Posted by acarlisle
Over the past six years, our team at Fractl has studied the art of mastering
content marketing press coverage. Before moving into Agency
Operations, I on-boarded and trained over a dozen new associates
for our digital PR team within a year as the Media Relations
Manager. Scaling a team of that size in a such a short period of
time required hands-on training and a clear communication of goals
and expectations within the role — but what metrics are
indicative of success in digital PR?
As a data-driven content marketing agency, we turned to the
numbers for something a little different than our usual data-heavy
campaigns — we used our own historical data to analyze and
optimize our digital PR team’s outreach.
This post aims to provide better insight in defining measurable
variables as key performance indicators, or KPIs, for digital PR
teams and understanding the implications and relationships of those
KPIs. We’ll also go into the rationale for establishing baselines
for these KPIs, which indicate the quality, efficiency, and
efficacy of a team’s outreach efforts.
As a guide for defining success by analyzing your own metrics
for your team (digital PR or otherwise), we’ll provide the
framework for the research design, which helped us establish a
threshold for the single variable we identified to best measure our
efforts and be the most significantly correlated with the KPIs
indicative of success of a digital PR team.
Determining the key performance indicators for digital PR outreach
The influx of available data for marketers and PR professionals
to measure the impact of their work allows us to stray away from
vague metrics like “reach” and the even more vague goal of
“more publicity.” Instead, we are able to focus on the metrics
most indicative of what we’re actually trying to measure: the
effect of digital PR efforts.
We all have our theories and educated guesses about which
metrics are most important and how each are related, but without
researching further, theories remain theories (or expert opinions,
at best). Operational research allows businesses to use the
scientific method as a way to provide managers and their teams with
a quantitative basis for decision making. Operationalization is the
process of strictly defining variables to turn nebulous concepts
(in this case, the effort and success of your digital PR team) into
variables that can be measured, empirically and quantitatively.
There is one indicator identified to best measure your effort
into a campaign’s outreach. It is a precursor to all of the
indicators below: the volume of pitch emails sent for each
Because all pitches are not created equal, the indicators below
gauge which factors best define the success of outreach, such as
the quality of outreach correspondence, the efficiency of time to
secure press, the efficacy of the campaign, and media mentions
secured. Each multi-faceted metric can be described by a variety of
measurements, and all are encompassed by the independent variable
of the volume of pitch emails sent for each campaign.
Some indicators may be better measured by using more than a
single metric, so for the purposes of this post, here are the three
metrics to illustrate each of these three KPIs to offer a more
holistic picture of your team’s performance:
Pitch quality and efficacy
Placement Rate: The percentage of placements
(i.e., media mentions) secured per the number of total pitches
Interest Rate: The percentage of interested
publisher replies to pitches per the number of total pitches
Decline Rate: The percentage of declining
publisher replies to pitches per the number of total pitches
Efficiency and capacity
Total days of outreach: The number of business
days between the first and last pitch sent for a campaign, which is
the sum of the two metrics below.
Days to first placement: The number of
business days between the first pitch sent and first placement to
be published for a campaign.
Days to syndication: The number of business
days between the first placement to be published and the last pitch
to be sent for a campaign.
Placement quality and efficacy
Total Links: The total number of backlinks
linking domains of any attribution type (e.g. DoFollow,
NoFollow) for a campaign’s landing page.
Total DoFollow Links: The total number of
DoFollow backlinks from external linking domains for a campaign’s
Total Domain Authority of Links: The total
authority of all backlinks from external linking domains of any
attribution type (e.g. DoFollow, NoFollow,) for a campaign’s
Optimizing effort to yield the best KPIs
After identifying the metrics, we need to solve the next
challenge: What are the relationships between your efforts and your
KPIs? The practical application of these answers can help you
establish a threshold or range for the input metric that is
correlated with the highest KPIs. We’ll discuss that in a
After identifying metrics to analyze, define the nature of their
relationships to one another. Use a hypothesis test to verify an
effect; in this case, we’re interested to find the relationship
between pitch count and each of the metrics we defined above as
being KPIs of successful outreach. This study hypothesizes that
campaigns closed out in 70 pitches or less will have better KPIs
than campaigns closed out with over 71 pitches.
