Marketing Operations

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.

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.

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!

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.

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.

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.

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?

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.

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).

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.

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.

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. 

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.

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. 

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.

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. 

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

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.

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.”