An Extensive Matrix Of Marketing Funnel Metrics
B2B marketers spend a lot of time with metrics. Between our longer sales cycles and data-packed martech stacks, there are plenty of numbers, figures, and facts to go around. The availability of these marketing metrics begs the question — how do we use them properly? Which funnel stage do they measure? Are there some types of metrics that are more useful than others? Which piece of martech can generate which metrics?
“The availibility of these marketing metrics begs the question — how do we use them properly?”
These questions are answered in the following B2B marketing matrix of funnel metrics. The infographic shows the funnel stage, the type of metric, and the source of the metric -- for 31 key B2B marketing metrics. There are, of course, more metrics than those contained in this list -- but this selection contains some of the most popular and/or most helpful.
Prospect behavior (like bounce rates, impressions, average time on site, and web traffic) are measured by top-of-funnel metrics. When optimizing web pages or surveying the overall, surface-level health of a website, these metrics can be very helpful. However, when it comes to making mid-funnel decisions, these metrics have very little bearing on lead-stage decisions or conversion rate optimizations. And, it’s next to impossible to gauge bottom-line business impact using top-of-funnel metrics.
Middle-of-the-funnel metrics dive a little deeper into the data. Here you find open rates, lead volumes, lead scores, conversion rates, and click-throughs. These metrics also show prospect behavior, but it’s generally tied to a more important user action (filling out a form as opposed to merely viewing a webpage, for instance). Hence, these metrics help marketers optimize lead-stage strategies and gauge the performance of their efforts to move prospects through the funnel.
But do keep in mind that these metrics can outlive their usefulness, because they still provide limited information. Mid-funnel metrics can answer questions like, “How many leads did this channel generate?” However, they can’t answer questions like, “How much revenue did this channel generate over the last 6 months?” or, “What was the opportunity conversion rate of this ad campaign?” Those are questions that only bottom-of-the-funnel metrics can answer...
Use bottom-of-the-funnel metrics to measure marketing performance at the opportunity and customer stages. Funnel metrics near the end of the buying journey are arguably the most useful for two reasons. First, they’re able to show revenue impact. Second, marketers can use these metrics to make decisions throughout the entire funnel.
By examining the down-funnel conversion rates of leads generated in earlier funnel stages, marketing activities can be better optimized. If you know the opportunity-conversion rates of leads generated by a given channel or campaign, you can make decisions about how to reuse or improve those activities in the future. You can easily compare campaigns against each other based on their respective down-funnel results (opportunities, customers, and revenue).
Volume-based metrics show magnitude, but they often lack context and perspective. They don’t show a relationship of any kind, they simply generate a number. The number can be compared to past and future volume-based trends, but it’s difficult to derive a correlative conclusion based on volume metrics. Therefore, they’re useful when observing general trends, but they’re not as helpful in the strategic decision-making process.
“Cost-per-[something]” metrics make up the majority of a B2B marketer's cost-based data figures. Most of these metrics compare marketing budget spend with conversion results from specific activities, campaigns, or channels.
“Rates” metrics are the most popular percentage-based metrics (e.g. open rate, click-through rate, conversion rate, win rate, etc), and they’re often used to compare two volume metrics against each other. For example, “lead conversion rate” on a web page compares traffic volume to form fills and generates a percentage result.
Percentage-based metrics are often the most popular type of marketing metric, because they can be pivoted in multiple different ways. But it is important to note that percentage-based metrics that measure lower-funnel activities usually carry the most insights for decision-making.
Algorithmic metrics are used to compare a set of weighted variables. In most cases, algorithmic metrics result in a type of score. Depending on the value of the score, it will prompt an action to be taken (or not taken). For instance, a lead score shows whether or not a lead has met certain behavioral criteria to warrant entering an email nurturing sequence or a sales outreach workflow.
Because these metrics are built to prompt an action, they’re often exceptionally useful in decision-making processes. Rather than making an educated guess at whether certain leads are ready for further nurturing or engagement, the metric will prompt the action without a decision having to be made on an independent basis.
This type of metric belongs to the family of “adaptive analytics,” which is an approach to marketing data that leverages real time information and martech capabilities to provide the most accurate marketing intel at any given moment.
Revenue-focused metrics are metrics that directly impact revenue generation. These metrics are almost exclusively bottom-of-funnel metrics, as those metrics are the closest to customer conversions. In addition, metrics that compare bottom-of-funnel results to higher-funnel activities are also considered revenue-focused metrics -- reason being, they’re used to make decisions based on revenue-level information.
Decision-making metrics have the ability to imply specific, measurable, strategy changes. For example, most cost-based metrics are also considered decision-making metrics because they directly apply to marketing spend. TOFU metrics aren’t usually decision-making metrics because they provide less contextual and tangible information from which to infer strategy changes.
Down-funnel metrics that shed light on opportunity-stage data and customer conversions are arguably the best decision-making metrics available to marketers. These metrics provide insights based on the ultimate goal of a marketing strategy -- revenue.
Metrics Derived by Web Analytics
Web analytics platforms measure top-of-funnel user behaviors on a specific domain. These figures can show overall growth over time or volume-based trends, they only infer basic levels of information. Hence, while they can be generally useful, they shouldn’t be used to make sweeping strategy decisions.
Metrics Derived by Social Media Networks
Social media networks can provide a few metrics surrounding social engagement and social reach of your messaging. Similar to web analytics, they can provide “interesting” information, though little of it is very valuable.
Metrics Derived by Marketing Automation
Marketing automation can generate a number of important metrics, mostly in the middle-funnel stage. However, since leads are passed onto the sales team at one point or another (and data is passed into the CRM), marketing automation platforms often can’t generate the down-funnel, revenue-level metrics that B2B marketers need.
Metrics Derived by Marketing Attribution
An advanced, marketing attribution solution has the ability to derive highly insightful metrics that can be pivoted in multiple ways. The attribution solution directly connects to the company’s CRM, and as a result, it can connect upper-funnel activities (keyword clicks, ad campaigns, blog articles, webinars, emails, etc.) to lower-funnel results (opportunity conversions, customer-closes, and revenue generated).
This information is available because an attribution solution collects and compares net new granular data at the touchpoint level. MQLs, opps, pipeline, and/or revenue can be connected directly to specific keywords, ads, landing pages, campaigns, channels, and events.
These metrics have huge implications for decision-making paradigms as upper-funnel changes can be educated by down-funnel results. Marketers can easily identify wasted ad spend, and they can prove their impact on the business’ bottom line.