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Using the Evaluating Dashboard

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

 

Evaluating

The main objective of the Evaluating category is to provide the marketer with focused, prioritized metrics to evaluate marketing program performance—particularly in terms of how Marketing’s programs are contributing to the business (revenue). Further, this category of metrics empowers marketers to explore their data like never before. 

For example, we can answer questions such as: how are marketing investments in industry conferences performing? Are there particular industry conferences that are outperforming others? How are conferences performing as compared to webinars? Are partner marketing activities yielding a lot of opportunities—but not a lot of revenue? The Evaluation category of metrics is ultimately about understanding how and where Marketing is creating tangible value for the organization.

Following are the key metrics included in the Evaluating category:

  • Opportunity to Won Conversion Rate by Campaign
  • Opportunity to Won Velocity Rate by Campaign
  • Average Deal Size by Campaign
  • Top Campaigns by Attribution – Most Revenue (Won)
  • Top Campaigns by Attribution – Most Revenue (Lost)
  • Top Campaigns by Attribution – Most Won Opportunities (Count)
  • Attribution by Campaign Type
  • Average Number of Touches for Won and Lost Opportunities
  • Disposition Reasons – MQL Disposition Reasons by Campaign Type
  • Disposition Reasons – SAL Disposition Reasons by Campaign Type
  • SQLs by Lost Reason
  • Pipeline by Lost Reason
  • Touch Timing by Type – Won
  • Touch Timing by Type – Lost

There are many different ways to gain powerful insights by using the metrics in the Evaluating category. In fact, an underlying premise of this category is that the data should be consistently explored by marketers. There are layers upon layers of data created and contained in our sales and marketing systems on a daily basis that go largely undiscovered, and the metrics in the Evaluating category provide a gateway for discovering previously unknown insights. 

Example Use Case: Evaluating Campaigns by Conversions, Velocity, and Deal Sizes

The scatterplots at the top of the Evaluating dashboard provide three critical metrics to help evaluate campaign performance: campaign conversion rates, velocity rates, and deal sizes. Exploring campaigns from these three important perspectives can yield powerful insights for an organization. 

Opportunity to Won Conversion Rate by Campaign

Let’s first consider the Opportunity to Won Conversion Rate scatterplot. This dashboard component provides an analysis of how many opportunities each campaign was responsible for (opportunity volume) and the average conversion rate (from opportunity to closed-won). Ideally, we would want all data points on this scatterplot to cluster toward the top right—where all campaigns would yield a high volume of opportunities and excellent conversion rates. Unfortunately, this is not usually reality. 

Oppty to Won Conversion Rate Scatterplot.png

Critical for exploration, this dashboard component allows for mouse-overs—a user can simply hover their cursor over one of the data points on the scatterplot to reveal underlying campaign data. Following is an example of this component's mouse-over visualization feature:

The yellow box that pops up as a result of the user's mouse-over provides metrics that are unique to each campaign. Here, the user is able to understand one particular campaign had a conversion rate of 18% and 33 opportunities influenced by it (as determined by Influential Touches determined by Influence Model 1 settings). 

This scatterplot can help marketers realize that perhaps certain campaigns are yielding a lot of opportunities—but their associated conversion rates are low. This scenario would cause a clustering of data in the top left corner. This type of insight could be very helpful. For example, could it be that there are particular campaigns creating a lot of unnecessary, ultimately ineffective work for Sales, not to mention wasted resources in the entire organization (e.g., Finance helping with proposals)? And we might want to dig even further: are there particular types of companies—or perhaps even segments or industries—where this is happening? Is there a pattern—or micro-patterns—that can be identified and explored? 

Alternatively, what if there are a lot of campaigns with really high conversion rates—but with limited volumes of opportunities? These would cluster toward the bottom right of the scatterplot. Is that a sacrifice your organization is willing to make? That might not scale very well, but perhaps it is still deemed worthwhile.

Interestingly, in the sample data provided above, there does appear to be a pattern in the data—we can essentially draw a trend line in the data. 

