My favorite question to ask during an interview is “If you could spend an extra
$100k on demand generation in your current role, what would you spend it on and
why?”
I really like this question because it helps me understand a variety of things
about how the candidate thinks. Dependent on the response, I can learn which
type of marketing campaigns they tend to prefer to work with and how they budget
and plan.
The answer I look for is typically something with a multi-channel approach.
Throughout my career I have learned to trust the saying “don’t put all your eggs
in one basket.” That has been even more true after the last 2 years of the
pandemic. The entire way we do Demand Generation or Field Marketing shifted with
the removal of in person events combined with the rising cost of digital
marketing. Now as we are shifting back to in person, we face the battle of
inflation overall. Multi-channel gives you a lot of at-bats and allows you to
shift funding around as needed.
Marketing Operations
This could be tech stack related questions, but also more general demand gen questions.
3 answers
Director of Demand Generation and Channel Marketing - Americas, Snow Software • October 22
Head of Growth Marketing, Observable | Formerly Issuu, OpenText, Webroot • November 4
My favorite demand generation interview question is around how do you measure
success. I tailor the question based on the role, but the essence of the
question is the same.
This is my favorite question because I’m a believer of being data informed
within demand generation. Right away I can gauge the sophistication of the
candidate's experience based on this question. I’m looking for an understanding
of how to gauge performance and the rigor of data analysis you’ll have with your
programs. This of course will vary based on the role.
What was the best answer I’ve received? When the candidate teaches me something
I’m not familiar with. Educate the hiring manager on a nugget of information or
a unique perspective and that will give them insight into how much you can bring
to the table.
Head of Digital Marketing, Demandbase • January 24
Walk me through your favorite campaign.
I love this question but it's pretty open-ended and can reveal a lot about the
way in which a candidate thinks. Did they understand their audience? Did they
design their program in a way to achieve the goals? Did it even have established
goals? Was in creative in the sense that it ultimately achieved what it set out
to achieve?
It enables a candidate to demonstrate how they think about strategy, planning,
and execution without leading the witness!
2 answers
Head of Digital Demand Generation & ABM, Front | Formerly a child • July 8
Yes. It's nothing out of this world -- typical UTM parameters pass all the way
through to the contact record, which, when an opportunity is created on the same
account, passes through to the opportunity record, which we follow all the way
down to closed-won. We then look at LTV:CAC down to the utm_term level to see
which paid search terms drove actual revenue/ARR. I do this for all paid
initiatives.
For non-paid initiatives, we tie it to the "journey" or asset level. What
original touch point got the user to convert on our website? From that initial
touch point, what additional marketing/sales touches led to a meeting? We follow
those all the way through to revenue.
Head of Growth Marketing, Observable | Formerly Issuu, OpenText, Webroot • January 20
The short answer is generally, yes. There is some nuance to this question
though. I currently work at a startup that offers a data collaboration tool, so
this is easier to manage than I’ve typically experienced in my career.
UTMs and tracking pixels can get you to revenue and CAC. However, depending on
the type of campaign you are running, this may not always be possible. If you
are using a channel like Google Ads, this is very easy to do. In some instances,
you may have to measure performance based on educated assumptions and/or lift
measurement.
2 answers
Global Head of Demand Generation, Morningstar | Formerly Ariba, Taleo, Showpad • July 20
Perfection should never be the priority. "Don’t let 'perfect' be the enemy of
'good.” Formulate some hypotheses, strive for your best, and then always plan to
iterate. The more data you collect from how your customer interacts with a
campaign the better your campaign can become with optimization over time.
Head of Growth Marketing, Observable | Formerly Issuu, OpenText, Webroot • January 16
I love this question. Oftentimes the question is what you should focus on, but
what we should not focus on is equally important. I recommend not focusing on:
* Perfection. Perfection is probably one of your biggest enemies. This is both
professionally and personally.
