Marketing Operations Analytics

I have listened to more than 13,000 podcasts, mostly focused on digital marketing. I recently came across a podcast called DemandGen Radio. Dave Lewis is the host and was reading a chapter from his Manufacturing Demand book. Interesting that I have had this book in my want to read list in Goodreads since 2013. If this is really the #1 book on lead management as is stated on the cover, a gross error on my part for not getting to it sooner.

So far, I have only listened to two episodes, #260 and #261, that was a two-parter called Marketing Analytics: Keeping Score of Your Success. Rarely do I take notes listening to a podcast, but this was brilliant and so much value was shared. I am sharing my notes and thoughts in hopes it’s beneficial to others.

It seems like marketing is too often trying to prove they deserve a seat at the adults table instead of being at the kid’s table making arts and crafts. Perhaps that leads to oversharing when we finally get the opportunity, or sharing vanity metrics, or just not speaking the language of the boardroom. Instead of sharing marketing activities including impressions, likes, or our recent ribbon win for the prettiest booth, let’s share metrics that lead to financial outcomes. There are three major types of marketing analytics or key performance indicators (KPIs).

The first set of analytics metrics are Executive KPIs and they need to measure the entire demand generation spectrum.

Executive KPIs

  1. Marketing sourced leads and opportunities
  2. Marketing contribution to revenue
  3. Marketing’s influence on opportunities and revenue

The second set of analytics metrics are Demand Funnel KPIs and they measure the velocity and efficiency of our demand funnel.

Demand Funnel KPIs

  1. How many prospects at each stage of the funnel
  2. Conversion rate between each of these stages
  3. Average time in each stage – this tells us the velocity of the demand funnel
  4. Lead scoring distribution – how many A, B, C, D, and E leads and does this look anything like a bell curve
  5. Campaign performance – number of leads, what channel or lead source, opportunities, revenue

The third set of analytics metrics are Campaign and Asset Performance KPIs and they measure the success of our assets driving leads to a closed stage.

Campaign and Asset Performance KPIs

  1. Use of our assets – tracking downloads for PDFs or how much of the video was watched like 25%, 50%, 75%, 100%
  2. Closed/won asset utilization – what assets get read by prospects who eventually buy

The three most important things to track with any form submission are the channel, lead source, and offer. Probably the most common way to implement this tracking is to capture the UTM tracking parameters in the URL query string and make sure they are passed as hidden fields in the form submissions. That form submission is tied to the Salesforce campaign object or whatever makes sense using another CRM. It’s important to make sure you can track every stage and really everything from that first click to the close of the sale. Until you know you are accurately measuring from click to close, you have a leaky funnel and nothing should be shared until you have confidence in your data and know you won’t lost trust.

The closing part of the podcast shared the four Cs.
1. What you can count – This reminds me of two quotes. First by W. Edwards Deming who said “If you can’t measure it, you can’t manage it”. Second by Lord Kelvin who said “If you can not measure it, you can not improve it”. The last thought is one I hear often, just because you can count/measure/track it doesn’t mean you should.
2. What counts – Although the Executive KPIs clearly cover what counts, there are other metrics that we want to track within our marketing group like micro conversions. Not everything tracked needs to be shared.
3. What you can count on – how important it is to trust your data so others trust you. When trust is lost, just about all is lost.
4. How you communicate it – often marketing needs to do a better job at marketing marketing. We could learn something from our sales colleagues in sharing what is working and that we are critical to the company’s success.

Preparing to Migrate to Google Analytics 4 (GA4)

There has been plenty of emotions leading up to Google Analytics 4 or GA4 being released and the increased pressure to make sure we are each ready. I finally decided to try to make sense of Google Universal Analytics and GA4 and here were my key notes in case others find this helpful.

  1. Dates – Universal Analytics (UA) deprecated July 2023 and UA 360 now deprecated October 2023.
  2. Users – same term used in GA4 but it means active users instead of total users.
  3. Model – UA is session-based (a session was a group of user interactions) where GA4 is an event-based model.
  4. Engaged Session is the count of sessions that lasted longer than 10 seconds, or had a conversion event, or had two or more screen/page views. This will replace the pages per session metric.
  5. Average Engagement Time Per Session is the amount of time the user is actually engaging with the page and is the page on the primary window being viewed on screen. This will replace the average session duration metric.
  6. Engagement Rate is the ratio of engaged sessions relative to total sessions. This will replace the bounce rate metric although it can still be calculated as the inverse of the engagement rate. 100 total sessions with 15 of them being engaged sessions results in a 15% engagement rate.
  7. Four Categories of Events
    a. Automatically collected events like user engagement, in-app purchases, and firebase app interactions.
    b. Enhanced measurement events (change in user interface; no code changes required) like page views, scrolls, form interactions, and video engagements.
    c. Recommended events that have predefined names and parameters like online sales and user behavior.
    d. Custom events that you define and create when existing events don’t exist.
  8. Segments – both in UA and GA4 you can compare up to four segments. Types of segments in GA4:
    a. User segments – subsets of users who engaged with your site/app like users from a page or channel.
    b. Event segments – subsets of events that were triggered on your site/app like purchase events.
    c. Session segments – subsets of the sessions that occurred on your site/app like a particular advertising campaign.
  9. Segmentation Conditions tell analytics what data to include in or exclude from the segment. There are three segmentation conditions:
    a. Dimension conditions like demographics, geography, and technology.
    b. Event conditions about particular details on one or more events. This is new to GA4.
    c. Metric conditions based on predictive metrics like an in-app purchase probability is above the 90th percentile.
  10. Attribution Modeling is assigning credit for conversions to different ads, clicks, and other factors. There are three types of attribution models available in the Attribution reports:
    a. Cross-channel rules-based model ignores direct traffic and attributes 100% of conversion value to the last channel that the customer clicked through or engaged view through for YouTube before converting. Other cross-channel rules-based models include:
    i. Cross-channel first click – all conversion credit to first channel that a customer clicked.
    ii. Cross-channel position based – attributes 40% credit to first and last interaction and remaining 20% credit distributed evenly to middle interactions.
    iii. Cross-channel linear – distributes credit for conversion equally across all channels a customer clicks.
    iv. Cross-channel time decay – gives more credit to touchpoints that happened closer to time of conversion. Uses a 7-day half life so a click 8 days before conversion gets half the credit of a click 1 day before a conversion.
    b. Ads-preferred rules-based model – attributes 100% conversion value to the last Google Ads channel that the customer clicked before converting. If there is no Google Ads click, attribution model falls back to cross-channel last click.
    c. Data driven attribution – uses machine learning algorithms to evaluate converting and non-converting paths. Distributes credit for the conversion based on your account data for each conversion event.
  11. UTM parameters – there are two new UTM parameters in GA4. See for more details.