Using the Optimizing Dashboard

Keep Up with the Trends (in Your Data)

Note: While this video refers to Response Management, its content applies specifically to Funnel Metrics.

Some trends -like the fad for bell bottom jeans- don't matter all that much. Trends in your data matter a lot. Your Optimizing dashboard lets you track trends in key operational metrics, like funnel stage volume, conversion rates, and velocity. By identifying patterns and anomalies, you can adjust your strategy on-the-fly and maximize the success of your marketing plan.

As you dive in, you'll notice that nearly all the metrics in the Optimizing category are grouped by month. This default is ideal for effectively identifying patterns and extrapolating immediate insights; however, you can also use the built-in Filter By drop-down feature to explore different time cohorts of data.

The Optimizing dashboard gives you visibility into critical areas like:

• Worked/Unworked MQLs
• Unworked MQLs by Rep
• MQL Disposition Reasons by Rep
• MQL Metrics
• SAL Metrics
• SQL Metrics
• Opportunity Metrics

Notably, each of the funnel stage components (MQL, SAL, SQL and Opportunity) measures four distinct dimensions:

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

Let's see what this all looks like in practice.

Problem Solving with Data: Investigating and Optimizing Lead Quality

Let's say that your company's sales team has communicated that the quality of leads they're getting from the marketing team isn't high enough. It's a complaint worth taking seriously, and your Optimizing dashboard empowers you to get to the bottom of it.

Time cohorts will play a major role throughout your investigation. You'll want to use the Filter By dropdown to see if any patterns have developed over time.

In this scenario, it makes sense to begin by digging into MQL volume, as well as MQL to SAL conversion rates and velocity rates over the previous 12 months. Are you able to identify any obvious patterns?

MQL to SAL Conversion Rate

Using this chart, you can determine whether the conversion rate from Marketing Qualified Lead to Sales Accepted Lead has materially changed over time. If it has - especially if it's dropped - that would certainly substantiate Sales' claim that Marketing's leads aren't up to snuff. That would a a good jumping-off point for a deeper dive into what was going on with Marketing around the time that the decline in lead quality began.

According to the data here, however, nothing seems wildly amiss. The changes that occurred within the given time period aren't out of the ordinary, with the green bars representing conversion rate holding pretty steady.

Pay attention to the blue line, though. That trend line represents the total volume of MQLs, which has noticeably decreased. This isn't necessarily unusual: Especially if Marketing or Sales has added a large number of new names to the database, it's common to see an increase in MQLs (because lead quality may have prematurely increased) at the same time as a decrease in conversion rate.

Action Items:

You'll want to keep an eye on this chart for the next two or three months to see if an inverse relationship develops between MQL volume and conversion rate. If it does, this may point to a process issue. Maybe Sales is being overwhelmed with too many MQLs and dispositioning them prematurely just to keep up with the pace. In this case, is there a way to balance the volume of leads and maximize the conversion rates? Or is it time for Sales to hire additional reps to handle all these great leads from Marketing?

MQL to SAL Velocity Rate

While you're waiting to see how the trend bears out when it comes to conversion rate, you can move on to check on your MQL to SAL velocity rate. Here's the chart:

Now, THAT'S what you call an anomaly! Back in January, it took Sales 1.2 days to act on an MQL. In February, it took 1.4 days. All of a sudden, in March, bam! 0.32 days, meaning Sales now takes less than a day to respond to an MQL. They've more or less shaved a whole day off their response time...but don't pop that champagne yet. First, ask yourself how this happened.

It's easy to assume that there just weren't very many MQLs in March, and so Sales could act on each one more quickly because they had less on their plate. Not so fast! That blue trend line shows fewer MQLs in March than in February, but it's only a 7% drop (from 420 to 390). That's nowhere near as dramatic as the decrease in velocity, so it's safe to say MQL volume wasn't the driving factor here. Again, this points to process. Recall that MQL to SAL conversion rates held steady over this time period: Could it be that Sales has become extremely proficient and efficient in taking action on MQLs?

Action Items:

• Check with Sales and Sales Management to see if they've implemented changes that increased their efficiency when acting on MQLs. If so, Marketing might need to increase the amount of leads (MQLs) they produce. Or maybe it's worth decreasing the scoring threshold for MQLs.
• Over the coming months, watch this data to see if velocity rates remain low. Remember to also compare the velocity rates to the conversion rates: If conversion rates remain high and velocity rates remain low, there are likely opportunities for process optimization (e.g., Marketing making adjustments to lead scoring parameters, and/or increasing lead volume).
• Try examining the above data with different filters. Are certain segments (e.g., Enterprise, Mid-Market, SMB) seeing 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. 
   

MQL Disposition Reasons by Rep

The MQL Disposition Reasons by Rep component tells you several important things. First, it lets you know how many MQLs each sales rep disqualifies. Second, it tells you why each rep is disqualifying those leads. This can add context to your lead processes, and it can surface previously unknown issues so you can optimize them.

