Marketing Analytics 101

Marketing metrics and analytics provide a strategic road map to improve efficiency, predict returns, and prove marketing return on investment (ROI) across the entire organization. 

What is Marketing Analytics?

The more technology develops, the more time and budget CMOs are allocating to understanding the performance and growth influence of their marketing efforts. In fact, a recent survey predicted spending in the area will increase by 200% in the next three years. Marketing teams can often struggle to demonstrate credibility, but the adoption of strategic marketing analytics can make it easier to show your marketing endeavors’ ROI. 

Marketing analytics is the practice of managing and studying metrics data in order to determine the ROI of marketing efforts like calls-to-action (CTAs), blog posts, channel performance, and thought leadership pieces, and to identify opportunities for improvement. By tracking and reporting on business performance data, diagnostic metrics, and leading indicator metrics, marketers will be able to provide answers to the analytics questions that are most vital to their stakeholders. 

Regardless of business size, marketing analytics can provide invaluable data that can help drive growth. Enterprise marketers at first may find the process too complicated, while small and mid-sized business (SMB) marketers assume a company of their size won’t benefit from implementing metrics, but neither perception is true. As long as marketing analytics is carefully curated and properly implemented, the data collected can help a business of any size grow.

With proper marketing metrics and analytics in place, marketers can better understand big-picture marketing trends, determine which programs worked and why, monitor trends over time, thoroughly understand the ROI of each program, and forecast future results. With 78% of B2B marketing executives currently measuring the impact of their marketing programs on revenue, it’s clear that more businesses are getting on board with marketing analytics, even if they were a bit hesitant before.

“Too often marketers talk about activities instead of outcomes—for example, how many campaigns they ran, how many trade shows they participated in, how many new names they added to the lead database. These are metrics that reinforce the perception that marketing is a cost center, not a revenue driver.”

— Glen Gow, Founder and CEO, Crimson Marketing


Common problems that marketing analytics can solve 

Determining the effectiveness of your marketing efforts can be more complicated than tracking simple engagement, and compiling that data doesn’t necessarily result in optimization of your future campaigns. Below are a few problems that a marketing analytics solution can help solve. 

  • Problem: I feel like I’m reporting for reporting’s sake. As a default, marketers often put primary metrics such as lead source tracking and cost-per-lead in place, but there is no holistic understanding of how marketing activities impact key bottom-line metrics. Make sure to set up analytics to support the goals that your stakeholders most care about, so you aren’t compiling data without a plan in place.

  • Problem: I don’t know how to unify my data. Get your data out of silos and spreadsheets. When you automatically connect and unify your data, you can spend more time acting on insights you’ve worked hard to collect and less time on tedious reporting tasks. 

  • Problem: I don’t know how to show the impact on revenue and profit. Many marketers think of marketing ROI as reporting on the outcome of their programs—often in the form of a set of reports they have to deliver monthly. However, the most successful companies recognize that reporting for reporting’s sake is less important than using those reports to make decisions that boost sales.

  • Problem: My metrics don’t tie to actions that drive outcomes. Don’t just report on a single campaign. The incremental contribution of individual marketing programs and the ability to show how your marketing campaigns influence sales at every stage of the customer journey will help build credibility and show long-term program success.  

  • Problem: I struggle with forecasting. When marketing takes responsibility for the early stages of the revenue cycle and understands how to model these stages, they have better visibility into future revenue. Marketing analytics allows the ability to forecast how many new leads, opportunities, and customers marketing will yield in future periods because it tracks where prospects are in each revenue cycle stage—and how likely they are to move through each stage over time. 


Components of marketing analytics 

Marketing analytics, a multifaceted practice used to drive ROI and improve future efforts, consists of:

  • Centralized marketing database: Analytics require access to highly detailed marketing data, so marketers need to begin tracking this information now—preferably in one place. Required information will include historical data around when marketing programs ran, what their attributes were, who they touched, how much they cost, and so on. Without this information, analytics are essentially worthless.  

  • Time series analytics: Unless an operational system stores historical data, a marketer cannot measure or understand marketing trends. Many marketing and sales solutions are operational and do not store historical information, requiring marketers who want to analyze their metrics for prior time periods to manually take data snapshots from their Excel spreadsheets. However, time series analytics gives marketers a full picture of their performance trends over time because the engine can analyze beyond point-in-time insight.

  • Advanced attribution capacity: With marketing data in one place, marketers can understand what moves the needle and maximize return on marketing investment, but only if they have technology that facilitates attribution reporting. Many solutions only offer basic attribution capacity— most commonly, first or last touch attribution or multi-touch attribution limited by the type of channel or time horizon. This limits your ability to grow into the most illuminating attribution models down the line. Be sure to understand the attribution limitations of any technology you’re considering before you buy. 

  • Powerful and easy insights: Very few of the marketers who want and need to consume analytics data are business analysts. For such an audience, user-friendly dashboards are required, so marketers can explore data trends and gain insight into their programs without wasting time acquiring the expertise needed to maneuver the technology, build custom reports, and so forth. Just make sure your marketing automation solution offers tools that are both and powerful and simple to use!  

