Demand Waterfall Conversion Rates – The Framework

Been part of numerous B2B marketing analytics journeys across various organizations, I have been witnessing how data and analytics have become the core of modern B2B marketing execution. The data-driven marketing approach with the ability to track demand waterfall conversion rates is a game-changer across the board and no doubt marketing analytics continues to be a top priority for the CMOs and marketing leaders across all the organizations.

Whether it is the hyper-personalized contextual engagement approach, increasingly higher marketing ROI/ROMI, higher influence on pipeline generation/acceleration, or the increasingly better demand waterfall efficiencies,

marketing analytics capabilities span across the areas of marketing execution and the key strategies.

With the ability to leverage data and analytics in real-time across the life cycle of all marketing campaigns irrespective of the channel and the source, all these data-driven strategies are transforming marketing into a revenue-generating function. CEOs/finance/sales leaders are all in this exciting transition led by the forward-looking marketing leaders.

While the B2B marketing analytics and the associated KPIs span across the funnel from awareness to demand generation to pipeline acceleration, Marketing ROI, there is one set of metrics that is top of the mind across marketing teams especially the ones with more matured analytics competencies.

Cohort based demand waterfall conversions are the set of forward-looking metrics and leading indicators that go beyond just tracking the engagement volumes across the funnel to providing the insights into the quality of these engagements across the different stages of the funnel.

Demand Waterfall conversions generally track 5 KPIs with some degrees of variations depending on the demand funnel definitions within an organization:

1. Leads to MQLs (Marketing Qualified Leads)
2. MQLs to SALs (Sales Accepted Leads)
3. SALs to SQLs (Sales Qualified Leads)
4. SQLs to SQOs (Sales Qualified Opportunities)
5. SQOs to Deals

These 5 KPIs along with the ability to slice the data for these KPIs across the dimensions like regions, campaigns, tactics, account segments, etc. provide valuable insights into the efficiencies across the funnel and the time/effort it takes to move the prospects through the funnel. Some of the key questions from the marketing leaders that are answered through this framework include:

• How many engagements it takes to generate MQLs that are accepted by SDRs?
• How many MQLs are needed to generate a new opportunity?
• How long it takes an MQL to convert to an opportunity?
• What is our win rate for all marketing engagements?
• How lead aging affects our ability to meet our funnel goals?
• What marketing tactics have the ability to move the pipeline in the shortest time?

A very important thing to note here is that these insights are derived from an organization’s data (as opposed to some irrelevant benchmarks, generalizations from research companies) and are critical for optimizing the marketing mix and accelerating the customer journey. Every company is unique in terms of what it offers, how it operates, how the go-to-market strategy is.

When you can use your own company’s data for the most relevant insights, the generalizations are irrelevant.

Based on my experience of implementing cohort-based conversion models across more than 30 B2B marketing analytics organizations, here is the framework that I have been following: To begin with, a set of leads are identified based on “Lead/Contact Created” date and their progression through the funnel is followed through the different stages of the funnel. All the dimensions needed to slice and dice the funnel data are included in this initial data set.

1. Lead to MQL conversion: All leads/contacts (Individuals) created in a given time frame are identified. Individual “Created Date” is used as the primary date dimension to track the conversions. Apart from the time dimension, various other dimensions like region, account segment, lead source are added to this first data set. To identify “How many of these leads have converted to MQLs”, MQL date stamps and flags are critical for this analysis so the CRM and Marketing Automation systems should have the workflows to stamp these fields. I look for each of the “People ID” and see if that People ID has been through the MQL stage of the funnel. If this condition is true, I set a flag to identify if that lead has been converted to MQL. The total of all “People IDs” gives the total leads created in a given time frame and of those “People IDs” wherever the MQL flag is set, the total of those are counted as MQLs. The division of these 2 metrics gives the “Lead to MQL” conversion number. With all the dimensions that I have on “People IDs”, I can find conversions by different slices like region, segment, trials, paid-ads, etc.

