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Full Circle Insights

Using the Optimizing Dashboard

AFTER READING THIS ARTICLE, YOU WILL BE ABLE TO: Understand all the components of the Optimizing dashboard and get ideas on how to most effectively use the provided information. 

 

Optimizing

The Optimizing dashboard provides key operational metrics, such as funnel stage volume, conversion rates, velocity rates, and how things are trending over time. The overall goal of the Optimizing dashboard's metrics is to identify patterns (and anomalies) in the data—for example, are things trending upward, downward, sideways, etc. 

The Optimizing metrics category includes measurement components that can greatly enhance operational line of sight and success, and facilitate strong sales and marketing alignment. We will discuss this category’s metrics in detail and consider how to most effectively utilize them.

Importantly, nearly all the metrics in the Optimizing category are based on monthly time cohorts in an effort help effectively identify patterns and extrapolate immediate insights. The Optimizing dashboard also has a ‘Filter By’ drop-down feature natively built into it to help quickly explore different time cohorts of data. 

Optimizing – Filter By.png

Following are the key metrics included in the Optimizing category:

  • Worked/Unworked MQLs
  • Unworked MQLs by Rep
  • MQL Disposition Reasons by Rep
  • MQL Metrics
    • MQL-to-SAL Conversion Rates
    • MQL-to-SAL Velocity Rates
    • MQL Disposition Reasons
  • SAL Metrics
    • SAL-to-SQL Conversion Rates
    • SAL-to-SQL Velocity Rates
    • SAL Disposition Reasons
  • SQL Metrics
    • SQL-to-Opportunity Conversion Rates
    • SQL-to-Opportunity Velocity Rates
    • SQL Lost Reasons
  • Opportunity Metrics
    • Opportunity-to-Won Conversion Rates
    • Opportunity-to-Won Velocity Rates
    • Opportunity Lost Reasons

Notably, each of the funnel stages—MQL, SAL, SQL and Opportunity—has four distinct dimensions that are measured:

  • Conversion Rate (%)
  • Velocity Rate (in days)
  • Volume
  • Disqualification/Lost Reasons

So, for example, the Optimizing dashboard provides clear line of sight on the key metrics for MQLs by month: conversion data; velocity data; volume data; and reasons for disqualification. It is powerful to have all these metrics, by funnel stage, on a single viewable plane and contained in a concise, intuitive dashboard.

Let's consider a specific use case for how a marketer might utilize some of the metrics provided in the Optimizing dashboard. 

Example Use Case: Investigating and Optimizing Lead Quality

The metrics in the Optimizing category can be used in many ways. One use case is understanding how lead quality may be impacting marketing operations and revenue. This often manifests itself in Sales communicating they believe Marketing isn’t sending them high quality leads. Following is one approach you may want to consider if this happens in your organization.

Looking at your data in time cohorts will be an important thing to do in a situation like this. Ideally, a marketer will want to look at their data over time to see if different patterns have developed. 

You may want to begin your investigative process by looking at MQL volume, and MQL to SAL conversion rates and velocity rates over the previous 12 months. Are you able to identify any obvious patterns? Following are some specific considerations.

MQL to SAL Conversion Rate

Using the Conversion Rate dashboard component, evaluate if conversion rates changed materially over any particular time cohort. If yes, this might be the first indicator of where to dive deeper into your data. Based on this sample data, while it does appear some changes occurred with conversion rates over the past three months, it does not appear anything significant changed. The conversion rates remained fairly consistent between 71% and 81% over the three month cohort. 

What is interesting in this example data set—and perhaps worth noting—is that the volume of MQLs over the three months has decreased over time (indicated by the blue trend line). In the real world it's often not unreasonable to see an inverse relationship between MQL to SAL Conversion Rate and Volume of MQLs—that is, as MQL volume increases, MQL conversion rates decrease. This is frequently due to the fact that Marketing or Sales may have added a large quantity of net-new people to the database, and their lead quality was perhaps prematurely increased; thus, the lower conversion rate for that time period.