  • Ad-hoc reporting and dashboards: On the other hand, marketing analytics experts will need the ability to delve deeply into the data and customize their ad-hoc reports. In this case, table-like reports and charts are most effective and allow analysts to “follow the scent” of particular insights as far as they need to go.  




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5 methods to analyze your marketing program

Having a strategic marketing analytics program means knowing which data points to monitor and which detract from your ultimate goal of demonstrating ROI. While this will take more work up front, it will—in the long run—make analysis a much simpler process.  

Method 1: Single attribution (first touch/last touch) - Single attribution is one of the most common marketing analytics strategies. This strategy allocates all of the value to either the first or last interaction with the prospect before buying. First-touch attribution credits the lead generation strategy with the eventual sale regardless of when the sale happens. For instance, if an SEO-optimized landing page draws in a new lead from a web search who later consumes branded content, engages on social media, then attends a trade show before becoming a customer, first touch attribution assigns the value of that sale to the SEO campaign. Last-touch attribution credits the final communication with the close of new business. In the example above, the trade show would be credited with the sale since it was the last interaction the lead had before purchasing. 

Method 2: Single attribution with revenue cycle projections - Single-attribution strategies are simple, but that simplicity can lead to disadvantages. Brands with longer buying cycles need to account for that period of time as well as all of the lead nurturing that happens in between in order to create an accurate picture of the quality of current marketing efforts. Adding revenue cycle projections to a first-touch/single-attribution analytics strategy can solve this problem. Revenue cycle projects use complete data from previous campaigns to project the eventual outcome of recent and similar marketing efforts. 

Method 3: Attribution across multiple programs and people - Attribution across multiple programs and people views credit more holistically. You recognize that no single marketing effort is responsible for a sale, and you try to determine the value of each touch by starting with the action that created a sale and working backwards. Once every touch has been identified, you then determine how to weigh each one so that their values can be properly assessed. Some assumptions are necessary for this method, and that's okay. Just make sure you are prepared to defend them to the C-suite, or you may risk invalidating the whole process. 

Method 4: Test and control groups - Test and control groups are a great way to measure the actual—rather than the projected or assumed—impact of a marketing campaign on your target audience. And in theory, it’s as easy as your middle school science fair experiment. Using test and control groups requires a little extra strategy from the start, since you have to plan a program you can test. The goal is to apply the factor you want to measure to one part of your target market. So, make sure you divide your audience into two groups that match up on other basic metrics. 

Method 5: Full marketing mix modeling (MMM) - Marketing mix modeling demonstrates how each unique marketing touch, as well as non-marketing variables, impacts sales volume. Statistical techniques create complex equations that can take into account an infinite number of factors, including advertising, distribution, economic conditions, pricing, and product. To be effective, this model requires a lot of data. So much so that most marketers find that MMM consumes too much time and energy. This explains why only 3% of B2B marketers use it.


ROI of a successful marketing analytics program

The ROI of a successful marketing analytics program is one of its greatest assets, and it can impact your company on a universal level.

  • Marketing analytics builds credibility. Marketers can earn the respect of their organizations by taking a professional approach to marketing metrics and analytics by using integrated technology to provide better insights for informing better business decisions. Marketers who invest in measuring and managing performance create more value, achieving 5% better returns on marketing investments and over 7% higher levels of growth performance. 

  • Marketing analytics save time and money, and improve efficiency. By providing a single platform for reporting across all channels, the entire process is simplified. Additionally, we found that across industries and regions, an integrated analytics approach can free up 15 to 20% of marketing spending.

  • Marketing analytics can result in faster revenue growth. Campaigns can be iterated to improve the bottom line when marketing metrics are readily available, resulting in more accurate forecasting and quicker revenue growth.

Learn more about how to speak the language of marketing metrics

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Planning, implementing, and optimizing your marketing analytics program

The first step to beginning your marketing analytics journey is setting goals and committing to the process. Get the ball rolling in just four steps. 

  • Step 1: Plan. Before you hit the ground running, you’ll need to set goals and targets so you aren’t reporting for reporting’s sake. Estimate your expected ROI, then start to develop programs that are designed to be measurable so you aren’t wrestling to pull data. Ensure you focus on any and all decisions that will ultimately result in an improvement in your marketing efforts. 

  • Step 2: Implement. As with any business transformation, the success of your marketing measurement program depends on how well you implement it. Set yourself up for success by making sure you find the right team, use the right tactics, and have the right technology in place.

  • Step 3: Create a culture of analytics. Hiring (or designating) the right people is only the first step. Even at companies that already have significant analytical activities underway, completing the analysis is only about a third of the battle. The other two-thirds involve driving that data into all current business workflows in a way that prompts your organization to use and act on your valuable conclusions to boost revenue. 

  • Step 4: Optimize. When you are able to spread the revenue over multiple activities, you know which marketing activities are effective for the top-of-the-funnel versus the bottom-of- the-funnel campaigns. You may determine that trade shows are great for getting leads, whereas webinars are more effective for moving leads further along the sales funnel. Without multi-touch attribution, it’s hard to learn this information, and your campaigns may not be as successful.