Revenue Operations

Boost Marketing Productivity With Analytics

Marketing has to use a lot of applications to meet prospects where they are. They generate a ton of data, but the systems aren’t developed with simplicity or integration in mind.


In other words, making sense of all that info is #complicated. 


Unfortunately, it’s about to get a lot more complicated. The companies that adapt to the new data-driven reality we face will gain the most market share. 


Building a strong data infrastructure should be at the top of RevOps priorities so your company’s go-to-market team isn’t left in the dust in 2022.

What’s Changing?

Safari, Firefox, and iOS devices have upped their browsing security, banning third-party cookies and reducing the timeframe they store first-party cookies. They have gone from 28 days to seven days of storage, and in some cases (where identifying information is collected), 24 hours. 


As of January 1, 2022, Chrome will be following suit and ban third-party cookies. Those of us watching market trends are certain they will also place much stricter time limits on first-party cookies. As the browser with the heftiest market share for desktop devices, this will have a big impact on marketing.



Traditional search and retargeting campaigns are already less effective (by as much as 60%), but not every company has noticed. Too many people don’t review pipeline and revenue results because their systems aren’t integrated or they don’t prioritize analytics.


There are things you can do to help marketing prepare and improve their tracking:

  • Make sure the latest pixel tags leveraging first-party technology are properly installed and tested
  • Research whether the vendor your team is using for retargeting is working directly with publishers to ensure target matching (rather than third-party cookie tracking)
  • Explore device ID technology as an alternative
  • Collect as much first-party data as possible


Because privacy laws and browser companies are both trending in a more secure direction, your teams must research the most efficient and effective ways to collect more first-party data that align with regional, country, and state laws. It’s time for marketing to become less reliant on external sources.

Quick Wins: Best-Practices In Action

Checking in to see if the team could use some technical support never hurts, but make sure you navigate this tactfully. Remind people you’re part of the same team and that helping them spend less time on crunching numbers is a big priority.


Quick wins can include:

  • Documenting and rolling out UTM standards
  • Installing web-logging code to capture website interactions
  • Establishing campaign hierarchies and data requirements
  • Verifying your URL with your advertising vendors (Facebook, LinkedIn, etc.)
  • Aligning ICP (ideal customer profile) definitions across marketing and sales
  • Integrating your advertising partners at the API level
  • Establishing a universal definition of an MQL (marketing qualified lead) or MQA (marketing qualified account)


UTM parameters are super useful when it comes to identifying how a person navigated to a conversion point. For example, if someone clicks on a Facebook ad then clicks on different pages before asking for a demo, that demo campaign entry will be marked with a source of Paid Social if you use UTMs.



The more structured your information comes into the system, the easier it will be to translate the data coming out of it.

Where Should All That Data Go?

Two things need to happen before maximum marketing effectiveness can be achieved:

  1. All relevant data has to be collected in a centralized location
  2. That data needs to be normalized—or matched up across accounts and contacts—so we can see every interaction leading up to a sale


A lot of marketers are forced to make decisions based on early indicators. Unfortunately, an ad that generates a lot of traffic to the website doesn’t always translate into increased revenue. It’s critical to link up activities to pipeline and revenue so marketing can understand if they engaged the right audience with their targeting and creative.


Because so much information is generated by marketing (particularly by website engagements), you’ll either need to purchase a CDP (customer data platform) that’s capable of integrating with all of your sources and normalizing the data or build your own data warehouse and models to go with it.

What Matters?

Like we mentioned, looking at early indicators to determine whether a campaign is effective isn’t enough. Ultimately, campaigns should be evaluated with engagement scoring, source activity, and attribution. 


Some campaigns are meant to drive awareness (engagement activity and attribution), others are meant to inspire people to engage directly with the company (source activity and attribution), and others are intended to inform the buyer committee and keep a sale moving along (attribution).



Tying all campaign activity to revenue through attribution models is essential--but so is having the right resource in place to translate all of that information into insights. Analysts must understand that pipeline and revenue are key, and if a lot of engagement leads to a little pipeline, the campaign may not be worth continuing.


We can only tell if campaigns are working if they translate into meetings and sales, which take time. Early indicators are still important, but shouldn’t overshadow pipeline evidence collected later on.