* Growth at all costs. Gone are the days of “growth at all costs”. We need to
be smarter and be more results driven.
* Volume without quality. Similar to the concept of “growth at all costs”,
volume without quality can put a company out of business nowadays.
* No tracking, no campaign. Should you not launch a campaign if full tracking
is not in place? How do you leave room for creativity while still being
responsible with the budget?
2 answers
Head of Digital Demand Generation & ABM, Front | Formerly a child • July 8
I break out the DG metrics into two groups:
1. Demand generation
Here I focused on generating demand through "push" campaigns to our ICP
accounts. I call this "push" because I am essentially shoving our ads in front
of folks who never asked for it. I look at things like ad impressions, CTR,
engagement rates, and conversions. From an ABM perspective, I look at the same
ad metrics, but I also look at account view-through rates (the % of accounts
who've seen my ads and then visit our website by other means), intent signal
increases, and funnel progression. Holistically, I want to ensure we are driving
the right level of engagement to accounts on our target account list. Within our
target account list, I want to make sure, period over period, I am creating
awareness & demand at the same accounts our outbound teams are working so they
have a higher rate of turning emails and calls into conversations.
2. Demand capture
Here I focus on capturing demand through our "pull" campaigns to audiences that
already have existing demand for our solutions. These are paid search, review
websites, and content syndication with intent signals. The metrics I look for
here are pure lead capture and lead nurture into MQLs that I can hand over to
our sales teams to work.
I review these metrics daily, but only when I see anomalies that last more than
3 days do I actually take action.
On a weekly, monthly, and quarterly basis, we review the demand gen machine by
channel (paid search, paid social, organic, direct, etc.), then break it out by
campaign, down to the utm_term level and ultimately follow these down to revenue
and LTV:CAC. The intent here is to analyze the numbers to surface insights and
propose action items for the month or quarter.
Head of Growth Marketing, Observable | Formerly Issuu, OpenText, Webroot • January 15
My recommendation is to review your KPIs on a daily basis with a dashboard that
is intended to give you a quick snapshot on how programs are trending. You’ll
want to do a deep dive on a monthly, quarterly and annual basis. Depending on
the program, you may have a different review cadence as well (e.g., bi-weekly).
I prefer to tailor my metrics based on the goal for the tactic or program. What
is the desired outcome? How do we measure towards this goal? This will help
dictate the metrics for success. Naturally all of our efforts should be tracking
towards generating revenue, but depending on the tactic, the metric might be
conversion rate, sign-ups, pipeline, etc. This is also highly dependent on the
go-to-marketing strategy. Is this a product-led growth (PLG) org or a sales led
org? The metrics for different GTM motions will differ.
For example, I work for an org with a PLG motion. Here metrics such as
retention, engagement and quality sign-ups matter. This is very different from
MQLs and SQLs. If I had to sum this up in one sentence, I would recommend that
you think through how each metric ties back to revenue (including down to
metrics such as impressions).
2 answers
Global Head of Demand Generation, Morningstar | Formerly Ariba, Taleo, Showpad • July 20
Campaign operations and demand generation are part of the same value chain of
campaign creation, so it depends on the size and scale of your teams as well as
the specialization you chose to prioritize. Often in startups, you have demand
gen and ops in one person, as the org scales, you begin to introduce
specialization. On the other end of the spectrum, I’ve seen roles and
responsibilities of ops, in particular, go as far as to be divided into 6
different specializations. For our team at Morningstar and consistent with my
experience at other organizations we have the campaign architecture and channel
selection live in demand generation and the actual build and measurement lives
in operations.
Head of Growth Marketing, Observable | Formerly Issuu, OpenText, Webroot • January 14
From my experience, campaign operations fall under marketing operations except
with smaller teams such as startups. For example, I work at a startup and drive
the majority of the campaign operations as they are needed for my programs.
Since I have experience in this it actually works in my favor.