Key Takeaways: 

Even though we're not yet seeing it in any significant way, we may want to watch these data over the next two or three months to see if an inverse relationship pattern develops. If it does, perhaps there is something with our process that needs to be investigated. For example, is Sales being overwhelmed with too many qualified leads (MQLs) from Marketing, and thus needing to disposition them prematurely just to keep up with the pace of leads? Might there be a sort of "lead equilibrium" that is ideal for the organization, where the volume of leads and the conversion rates are maximized? Or might Sales need to add more headcount to efficiently and effectively handle all Marketing's qualified leads? These are the things Marketers will want to monitor over time and collaboratively discuss with Marketing and Sales Management. 

MQL to SAL Velocity Rate

Using the MQL to SAL Velocity Rate dashboard component, we can have a closer look at MQL to SAL velocity rates. 

The first thing that jumps out in this monthly cohort data visualization is the relatively significant decrease in velocity rate from January and February as compared to March. (This is a great example of what makes visualizing data so helpful (as compared to reading raw data)—here, the anomalies literally jump out at you.) The March velocity rate is .32 days (which means it's taking Sales less than a half day, on average, to take action on an MQL). However, in the months of January and February, the velocity rates were 1.2 and 1.4, respectively. So, March has experienced a material change in MQL to SAL Velocity Rate—almost a full day has been shaved off. The question is: why? 

In a situation like this, the first natural assumption to make is that perhaps the volume of MQLs in the month of March decreased so significantly that it enabled Sales to take action on the MQLs much more quickly. However, the data shows (blue trend line) the difference in volume of MQLs from February to March only decreased by about 30 (420 in February; 390 in March). This is a decrease of only 7% in MQL volume month-over-month, so we can safely assume that MQL volume did not have a material affect on the MQL to SAL Velocity Rate. 

Could it be that Sales has become extremely proficient and efficient in taking action on MQLs? This could very well be, especially considering MQL to SAL conversion rates have not changed over the same time period. 

Key Takeaways: 

  • Quickly engage in a dialogue with Sales and Sales Management. Ask them if they have indeed become more proficient in taking action on the MQLs Marketing is delivering to them. If they have, perhaps there's a need for Marketing to increase the amount of leads (MQLs) they produce. And/or perhaps there's a need to reconsider any lead scoring programs that are currently in place—specifically, decreasing the scoring threshold to make a lead Marketing Qualified (MQL). 
  • It would be prudent to watch these monthly cohort data over the next month or two to see if this pattern continues—that is, very low velocity rates. It is equally important to compare the velocity rates to the conversion rates. If conversion rates continue to remain the same (i.e., good) and velocity rates continue to remain low (relative to January and February), there would appear to be some opportunities for process optimization (e.g., Marketing making adjustments to lead scoring parameters, and/or increasing lead volume). 
  • It may be worthwhile looking at any of the above data with different filters. For example, are there perhaps certain segments (e.g., Enterprise, Mid-Market, SMB) experiencing different patterns? What about industries? Or geographies? Maybe there are certain segments—or clusters—in your business that are experiencing some MQL metrics changes, and things can be optimized.  

These are examples of things that could be explored to help determine if there may be things that are happening to affect the perception that lead quality has decreased. Using these data like this can facilitate a powerful, data-driven dialogue with Sales—and increase Sales and Marketing alignment.

MQL Disposition Reasons by Rep

The MQL Disposition Reasons by Rep dashboard component can help add important context to lead processes—and can help optimize previously unknown issues. 

This dashboard component helps us accomplish two things. First, it tells us how many MQLs each rep is disqualifying. And it also tells us for all the MQLs being disqualified, what are the reasons each rep is disqualifying them—and how many for each reason.

Once again, because we are visualizing our data, several things jump out at us. 

It immediately appears several reps are disqualifying significantly more MQLs than other reps. This could end up being okay—but this dashboard component and its underlying report and data help us have a data-driven conversation to find out more.

For example, could it be a mere coincidence that the volume of MQLs being dispositioned by each rep closely resembles the actual distribution of MQLs (i.e., the volume of MQLs being assigned to each rep)? If yes, maybe that's okay. But if not, then perhaps we have some investigating to do. 

For example, let's say Oz—who's shown here—has the highest volume of disqualified MQLs. And we also know that Oz is one our reps being assigned the fewest quantity of leads. So, in other words, Oz is receiving the fewest MQLs—and yet he's disqualifying the most. This would be something we would want to explore further. It might be that Oz is simply being assigned some really poor quality leads. Or it could be something totally different. In reality, it could be the result of a variety of different things—and having these data clearly reported and visualized help us move forward with a data-driven, collaborative conversation with Oz and Sales Management. 