2. MQL to SAL conversion: SAL phase of the funnel tracks the acceptance of leads by the SDR teams. The acceptance of a lead is usually tracked in the CRM by a combination of “Individual Status” change, date stamping the acceptance or/and the logging of sales activities on the individuals (MQLs in this case) by the SDR teams either manually or by the sales engagement platforms like Outreach, Salesloft. I look at each of the “People IDs” and see if that People ID has been through the SAL stage of the funnel. If this condition is true, I set a flag to identify if that lead has been converted to SAL. Of the MQLs identified in step 1, all those “People IDs” where the SAL flag is set, the total of those are counted as SALs. The division of these 2 metrics gives the “MQL to SAL” conversion number.

3. SAL to SQL conversion: this is the most tricky of the cohort conversion metrics because we have to transcend from “People” objects in Salesforce.com (SFDC) to “Opportunity” object and account for “many-to-many” relationships between MQLs and their influence at generating opportunities.

In B2B marketing, one opportunity can be influenced by more than 1 individual, and at the same time, one individual can influence more than 1 opportunity in a given time frame.

The joys of “marketing influence on the pipeline” keep the world of data analytics exciting!

This part of the framework gets even more exciting (both from the insights it provides and the data analytics tricks – the good old windows and ranking functions in SQL) because I leverage attribution data here to account for the “many-to-many” scenarios. The key question I am trying to answer for marketing here is –

how many engaged leads and marketing engagements it takes to create a new opportunity?

Whether it is the custom attribution algorithm, out of the box algorithm from SFDC, or via popular tools like LeanData, Bizible, FullCircle, when done right, any attribution approach would provide the insights into the relationship between individuals and the opportunities they are influencing. I have been using the “Multi-Touch (MT)” and “First-Touch (FT)” attribution algorithms in my conversion framework. The “FT” approach gives the opportunity influencing credit to only those marketing engagement

s (and in-turn the leads/contacts) that end up being the first touch on the opportunities. The MT attribution model gives a fractional share of each opportunity to the lead/contact depending on the number of marketing touches that are involved.

Now, for the SAL to SQL conversion, I first have to identify all the opportunities that have been created and find the individuals on those opportunities. The marketing attribution data gives this relationship and helps with identifying the “People IDs” (SALs in this case) that are associated with the opportunities. With these details, I identify all those People IDs that have influenced the opportunity creation. I already have calculated (from above) all the People IDs that have been flagged as SALs. With these 2 counts, I can find out how many of these SALs have influenced the opportunity creation and thus, the SAL to SQL conversion.

4. SQL to SQO Conversion: for SQL to SQO cohort conversion, the framework involves identifying the SQLs that have gone beyond the pre-qualification stage. The primary difference between SQL and SQO is when an opportunity moves to a qualified stage. For some organizations that signals opportunity creation by SDRs and for some, it signals an opportunity acceptance by the AEs. For all such opportunities that qualify as SQOs, a flag is set to mark the opportunities that in turn is divided by the total SALs (from above) to get to SQL to SQO conversion rate.

5. SQO to Won Conversion: the logic for SQO to Won conversion follows the same approach as is used for the SQL to SQO conversion. The only difference is that for identifying the won opportunities, I take the SQOs and find how many of these SQOs have been won. From there, I count these opportunities and divide them by the count of SQOs that were identified in the previous step to get the conversion rate or in this case, qualified opportunity to win rate.

Once the logic build for all these KPIs is completed, multiple rounds of data validation and unit testing (taking sample leads and opportunities and tracking their journey through the funnel) are undertaken to confirm that all calculations are working as intended. I then design the visualizations (in popular BI tools like Tableau, Looker, Periscope, DOMO, PowerBI) to

make it easy for the marketing and sales teams to understand the data, the associated trends along with the ability to slice and dice this data

to get the answers for some of the questions from above.

Modern marketing technology and analytics platforms have made it easy to actively track marketing engagements and the pipeline associated with those engagements.

With the right analytics strategy and tools, I work with marketing leaders to empower their teams with these critical data points. These KPIs provide strategic insights to the marketing leaders and CMOs so that they can confidently demonstrate the impact that their organizations are making.

I am always on the lookout for inputs and examples from the marketing community to keep adding value for our customers. I welcome the inputs and feedback from other leaders and practitioners around this approach to tracking funnel conversions and the framework they have implemented at their organizations. What key insights are you driving from the funnel conversions? What are some of the challenges that you are facing to provide these insights for your teams?