For larger organizations, I’ve worked with MarOps teams where they define and
execute what is needed and I am just informed. In this scenario, it allows
campaign operations to be centralized and uniform for reporting.
So from my perspective, this is determined by the size of the company and the
capabilities of the individual.
2 answers
Head of Digital Demand Generation & ABM, Front | Formerly a child • July 14
I would consider data and metrics to be synonymous in most cases, however, if I
had to define it individually, I'd say data is the raw format of all the little
quantitative metrics we are collecting on an individual, company, or campaign.
Metrics are a bit more aligned to the quantitative data that I want to see
because it's important for the analysis. For example, raw data might contain
page views, sessions, new users, returning users, conversions, cost per
conversion, and conversion value. The "metrics" I care about when looking at a
website's performance is page views, sessions, new users, and conversions.
Conversion value is more of a metric to look at the ROI or ROAS of a campaign.
Basically -- data is the raw dump of quantitative metrics as a whole. Metrics
are what I filter for to do a proper analysis.
This brings me to analytics. To me, analytics is the effect of pulling the raw
data, sifting through the core metrics, and analyzing the quantitative metrics
to uncover actionable insights. Analytics, to me, is translating quantitative
metrics into a qualitative story/narrative.
Head of Growth Marketing, Observable | Formerly Issuu, OpenText, Webroot • January 13
I see the definition for data, metrics and analytics as different although
related.
For me, data is the raw information. It’s probably messy and isn’t very
actionable. It's most likely arranged into columns and rows.
Metrics are measurements layered on top of the data or raw information. This is
the first attempt to make sense of the data.
Lastly, analytics is the process of coming to insights based on the metrics and
data. For example, Google Analytics attempts to take raw data, organized into
metrics (e.g., time on page) and is displayed in a way that intends to be
actionable or at a minimum insightful.
2 answers
Head of Digital Demand Generation & ABM, Front | Formerly a child • July 19
I believe a successful structure requires a robust data analyst on both
Marketing and RevOps. The RevOps analyst is very important because they decide,
based on the company's revenue goals, how many various SFDC stages are needed to
hit that goal. They understand the stages and their progression rates (i.e.,
Stage 1 to Stage 2 rate, Stage 3 to close) to properly define how many opps are
needed to hit their goals. They are too far removed from the marketing
operations and marketing campaigns to determine how marketing can properly
impact the growth and efficiency of these metrics.
This is where the marketing analyst comes in. Based on the RevOps' assessment of
revenue goals, they have determined the # of sales accepted opportunities, sales
qualified leads, marketing qualified leads, and marketing engaged leads are
required to hit our goals. The marketing analyst now comes in to properly
understand the marketing funnel stage progression rates and the various
campaigns that are running to give the numbers a bit of a massage to say, "based
on our marketing spend and cost per lead, we will need $X,XXX to hit our goals."
They can also understand the seasonality of marketing campaigns and determine
when to set goals higher or lower through the year. They will also know that if
paid search leads come in at $50 a pop, you can just give marketing $50m to
generate 5m leads... there's the knowledge of diminishing returns, total
addressable market, and efficiency scale that a marketing analyst that's closer
to marketing would know.
They then work together to come up with the numbers and tell their story to the
sales and marketing leaders, who can both agree, based on their push, pull, and
ABM strategies, that they are comfortable with the goal, and a partnership is
formed.
Head of Growth Marketing, Observable | Formerly Issuu, OpenText, Webroot • January 6
I’m approaching this question from the lens of being an SMB organization. This
can of course vary between startups, SMBs and enterprises. I’ve seen the typical
SMB org structure consist of the following teams to cover data and analytics.
* Revenue operations: Overarching revenue strategy to ladder up to company
goals.
* Marketing operations: Ensures that the required tracking is in place to
accurately and efficiently measure data.