Striking a Balance: Campaign Improvement

Campaigns should be reviewed daily to ensure peak performance. But before that can happen, teams must be united on the definition of ICP and MQL. This means understanding existing conversion rates and what is and isn’t working.


For example, I was at a company that considered Content Syndication an MQL. When we looked at all campaign sources and how MQLs were converting into meetings, the average was 7% and Content Syndication converted at less than 1%.


Instead of debating why sales didn’t want to follow up on these leads, we put email nurture campaigns in place for these leads and did not consider them an MQL until they engaged with some other activity.


We saw a $1 million uplift in generated pipeline in a single quarter by effectively decluttering the MQL pool.


Once everybody reaches an agreement on definitions, you can focus on combining early and late indicators to ramp up marketing productivity.

Use Case 1: Understanding Causation and Correlation in Email

Email used to be very effective. People were constantly monitoring their work email and that automatic preview feature most email carriers provided meant that the message got through more often than not.


Then everyone figured out email marketing worked and started blanket bombing lists they purchased.


And people got sick of email marketing.



Then the pandemic made digital fatigue even more real.


It’s still possible to get high open rates and engagement through email, but this can’t happen if you’re using the same old playbook.


Today, you must:

  • Offer something worth subscribing for
  • Collect email data through proactive opt-ins
  • Follow GDPR and other guidelines
  • Consistently provide value
  • Limit your frequency
  • Stay away from the hard sell


If you’re following these rules of engagement and still wondering what isn’t working, use the following metrics to sleuth out the problem:

  • How are your open rates? (low rates could indicate blacklisted servers, a weak subject line, a reliance on SPAM trigger words, or too much formatting)
  • What do your click-through rates look like? (low rates could indicate too many words, too many images, too many CTAs, or the wrong message)
  • Did you test the user experience?
  • How is the page you are directing your audience to perform? (high bounce rates and short visit durations could mean your email copy doesn’t align with the offer, the offer isn’t compelling, or the offer doesn’t give the person enough value up-front to keep reading or perform the next action)


When measuring email, balance what is trending in the market and what is or isn’t working for your company.

Use Case Two: Gauging Facebook Ad Performance

Not every business believes in Facebook, but we have to hand it to them. They have a huge share of social media users and machine learning that is really effective. To get the most out of Facebook, marketers should be using very specific targeting (one-to-one email matching wherever possible) and stay away from interest-based campaigns.


Once your targets are defined, it’s time to set goals for the campaign and watch it like a hawk. This doesn’t mean you should kill your campaign if you’re not seeing as many click-throughs as you’d like in the first 24 hours! Facebook improves its targeting algorithms as time goes on, changing where and how your ad is surfaced. If people begin to engage with your ad (even if there are some negative comments), shutting that ad down means you lose the social evidence needed to help elevate your message to more people.


Early indicators you should follow:

  • Click through rates (be sure to review what’s normal in your industry and expect this to trend up over time)
  • Form fills (Low form fills may mean your message isn’t in alignment with your ad or your offer isn’t compelling enough to inspire a form fill)
  • Landing page statistics (High bounce rates may mean your message isn’t in alignment with your ad or your offer isn’t compelling enough to inspire a form fill. Low form fills may mean you’re asking for too much data)
  • Low chat engagement (Your user experience may be annoying. Check for loud sounds, music, and too many questions gating engagement)



As always, revenue should be your true north. If your $500 Facebook advertisement only generated five form fills, but one of those form fills led to a $150,000 sale, it wasn’t a waste of time or money.

It’s Only a Failure If You Learn Nothing

The key to selling marketing on leveraging analytics is to change the fixation on reports being the end of the campaign. A campaign’s data should be viewed as a jumping point to improve the next campaign.


Embrace the mentality that there are no bad campaigns, only lessons—and encourage your marketers to do the same.


Marketing analytics requires a balance of market context, leading indicators, and long-range revenue data. Once you strike a balance, educate your marketers on which insights can positively impact their campaigns, and automate your reports as much as possible, the sky is the limit.


This is a lot to cover in a single post! If you want us to provide more information on a specific topic or feel we didn’t cover a topic effectively, let us know! Our goal is to provide you as much value as possible with each piece of content.


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