It’s the age of analytics, but B2B marketers are drowning in data. Our abundance of martech tools churns out data like a vending machine, but rows and columns of information aren’t helpful if a marketer doesn’t know how to use them. Unfortunately, recent reports indicate that the majority of marketers aren’t well versed in harnessing the power of data and using it to inform their marketing decisions. Three laws of marketing data from the esteemed genius, Sir Isaac Newton, might be able to help.
Do B2B marketers have too much data?
There does comes a point when more isn’t always better. Sometimes it’s just more. But like all other information, data is merely a tool. And the issue isn’t that we have too many tools. Instead, the question is, “How we should use the myriad of tools at our disposal?” Or, “Where can we find more of the type of data we need?” For data to be effective, it must be used effectively.
Gathering this information about your B2B marketing strategy is far better than remaining naive or ignoring the potential impact of marketing data. But data’s tough. According to a recently released report from B2BMarketing.com, only 25% of B2B marketers say that they’re confident in their ability to effectively use and leverage their marketing data. So, we’re drowning in data, and only ¼ of us of us know how to swim? Those aren’t good odds.
FACTS ABOUT B2B MARKETING DATA:
- 75% of marketers don’t know how to make the most of their data
- Marketing data is a powerful tool that shouldn’t be ignored
- Data is plentiful, but B2B marketers’ data literacy is primitive
So, what do we do? The following three laws could help B2B marketers gain a different perspective on how data functions and how it can be put to use. After that, we’ll examine different types of data and how to (and whether or not to) use it in B2B marketing decisions.
[First law] Marketing data tends toward disorder
Data by itself isn’t orderly. Usually, it’s a massive spreadsheet that has to be sorted, pivoted, consolidated, and compiled into a meaningful report. But before this (often tedious) organization, marketing data begins as thousands of descriptive data figures haphazardly floating in cyberspace. Whether we can use the data, process the data, and learn from the data is completely dependent on the type of data that it is.
Without practiced skills in analysis, marketing data tends to become a disorderly mess. As you begin to pick apart the metrics and information you have to find the nuggets you need, start by asking these questions.
QUESTIONS TO VET YOUR MARKETING DATA:
- Is it recent?
- Is it accurate?
- Is it comprehensive?
- Is it functionally helpful?
- Is it from a reliable source?
[Second law] The disorder of a data system approaches absolute zero as data proficiency increases
In layman’s terms, this law means that as a user becomes more proficient at gathering and reporting data, their data process becomes less disordered. To correctly handle data, the first step is to understand it — a skill marketers tend to call “data literacy.” Secondly, a B2B marketer should understand how metrics can be compared to each other. This is commonly known as “pivoting” the data against itself.
To properly understand your data, you have to ask several probing questions about it.
QUESTIONS TO ASK ABOUT MARKETING DATA:
- How can I use this data?
- What questions can it help us answer?
- Does it have the scope we need?
- Is my sample size large enough?
QUESTIONS TO ASK WHEN PIVOTING MARKETING DATA:
- How does one metric relate to another?
- How does time affect this metric?
- Which variable should be primary and which is secondary?
- Which metrics would answer my research question?
AN EXAMPLE OF UNHELPFUL VS. HELPFUL MARKETING DATA
In the example below, we attempted to measure pipeline velocity of first-touch prospects converting to leads. While the data was recent, it wasn’t functionally helpful. First, we had changed our data-processing methods halfways through the year, so the data we pulled across the entire year-span didn’t correlate. Second, without additional information about each touchpoint dot, we’re left with zero ability to optimize our pipeline. Basically, the data was inaccurate and unactionable.
The bar chart below succeeds where the scatterplot does not. It’s drawn from a comprehensive data set, it’s quantifiable, and it’s actionable. In this case, we attempted to discover how our organic search marketing channel was performing over the course of the year based on leads, opps, and revenue driven. The steady uptrend was encouraging. While the chart below is a simplified version, the original chart showed specific numbers of leads, opps, and revenue driven each month, as well as a total of each of those metrics for the year.
Data might tend toward disorder, but the issue can be reversed. Pulling, processing, organizing, and presenting data is one of the most sophisticated tasks a marketer can undertake.
[Third Law] For every assumption there is an equal and opposite analysis
Data-driven marketers try their best to operate with the mindset that an outrageous assumption ought to be tested by an equally outrageous data analysis. Essentially, assumptions ought not to be permitted in marketing if there is retrospective (or better yet, predictive) data that can be examined to prove or disprove our assumptions.