* Business intelligence: This is the team where I’ve seen the most variance in
skills depending on the org. This may consist of a mix of data analysts, data
scientists, dataviz developers, marketing analysts, SQL specialists, etc.
2 answers
Head of Digital Demand Generation & ABM, Front | Formerly a child • July 19
I think strategies like ABM are decided upon when you identify a strong, core
ICP that you can trust both quantitatively and qualitatively. ABM requires a
solid ICP to understand who to go after but strong qualitative feedback from
sales leadership validates and supports that ICP. When you have that ICP and
both sales & marketing believe in that profile, you decide that your tier 1 and
2 accounts are the only accounts that meet the ICP.
Some things that I'd look at to determine ICP (which would lead to ABM
strategies) include average contract value, deal velocity, and LTV. I would take
those metrics and start to extract firmographic parameters to determine what
makes up the characteristics of accounts with high ACV, the fastest deal
velocity, and that have strong LTV. I'd take those characteristics and start
looking at ABM strategies to only go after accounts that meet these
characteristics since these will give me the most bang for my buck.
As far as PLG goes, I believe that any/all software companies have an
opportunity to build a little side PLG model that you can decide to blow up into
a full-fledged program. Who you start with is what matters most. Some of the
metrics I'd look at are your product's power users, sign-up to activation, and
high-growth accounts. Once again -- take the characteristics of those accounts
and determine who set of personas are prime for a self-serve model. From there,
you start to analyze every step of their journey within the product. You look at
metrics in-product such as # of sign-ins, pages with the highest session
duration, the most amount of clicks, and exit pages. You then start to remove
any/all friction points on these pages while simultaneously pouring more people
into this self-serve/PLG funnel to get more data and identify more friction
points to remove. Over time, you will have a well-oiled self-serve machine where
users are going through the sales journey themselves and eventually helping each
other out through the building of communities and forums.
Head of Growth Marketing, Observable | Formerly Issuu, OpenText, Webroot • January 4
From my perspective, ABM and PLG are two different approaches and while the
associated metrics will have some overlap, they will largely differ. There is a
complementary framework from a product-led sales and ABM perspective, but that’s
a separate topic.
Example metrics within the context of this question:
ABM metrics:
* Marketing qualified leads (MQLs)
* Conversion rate
* Customer acquisition cost (CAC)
* Target accounts in pipeline
* Average deal size
PLG metrics:
* Product-driven revenue (PDR)
* Time to value
* Churn
* Product qualified leads (PQLs)
* Average revenue per user (ARPU)
This list is NOT exhaustive. Depending on your business model and go-to-market
strategy, you can then define your marketing plan and framework, and the metrics
that will drive the desired outcomes.
Related:
https://sharebird.com/h/demand-generation/q/how-do-you-break-down-responsibilities-and-kpis-between-demand-gen-and-product-marketing
1 answer
Global Head of Demand Generation, Morningstar | Formerly Ariba, Taleo, Showpad • July 20
First one would be a sound campaign planning process that starts with the
outcome you are trying to achieve, who your target audience is, and an
understanding of where they "live" i.e. where they spend time with their peers
learning about new solutions. Second would be an airtight Q/A process that
includes more than one set of eyes. The campaign planning process, if done well,
should include time for the Q/A process.
1 answer
Global Head of Demand Generation, Morningstar | Formerly Ariba, Taleo, Showpad • July 20
This question is specific to scaling a business. No one size fits all answer,
what you choose to centralize or decentralize has a lot to do with what growth
stage you are in and your priorities. In the case of Morningstar, we have
central marketing functions (like brand, PR, demand, and customer marketing) and
marketing functions like product marketing that live in a decentral business
unit. Decentral models allow for agility and focus and the central functions
play a role in consistency and amplification. I do find it particularly useful
to centralize a function if the discipline does not exist to drive consistent
KPIs and processes. For more on this topic check out the great blog post from
First Round Review on “Give away your Legos and other commandments for scaling
startups.”