customer journey, from the first interaction with a product to buying the product and all the steps in between. There are multiple analytics attribution models to choose from and most models can be classified as a single touch or multi-touch model.

A common touchpoint that single touch models track is the first touch or the first engagement with a potential customer. This model shows the first interaction a person has with a brand before purchase. This allows marketers to know which of their campaigns generate the most clicks and conversions. The qualified lead model or the last touch attribution indicates what a person last interacts with before making a purchase.

Last touch and first touch attribution are some of the most commonly used single touchpoint attribution models; however, there are other models that may be more beneficial to help analyze data.

In addition to single-touch attribution models, there are also multi-touch models. Linear attribution is one of the multi-touch models that equally tracks all the touchpoints in a consumer's journey. So if there are four touch points tracked each touchpoint would get 25% of the overall credit. Similar to linear attribution is time decay attribution. This model gives credit to multiple touchpoints but the most recent touchpoints get a larger percentage while the least recent interactions get less.

There are many different single and multi-touch attribution models that can be used for analyzing data. Overall, each attribution model helps to organize, interpret, and draw conclusions about consumer behavior and marketing initiatives.

An example of marketing analytics in action.

Website analytics is a key marketing channel that needs to be monitored, optimized, and leveraged properly. Web analytics providers offer a variety of features to gather important information, making it simple to analyze and implement change.

Language and location are some of the most important metrics for global businesses to track. Location can help marketers to see where most of the consumers are from, enabling them to alter websites to accommodate various languages and payment processes. Changing websites to be in different languages can also influence the web design, as the translation of words may alter the length, or format content differently, which may affect the overall design and flow of the website.

Website analytics tools also allow for tracking of demographics such as the average age of people visiting the website. Demographic analytics enables marketers to see differences in various visitors, as well as how frequently people visit the site. Age and other demographic indicators help marketers better understand their target market and therefore create more curated advertisements for potential customers.

Alongside demographics, tracking the use of different browsers and devices can be informative. Knowing what browsers and devices consumers are using allows testing to be done on the most commonly used browsers to optimize the digital experience on that particular medium. Tracking the type of devices like a phone, tablet, or laptop can reveal the level of income of a user, which influences the design of the website, and again, helps provide information to generate targeted advertisements.

Web analytics tools have many other features including benchmarking, comparing your website with other websites like it, frequency and recency, how frequently a user scrolls through the website, and how often that is, as well as the time of day, the specific time a person visits the website. These specific metrics, amongst others, enable marketers to find pain points for buyers, allow for a better understanding of consumer behavior, and reinforce marketing initiatives.

Like all marketing analytics platforms, website analytics has its limitations. You simply cannot track everything that is happening on a website at one time for various reasons, so data should be taken as an approximation. It’s important to remember that people have the option of blocking cookies or turning cookies off so information is under-reported and leads to missing information.

Another limitation is that it’s difficult to differentiate between robots and humans interacting with the website. So as blocked cookies lead to under-reports, robot interactions lead to over-reporting data.

Platforms like Google Analytics and Adobe Analytics are two of the many marketing analytics platforms that track and record website behavior. However, in order to perform an omnichannel analysis of your marketing data, it’s important to invest in a platform that can consolidate all of your data in a single environment.

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Combining marketing data across channels.

Gathering data is key to successfully developing effective marketing campaigns, but the volume of data and data sources gathered across channels quickly becomes overwhelming.

It is important to know how to connect, transform, and analyze marketing data from all channels in order to draw the best conclusions. Without proper tools, data volume can become one of the biggest challenges for marketing professionals.

Data scientists are experts in the field of analytics and are key to helping companies interpret data, however, not every company may have access to these experts. User-friendly business intelligence solutions make data consumption and analysis approachable to people of all skill sets and backgrounds.

Along with handling data volume, a good business intelligence tool also provides functionality to improve data quality. If marketers do not take the time and effort to implement a process to maintain data quality, time and money will be wasted and the best results will not be delivered.

Performing marketing analysis within a reporting solution brings improved results and functionality when gathering and analyzing data to improve marketing efforts.

However, marketers need to be conscious of the importance of using the right models, the right people, and the right tools to effectively analyze all the marketing data they’ve gathered.

Driving value with marketing analytics.

With the growth of digital platforms comes the growth of digital marketing. Now, more than ever marketing analytics can provide detailed data to improve marketing efforts and marketing campaigns. Properly gathered data will help marketers to understand consumer behavior, drive sales, increase return on investment, and solidify marketing efforts.

Marketing analytics takes time and resources to provide accurate data but the improved return on marketing investment is one of the greatest potential benefits from investing in better marketing analysis. Analytics generates credibility, improves efficiency, saves time and money, and it results in faster revenue growth for the company.

Overall, regardless of the size of a business, marketing analytics can provide invaluable data that will drive growth along with increased market share.