Analyzing the relationship and determining significance of the data
Next, determine if the relationship is significant; when the
relationship is stated as statistically significant, the
relationship observed has a high likelihood of happening in the
future. When it comes to claiming statistical significance, some
may assume there must be a complex formula that only seasoned
statisticians can calculate. In reality, determining statistical
significance is done via a t-test, a simple statistical test that
compares two samples to help us infer a correlation of the same
relationships in future samples.
In this case, campaigns with pitch counts below 70 are one group
and campaigns above 71 are a second group. The findings below
define the percentage difference between the means of both groups
(i.e., the campaigns from Q2 and Q3) to determine if lower pitch
counts do have a desired effect for each metric; those that are
asterisked are statistically significant, meaning there is a less
than a 5 percent chance that the observed results are due to
How our analysis can optimize your digital PR team’s efforts
In practice, the relationships between these metrics help you
establish a better standard of practice for your team’s outreach
with realistic expectations and goals. Further, the correlation
between the specified range of pitch counts and all other KPIs give
you a reliable range of what values you can expect when it comes to
the metrics for pitch quality, timelines, and campaign performance
when adhering to the range of pitches.
The original theory — that a threshold for pitch counts exists
when the relationship between pitch count and all other metrics of
performance were compared — is confirmed by the data. The sample
with lower pitch counts (less than 70) sees a positive relationship
with the KPIs we want to decrease (e.g. decline rates, total days)
and negative relationship with the KPIs we want to increase (e.g.
placement rates, link counts). The sample with higher pitch counts
(greater than 71) saw the inverse — a negative relationship with
the KPIs we want to decrease and a positive relationship with the
KPIs we want to increase. Essentially, when campaigns with less
than 70 pitches sent were isolated, the numbers improved in nearly
When this analysis is applied to each of the 74 campaigns from
Q3, you’ll see nearly consistent results, with the exception
again being Total Domain Authority. Campaigns with up to 70 pitches
are correlated with better KPIs when compared to campaigns with
over 71 pitches.
Vague or unrealistic expectations and goals will sabotage the
success of any team and any project. When it comes to the effort
put into each campaign, having objective, optimized procedures
allows your team to work smarter, not harder.
So, what does that baseline range look like, and how do you
Establishing realistic baseline metrics
A simple question helps answer what the baseline should be in
this instance: What was the average of each KPI of the campaigns
with fewer than 70 pitches?
We gathered all 70 campaigns closed out of our digital PR
team’s pipelines in the second and third quarters of 2018 with
pitch counts below 70 and determined the average of each metric.
Then, we calculated the standard deviation from the mean, which
defines the spread of the data to establish a range for each KPI
— and that became our baseline range.
Examining historical data is among the best methods for
determining realistic baselines. By gathering a broad, sizeable
sample (usually more than 30 is ideal) that represents the full
scope of projects your team works on, you can determine the average
for each metric and deviation from the average to establish a
These reliable ranges allow your digital PR team to understand
the baselines they must strive for during active outreach when in
compliance with the standard of practice for pitch counts
established from our research. Further, these baseline ranges allow
you to set more realistic goals for future performance by
increasing each range by a realistic percentage.
Deviations from that range act as indicators of potential issues
related to the quality, efficiency, or efficacy of their outreach,
with each of the metrics implying what specifically may be array.
We offer context into each of those metrics defining our three KPIs
in terms of their implications and limitations.
Understanding how each metric can influence the productivity of
your teamPitch quality and efficacy
The purpose of a pitch is to tell a compelling and succinct
story of why the campaign you’re pitching is newsworthy and fits
the beat of the individual writer you’re pitching. Help your team
succeed by enforcing tried and true best practices to enable them
to craft each pitch with personalization and compelling narratives
at the top of mind. The placements act as a conversion rate to
measure the efficacy of your team’s outreach while interests and
declines act as a combined response rate to measure the quality of
To help your team avoid the “spray and pray” mentality of
blasting out as many pitches as possible and hoping one will yield
a media mention, which ultimately jeopardizes publisher
relationships and are an inefficient use of time, focus on the
rates our teams secure responses and placements from publishers in
relation to the total volume of pitches sent. Prioritize this
interpretation of the data rather than just the individual counts
to help add context to the pitch count.