This is one of the main functions of attribution data. Rather than sitting back and assuming that “Marketing generates leads through organic search,” we test that assumption to find the precise number of leads organic search is driving. And, attribution doesn’t stop there. Marketers make assumptions about all kinds of other metrics and marketing strategies. Here are some common examples of marketing’s assumptions:
COMMON B2B MARKETING ASSUMPTIONS:
- “Organic search drives a comparatively high percentage of our leads.”
- “This media campaign’s CTR is high, so it’s likely driving revenue.”
- “Events are effective because our post-event email open rates are high.”
- “We should invest in the channels that drive the most website traffic.”
- “Bid higher on this keyword, it’s getting lots of clicks.”
Each one of these assumptions can be tested with attribution data. B2B marketers can follow leads through their pipeline and see whether they tend to convert. Anonymous first-touch data can indicate whether those initial clicks are worth their weight in revenue.
QUESTIONS TO ASK TO ANALYZE MARKETING DATA:
- Are these metrics functionally comparable?
- Are any variables out of place?
- Did we process the data consistently?
- Does this data answer our research question?
Best Practices for Handling Data From B2B Martech Sources
B2B marketing data originates from a plethora of sources, and all of them are likely present within your martech stack. Web analytics, marketing automation, the CRM, and B2B attribution are exceptional data sources, each in their own way.
DATA & METRICS FROM WEB ANALYTICS
Website analytics are helpful for keeping your finger on the pulse of your TOFU marketing activities. However, part of the job of a data-driven marketer is to know the limits of their data. While web analytics splay an array of information across your screen, a data-literate marketer is able to smartly sort through that data. Web analytics are often called “vanity metrics,” because they’re so top-of-funnel that they can’t inform lower-level marketing efforts.
WEB ANALYTICS METRICS:
- Unique visitors
- Keyword rankings
- Bounce / exit rates
- Geographic locations of users
- Website traffic referral sources
DATA & METRICS FROM MARKETING AUTOMATION
As an effective integration and performance tool, the data offered by marketing automation tools can be helpful for analysis around lead-stages and, more broadly, concerning middle-of-the-funnel activities.
MARKETING AUTOMATION METRICS:
- Click-through rates on CTAs
- Lead-conversion of landing pages
- Open rates and clicks for email
- Lead-stage conversion rate for emails
Notice what these metrics don’t tell you — you don’t know whether those leads came from before they hit your landing page. What happened to the SQL that converted on your email? Despite some limitations, when it comes to mid-funnel insights of your marketing touches, marketing automation is an good data source for small-scale, lead-focused optimizations.
DATA & METRICS FROM B2B MARKETING ATTRIBUTION
Marketing attribution is one of the most actionable types of marketing data available in the martech array. Attribution does granular tracking of marketing touchpoints throughout the entire funnel. While you can’t create CTAs or send emails with attribution (like you can with automation), it arms a marketer with information that extends beyond the limitations of automation data.
From the moment a visitor lands on a website to the instant they close as a customer, marketing attribution can track every touchpoint that the buyer took along their journey through the funnel. This allows a data-driven B2B marketer the ability to pivot multiple types of data in number of ways in order to answer a wide range of questions.
MARKETING ATTRIBUTION METRICS:
- Revenue by lead source
- Opportunities by ad campaign
- Leads by keyword
- Lead conversion of organic search compared to paid social
- Opportunity conversion of one ad campaign compared to another
- Revenue credit attributed to the impact of an email campaign
DATA & METRICS FROM THE CRM
The CRM provides outreach metrics and BOFU conversion data. This can be particularly helpful, especially when coupled with attribution data. In addition to seeing how your opportunity and customer conversion efforts are performing, you’re able to gain the insights from the earlier funnel stages as well. Advanced attribution solutions push their attribution data straight into the CRM, which allows both marketing and sales to see a comprehensive picture of each potential customer’s journey, which also keeps sales and marketing properly aligned.
- Sales email outreach performance
- Nurturing sequence interactions
- Lead conversion rates
- Conversion of SALs to Opps
A data-driven marketer’s goal is to make every marketing dollar profitable. B2B marketers can do this by quantifiably measuring the success of their marketing campaigns and optimizing their strategy based on those insights. At the end of the day, data proficiency helps a marketer prove his or her value add, and help marketing teams successfully plan and implement their marketing activities.
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