Also, why does it appear that only some reps are using the Junk Data disqualification reason (in purple color)? And why are some reps using it significantly more than others? Is the organization truly distributing a higher quantity of lower quality leads ("Junk Data") to some reps? That could be a significant operational and organizational issue—we may be unknowingly putting some reps at a significant up-front disadvantage to meet their numbers and contribute value to the organization by giving them poor quality leads from the very start. 

Also, this is a good time to point out it appears something is missing from our data: there appears to be a disqualification reason that is blank (the royal blue label). And it appears this disqualification reason is being used by several of our reps. This is where it's important to have a qualitative dialogue with your sales team to find out what is happening here. Why are some reps not using the blank disqualification reason—but others are using it a lot? Might there be a training opportunity here? Might there be a new disqualification category that needs to be added (or changed) to better meet the reps' disqualification reason needs? Might there be an operational issue with how reps are using the CRM and its MQL disqualification criteria? Don't underestimate the power of getting this blank disqualification reason cleaned up; it may end up yielding some powerful insight for Marketing, Sales, and the entire organization. 

MQL Disposition Reasons

The MQL Disposition Reasons dashboard component can help us evaluate, in aggregate, any trends in MQL disqualification reasons. 

For example, in this sample data visualization, we can quickly determine the following insights:

  •  It appears there is a downward trend in the overall volume of disqualifications between January and March
  • The blank disqualification reason (again, labeled in royal blue) appears to be reducing in quantity over time
  • 'Junk Data' disqualification reason volume has been consistently lower in February and March than January
  • 'Not the Right Fit' disqualification reason appeared for the first time this fiscal year

Also, even if we were to momentarily ignore the high number of blank disqualification reasons in January, it appears January had a high volume of disqualifications. We might want to explore this metric a bit further to see what might have caused this—for example, perhaps Marketing imported a large net-new list of contact records to the database that month. 

The same type of analysis can be performed for each of the other funnel stages, too. Are we able to identify certain disqualification/lost patterns over time? 

Lost SQL by Reason

The Lost SQL by Reason dashboard component can help us see interesting patterns develop over time. For example, losing SQLs because of 'Budget' should always be something in which a marketer is keenly interested. If the organization is losing prospects at this stage of the buying process because a prospect has an issue with the cost of your organization's solution or service, then it's a good bet both Marketing and Sales could have done a better job establishing value from the very beginning of the organization's engagement with the prospect. In other words, the prospect ideally shouldn't care (within reason of course) what the cost of the solution or service is; they should desire it so badly that the cost is justifiable. 

The disqualification/lost dashboard components can really help inform messaging, content, and sales enablement priorities and strategies for the organization, since it can help surface the reasons why prospects are not interested in doing business with an organization. 

An Additional Consideration:

Each of the Disqualification/Lost dashboard components can be helpful to determine if and when an organization reaches an inflection point to start a formal and/or sophisticated lead nurturing program. By analyzing disqualification patterns over time marketers can start to see, for example, when and why "Not the right fit" or "Too early" or even "Purchasing competitive product" reasons are trending higher, and put together sophisticated nurturing programs to recycle these potentially future opportunities. This is yet another example of how the Optimizing dashboard can help add instructive, actionable insights and value for an organization. 

Review of Additional 'Optimizing' Dashboard Components

Worked/Unworked MQLs

  • This dashboard component shows the current ratio of Worked/Unworked MQLs by month. In other words, these data show the percentage of leads with which Sales has not yet engaged. Ideally, a marketing organization would want to see all 'False' values, meaning there are no unworked MQLs (i.e., the value of 'True' means there are unworked MQLs). This is particularly true in organizations where the expected time for Sales to follow up with leads (aka, Service Level Agreement—or SLA) is within a short timeframe (e.g., 24 hours). 
  • Time Parameter: Fiscal Year to Date in monthly cohorts

Unworked MQLs by Rep

  • The Unworked MQLs by Rep dashboard component shows the total volume of unworked MQLs by rep.
  • Time Parameter: All time

Note: New customers deploying Response Management from April 1, 2017 onwards will receive this reports and dashboards package as part of the implementation.  Existing customers interested in this package should log a case on the Full Circle Community to arrange to have the package installed.

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