Understanding marketing analytics begins with understanding their definition. The following will discuss the different types of marketing analytics, where they fit into reporting, and how you can leverage analytics into actionable insights.
The term "marketing analytics" concerns the strategies of compiling, analyzing, and using marketing data to make better decisions.
For example, a business may want to understand how well a page on their website is performing in terms of organic search ranking, traffic, and conversion rate. The company's marketing team can leverage a marketing analytics tool to gain insight into the page's performance. They can then make tactical adjustments as needed (e.g., different keywords, better formatting, etc.) to optimize its effectiveness.
There are three basic categories of marketing analytics: descriptive, predictive, and prescriptive.
The majority of analytics tools used by marketers today are descriptive in nature. However, there are some "hybrid models" on the market.
By itself, the raw data produced by marketing analytics is hard to convey in an easily understandable way. Rather, data needs to be carefully analyzed and eventually turned into insights in order to have tangible business value. Thus, analytics data should be included in reporting only as far as it is practically useful to the company.
The key to making data analytics relevant is to put them within a greater context — especially regarding key performance indicators (KPIs) such as return on investment (ROI) or sales.
For instance, determining that a landing page has a conversion rate of 5% is meaningless without looking at the "big picture." Is 5% good or bad? What are the industry benchmarks? What factors are driving the successful conversions, and where are the bottlenecks? How does that 5% conversion rate impact marketing ROI?
Bounce rate provides another example of how important context truly is. Bounce rate is typically defined as a session that triggers only a single request to the server. In other words, a user reaches a page and then leaves without engaging any element on the page. By itself, a page's bounce rate doesn't give a lot of useful information. For example, a high bounce rate on the company's home page is usually a bad sign, since prospects aren't visiting other pages on the site from the home page. In contrast, a high bounce rate on the company's "Contact Us" page may not indicate any trouble, especially if the page only contains an email address and phone number.
With the context in mind, it's easier to obtain actionable insights from the raw data. A high bounce rate on the home page usually indicates a bottleneck in the customer's journey. Thus, the home page needs to be altered in order to facilitate the consumer's journey through the sales funnel.
Context-driven analytics also helps marketers decide where to prioritize resources. For example, when examining the ratio of site traffic to conversions, the marketing team shouldn't just look at the volume of traffic but should also consider which channels drive the highest quality traffic. For instance, an ad may drive a lot of traffic to a specific landing page. However, if the conversion rate is low, then that may indicate the ad is somehow misleading consumers and needs to be changed in some way.
Marketing analytics must be viewed in light of larger goals. Analytics data only transforms into actionable insight when it's put in the context of those goals. By using marketing analytics in this highly targeted and practical way, you'll know how to increase your ROI and grow your business.