Immediately, you'll notice that certain reps disqualify way more MQLs than others. Should you be worried? Maybe — or maybe not. This chart and its underlying reporting data can shed some light on the issue.

First, you'll want to check and see how closely the volume of each rep's dispositioned MQLs aligns with the actual distribution of MQLs assigned to them. If they're all disproportioning about the same ratio of leads, that's fine. If not, it's time to get in touch with your inner Sherlock Holmes.

Oz (third from the right on your chart) has the highest volume of disqualified MQLs. You happen to know that Oz has been assigned the smallest number of leads. So, in other words, Oz is receiving the fewest MQLs and disqualifying the most. Hmmm. It could be that Oz has really bad luck and has simply been assigned a streak of really poor-quality leads. But there could be any number of other explanations, and there's no getting around the fact that it's worth having a conversation with Oz and Sales Management. Bringing this clear, visualized data to the table will help keep that conversation focused and collaborative.

Looking at the larger picture, there's something else unusual here. Some reps disqualify with the Junk Data reason quite a bit, but others don't use that reason at all. If the organization is truly distributing more low-quality leads to some reps than to others, that could point to an operational and organizational issue that puts certain reps at a significant up-front disadvantage when they're trying to meet their numbers and add value.

There's also a blank disqualification reason (in royal blue), and several reps use it a lot. This tells you that it's time to have a conversation with Sales to see what's up. Why do reps use this reason? It may simply be a matter of needing to add or change a reason to better meet reps' needs. Alternatively, is there a training opportunity here? An operational issue with how reps are using the CRM and 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.

Action Items

• Meet with Oz and the Sales Management team to understand his unusually high rate of lead disqualification.
• Investigate the possibility that some reps receive an unfairly high proportion of poor leads.
• Ask for Sales' input regarding the blank disqualification reason, and make changes as necessary.
   

MQL Disposition Reasons

Rather than focusing on the reps' dispositioning of leads, this chart focuses on the disposition reasons themselves.

Here, four patterns jump out immediately:

• A downward trend in the overall volume of disqualifications between January and March
• Decreasing use of the blank disqualification reason over time
• Less use of the 'Junk Data' disqualification reason in February and March than in January
• Appearance of 'Not the Right Fit' disqualification reason for the first time this fiscal year

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

Action Items

• Check other dashboard components to see what might have caused the January spike.
• As necessary, analyze other funnel stages in the same way. This may enable you to spot disqualification/lost patterns over time.

Lost SQL by Reason

Nobody likes losing leads. Unfortunately, it's inevitable. Fortunately, there's a silver lining: When you can track the reasons why those leads were lost, you can understand how to lose fewer of them in the future through messaging, content, and sales enablement strategies.

Just as you saw markedly fewer dispositioned leads in March compared with January and February, you're seeing the same pattern here: A steady and substantial decrease in the number of lost leads.

As a marketer, you'll want to pay particularly close attention to the "Budget" reason. If your company is losing prospects at this stage of the process because they're objecting to the cost of the product or service, then it's a safe bet that Marketing and Sales could have done a better job establishing value from the very beginning of the engagement. This late in the game (within reason, of course!) about the cost. They should want what you're selling so badly that they can justify the expense.

Note which reasons appear in all three months: "Demo Didn't Happen," "Not a Fit," "Too Early," and "Went with a Different Solution." You can't do much about "Too Early" other than set those leads to nurture, but the other three are actionable. Why didn't demos happen, and how can that issue be addressed with future demos? In what way was your solution not a fit, and should Marketing qualify leads differently to avoid this problem? And for the leads that went with a different solution, which solution, and what made it more appealing?

Action Items:

• Verify if the decrease in lost leads shares a cause with the decrease in disqualified ones.
• Investigate the context around reasons that come up over and over again, and work with Sales and Marketing to prevent this from happening.

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 component shows the current ratio of worked and unworked MQLs by month. In other words, you can check this chart to see what percentage of leads Sales has not yet engaged. Since green "True" values indicate unworked leads, you're ideally hoping to see solid blue "False" bars on the chart, especially if your organization has a short timeframe for follow-ups from Sales.

Here, you've got a remarkably consistent proportion of leads left unworked each month, despite the drop in overall MQLs. That's worth looking into and rectifying, especially if it turns out to be happening because certain reps aren't pulling their weight.

Action Item

• Identify source and reason for unworked leads.

Unworked MQLs by Rep

Remember that idea of checking in on all those unworked leads to find out if there's an issue with a specific rep or reps? Here's a donut chart to help you do just that!

Note that the time parameter here is not monthly, but rather all time. It looks like Luke Duncan is responsible for a huge proportion of unchecked leads over time, so it's well worth having a conversation with him and the Sales Management team.

Action Item

• Identify reasons why Luke has left so many leads unworked, and work with him and Sales Management to rectify the issue.