Campaigns with a high-ratio of interest and placements to
pitches from publishers imply the quality of the pitch was
sufficient, meaning it encompassed one or more of the factors known
to be important in securing press coverage. This includes, but is
not limited to, compelling and newsworthy narratives, personalized
details, and/or relevancy to the writer. In some cases, campaigns
may have a low-ratio of interest but high-ratio of placements as a
result of a nonresponse bias — the occurrence where publishers
will not respond to a pitch but will still cover the campaign in a
future article, yielding a placement. These “ghost posts” can
skew interest rates, illustrating why three metrics compose this
Campaigns with a high-ratio of declines to pitches imply the
quality of the pitch may be subpar, which signals to the associate
to re-evaluate their outreach strategy. Again, the inverse may not
always be true, as campaigns with a low ratio of declines may be a
result of non-response bias. In this case, if publishers do not
respond at all, we can either infer they did not open the email or
they opened the email and were not interested, therefore declining
While confounding variables (such as the quality of the content
itself, not just the quality of the pitch) may skew these metrics
in either direction and remain the greatest limitation,
holistically, these three metrics offer actionable insights during
Efficiency and capacity
Similarly, ranges for timeline metrics can give your associates
context of when they should be achieving milestones (i.e., the
first placement) as well as the total length of outreach. Deviating
beyond the standard timeline to secure the first placement often
indicates the outreach strategy needs re-evaluating, while
extending beyond the range for total days of outreach indicates a
campaign should be closed out soon.
Efficiency metrics help beyond advising the strategy for
outreach, informing operations from a capacity standpoint. Toggling
between tens and sometimes hundreds of active campaigns at any
given point relies on consistency for capacity — reducing
variance between the volume of campaigns entering production to
campaigns being closed out of the pipeline by staggering campaigns
based on their average duration. This allows for more robust
planning and reliable forecasting.
Awareness of the baselines for time to secure press enables you
and your team to not just plan strategies and capacities, but also
the content of your campaigns. You can ensure timely content by
allowing for sufficient time for outreach when ideating your
campaigns so the content does not become stale or outdated.
The biggest limitation of these metrics is a looming external
variable often beyond our control — the editorial calendars and
agendas of the publishers. Publishers have their own deadlines and
priorities to fill, so we can not always plan for delays in
publishing dates or worse yet, scrapping coverage altogether.
Placement quality and efficacy
Ultimately, your efforts are intended to yield placements to
gain brand awareness and voice, as well as build a diverse link
portfolio; the latter is arguably easier to quantify. Total
external links pointing to the campaign’s landing page or client
homepage along with the total Domain Authority of those links allow
you to track both the quantity and quality of links.
Higher link counts built from your placements allow you to infer
syndication networks of the placements your outreach secured,
while higher total Domain Authority measures the relative value of
those linking domains to measure quality. Along with further
specifying the types of links (specifically Dofollow links,
arguably the most valuable link type), these metrics have the
potential to forecast the impact of the campaign on the website’s
own overall authority.
Replicating our analysis to optimize your team’s press coverage
Often times, historical research designs such as this one can
have limitations in their cause and effect implications. This
collection of data offers valuable insight into correlations to
help us infer patterns and trends.
Our analysis utilized historical data representative of our
entire agency in terms of scope of clients, campaign types, and
associates, strengthening internal validity. So while the specific
baseline metrics are tailored to our team, the framework we offer
for establishing those baselines is transferable to any team.
Apply these methods with your digital PR team to help define
KPIs, establish baselines, and test your own theories:
- Track the ten metrics that compose the KPIs of digital PR
outreach for each campaign or initiative to keep a running
- Determine the average spread via the mean and standard
deviation for each metric from a sizeable, representative sample of
campaigns to establish your team’s baseline metrics.
- Test any theories of trends in your team’s effort (i.e.,
pitch counts) in relation to KPIs with a simple hypothesis test to
optimize your team and resources.
How does your team approach defining the most important metrics
and establishing baseline ranges? How do you approach optimizing
those efforts to yield the best press coverage? Uncovering these
answers will help your team synergize more effectively and
establish productive foundations for future outreach efforts.
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