There appears to be a negative relationship between the volume of opportunities and conversion rate. Specifically, the more opportunities a campaign influences, the lower the conversion rate. And the inverse is true: the higher a campaign's conversion rate, we might expect a lower volume of opportunities. This could be an interesting dynamic to be further considered and explored. 

Another potential way to explore this dashboard component is to right-click on the dashboard component to open and explore the underlying report and its data. This produces the following report visualization.

This report is similar to the above scatterplots but this shows all the data by campaign type (instead of individual campaigns). We are still using volume of opportunities and conversion rates as the primary metrics dimensions. 

Interestingly, there yet again appears to be a negative relationship between the volume of opportunities and conversion rates. Might there be really important insights in your own data where you can, for example, confirm that certain campaign types tend to yield really high conversion rates but lower volumes of opportunities—and vice versa? 

Opportunity to Won Velocity Rate by Campaign

Let’s next consider the Opportunity to Won Velocity Rate scatterplot. This dashboard component provides a visual analysis of the volume of opportunities each campaign influenced and how quickly (the velocity) the opportunities closed.

Oppty to Won Velocity Rate Scatterplot.png

For this particular scatterplot we ideally want the data points to cluster toward the upper left—meaning there would be a lot of opportunities for each campaign that moved quickly through the sales process (i.e., high velocity). 

In the sample data on the above scatterplot we notice the majority of campaigns are clustered toward the bottom left. Here, the majority of campaigns influenced less than 10 opportunities and nearly all campaigns had an average velocity rate of less than 200 (days). This is not only powerful insight relative to understanding campaign performance, but it could prove valuable in demand planning exercises, too. 

We can also perform a right-click on this dashboard component to open and explore the underlying report and its data. Additionally, scroll all the way to the bottom of the report and, in the drill-down drop-down box, select 'Campaign: Type' (it's under the Campaign Fields category). This produces the following report visualization.

This report is similar to the above scatterplots but this shows all the data by campaign type (instead of individual campaigns). We are still using volume of opportunities and velocity rates as the primary metrics dimensions. 

Also, the following table of data is shown directly below the report visualization.

We are now empowered to understand how campaign types are influencing the volume and velocity of opportunities. For example, we can now have a data-driven understanding of the volume of opportunities Webinars are influencing and their velocity rate compared to Events. 

Average Deal Size by Campaign

Arguably the most important component of any marketing measurement exercise is the evaluation of revenue, so let's next consider the Average Deal Size by Campaign scatterplot. This dashboard component plots the volume of opportunities that were influenced by each campaign and the average deal size for each campaign.

Avg Deal Size Scatterplot.png

Ideally we would want the data points to cluster toward the top right—meaning there would be a high volume of opportunities for each campaign with very high average deal sizes. 

Are there campaigns in our data that are yielding strong average deal sizes and high volumes of opportunities? Those are likely the campaigns we want to continue to replicate—and perhaps even improve. 

In this particular sample data set (above) it does appear that there's almost a bell-shaped curve to the data, meaning there almost seems to be a sweet spot—or a centralized area—for campaigns, where we can expect them to yield a high volume of opportunities and high deal sizes. 

One of the more important considerations for this dashboard component (and its underlying report and data) is to be thoughtful about the volumes of opportunities associated with each campaign. We may want to avoid, for example, putting any significant meaning into campaigns and their deal sizes if only one or two opportunities are associated with them. It may be prudent to consider at least some sort of "critical mass" of opportunities associated with a campaign to consider a campaign meaningful. What determines "critical mass," however, must be determined by the organization performing the analyses. 

Note: Always be cautious in using averages for calculations, as outlier data points could skew results. Sometimes median is the better calculation to use.

For example, let's say you closed 10 deals last year and all but one (1) of the deals had a typical $10,000 deal size. And the one deal that didn't have the typical deal size had a deal size of $1,000,000. If you're using average as your calculation, your average deal size is going to be $109,000. That can be very misleading. However, if you used median as your calculation, the median deal size would be $10,000. Median is typically the more effective calculation to use when dealing with data that have the potential of being highly variable. And even when you don't use median, be sure to consider—and perhaps even make a note or mention of—outlier data points to provide important context in your calculations and reported data. 

In totality, the above three scatterplots can be used and combined to uncover powerful insights. We are now empowered to understand how campaigns are:

  • influencing opportunity volume;
  • influencing conversion rates;
  • and influencing deal sizes.

For example, we can now identify which campaigns have very high deal sizes and velocity rates—but have low conversion rates. Perhaps we create a task force to explore how those campaigns' conversion rates can be increased. Or, are there campaigns that have high conversion rates and deal sizes—but take a really long time to close (i.e., low velocity)? In those cases, perhaps we need to thoughtfully plan the timing of those campaigns so the organization does not depend on them at the most critical points during the year. 

Review of Additional 'Evaluating' Dashboard Components 

Top Campaigns by Attribution – Most Revenue (Won)

  • This dashboard component shows a list of campaigns with the most influenced won revenue, in descending order (influenced revenue determined by Influence Model 1 settings).
  • Time Parameter: All time

Top Campaigns by Attribution – Most Revenue (Lost)

  • This dashboard component shows a list of campaigns with the most influenced lost revenue, in descending order (influenced revenue determined by Influence Model 1 settings).
  • Time Parameter: All time

Top Campaigns by Attribution – Most Won Opportunities (Count)

  • This dashboard component shows a list of campaigns with the highest volume of opportunities with won revenue, in descending order.
  • Time Parameter: All time

Attribution by Campaign Type

  • This dashboard component shows the distribution of revenue by organizational department (based on campaign sources)—in other words, the amount of revenue for which each organizational department is responsible.
  • Time Parameter: All time

Evaluating – Attribution by Campaign Type.png

Average Number of Touches for Won and Lost Opportunities

  • This dashboard component shows the average quantity of influential touches for both won and lost opportunities by fiscal quarter (influential touches are determined by the settings in Influence Model 1). These data provide engagement pattern insight over time. From the example data below we can see a general pattern of increased touches for both won and lost opportunities over the past eight fiscal quarters. 
  • Time Parameter: All time by quarter

Evaluating – Avg Number of Touches for Won and Lost Opptys.png

Disposition Reasons – MQL Disposition Reasons by Campaign Type 

  • This dashboard component shows the volume of MQL disposition reasons by campaign type 
  • Time Parameter: Fiscal Year to Date

Disposition Reasons – SAL Disposition Reasons by Campaign Type 

  • This dashboard component shows the volume of SAL disposition reasons by campaign type 
  • Time Parameter: Fiscal Year to Date

SQLs by Lost Reason 

  • This dashboard component shows the volume and frequency distribution of SQL lost reasons 
  • Time Parameter: Fiscal Year to Date

Pipeline by Lost Reason 

  • This dashboard component shows the volume and frequency distribution of Pipeline lost reasons 
  • Time Parameter: Fiscal Year to Date

Touch Timing – Touch Timing by Type (Won)

  • This dashboard component shows the pattern of touches by campaign type in 30-day cohorts prior to a won opportunity. For example, for the 151-180 time cohort, a marketer can visually understand the volume of touches by campaign type relative to other campaign types during that same period. It helps answer questions such as: What types of campaigns did people interact with during this time cohort? And how does it compare to other 30-day cohorts? 
  • Time Parameter: All time

Touch Timing – Touch Timing by Type (Lost)

  • This dashboard component shows the pattern of touches by campaign type in 30-day cohorts prior to a lost opportunity. For example, for the 151-180 time cohort, a marketer can visually understand the volume of touches by campaign type relative to other campaign types during that same period. It helps answer questions such as: What types of campaigns did people interact with during this time cohort? And how does it compare to other 30-day cohorts? 
  • 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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