Jacky Ye

Prior to joining Adobe, I worked as an economic consultant specializing in antitrust and competition. Like most consultants joining fresh out of undergrad, I saw consulting as a stepping stone and a great opportunity to learn a lot while figuring out what I wanted to do long term. At the time I was debating three exit ops: (1) get an MBA, (2) get a PhD in economics and become a professor, or (3) work in tech. After working for two years, the third option appealed to me most because it represented the most choice, and in some ways, almost a round two of consulting - a great place to learn a ton of useful skills while figuring out what I enjoy long term. In the absence of clarity, pick a direction that seems reasonable and figure it out along the way. That's advice a lot of mentors have given me. Most of us don't know what we "really" want to do, so prioritize learning and give yourself the best shot possible to keep growing. That's what attracted me the most about my current role. Although Adobe is an old organization (as far as tech companies go), the team I'm in is relatively new and growing quickly. There's a ton of whitespace, a ton of new challenges, and consequently a lot of opportunities to grow. That said, it's not all work, all hustle, all the time. Within tech, Adobe has a strong reputation for promoting work-life balance and in my experience, that's been true. As an example, Adobe has company wide week-long shutdowns in the summer and winter, true time off to unplug where since everyone's offline, there's no pressure to still work/check emails. Put those things together and this role felt like the perfect launchpad into the next stage of my career, balancing both career growth and personal well-being. I loved how one Exponent article put it, "One of the best parts of a BizOps role is that you can be like a chameleon—morphing and changing to address the most critical needs of a business. [...] You get enormous visibility into how a company thinks from the top down. How each team sets priorities can help you see how a business is growing and preparing for the future." On vision, the one-liner is to be true strategic partners rather than simply data aggregators. Internally, we call the latter function "reporting the news" where traditionally, ops has been focused on running day-to-day operations - things like measuring and reporting pipeline, tracking deals, etc. These are are vital functions and necessary for running the business, but the vision is to go one step further and think of ways to also change the business. It's the difference between just reporting pipeline coverage vs. coming up with initiatives to improve pipeline coverage (by increasing generation, conversion, quality, etc.). -------------------------------------------------------------------------------- *Footnote: on why an MBA and a PhD didn't make sense for me after consulting * On (1), I felt that getting an MBA felt pre-mature without first working in a different industry and getting broader functional exposure. I also didn't have a clear idea of what I would be doing the MBA for and didn't have a strong vision of what I wanted to do afterwards. It simply felt like something I "should" do. I know many people who have gone straight into their MBA after consulting and done very well, but it didn't feel right for me. * On (2), I don't know that I've completely thrown out the idea of becoming a professor. I've always loved teaching/mentoring and there's something quite appealing about living out in a college town and being able to teach and study and research things you love. But I'm not convinced I love economics (or any subject frankly) enough to devote 5 years of grad school and the rest of my immediate future to it.

One of my managers is fond of saying that to succeed in this role, there's only two things you really need to know how to do - analyze data and tell a good story. There's a lot of detail we could unpack but when you distill it down, it comes down to whether or not you can take raw data - quantitative and qualitative - and write a compelling story about it to influence decision-making. In "business" speak, it's knowing how to mine insights, investigate hypotheses, model scenarios, and synthesize all of above into succinct strategic recommendations. In the process, there's a lot of ambiguity and complexity that requires sensible assumption making and structured reasoning. Consequently, the interview process we have and that I went through tests for the above. There's the standard behavioral component that evaluates for interest in the role, background experience, and general "fit" and then there's a case component that mirrors a real project we would work on, involving data analysis and a presentation to pitch your proposal in front of a panel. * On the technical side, the interview assesses whether someone has the technical chops to analyze data under time pressure and put a deck to tell a cohesive narrative about it. * On the softer side, the interview also assesses things like whether a candidate knows how to read an audience, speak at the right altitude and level of detail, respond to feedback, and challenge their thinking further.

I think it comes down to a few things and I don't think this is specific to revenue ops. I borrow this concept from one of my favorite Youtubers, Ali Abdaal, who notes in his upcoming book that meaningful work boils down to three P's - power, play, and people. And if you have a team or an organization that does all three well, you'll have a good chance of retaining people. * Power is about autonomy and progress, the feeling that people have control over the work that they do. It also encapsulates a feeling of progress, and having a destination to work towards. Do people feel like they have a path forward? Do they have autonomy? Being able to say "Yes" to both is necessary to retaining (and hiring) talent. * Play is simple - it's enjoyment. It's the idea that doing the thing, the work in and of itself, is joyful and fun. Do they enjoy the work? Are they being challenged enough? Are they bored? * People is many things, but fundamentally I think it's about teams. I picture this as an inner circle and an outer circle. The inner circle is the immediate team that someone is in, their manager, their direct coworkers, the people they interact with on a daily basis. Is there a sense of connection with the team? Do people feel like they have to compete with one another? How are ideas discussed? How are they criticized? It's often said that people leave managers (and teams), not companies, and that's what the inner circle is about. The outer circle is the broader organization and company. It's less relevant than the inner circle, but still important. A strong sense of connection the company's mission is a indicator of retention.

At a high level, there's generally two buckets of metrics that Sales/Rev Ops teams look at that are fundamental to understanding the health of the business: revenue generation and revenue retention. 1. Within revenue generation, the main three categories of KPIs are pipeline execution/progression, pipeline generation, and pipeline coverage. * Pipeline progression and execution is about how much of the existing pipeline the sales team has progressed (matured from one stage to the next) or closed (booked $'s). Focus is on current quarter. Typical KPIs are $'s booked, the amount of pipeline in various stages of maturity, and close ratios. For $'s booked, the two main levers we evaluate are $ attainment vs. target and Y/Y growth. Both are important because they measure different things. Targets measure expectations. Y/Y growth measures pace. You can have a product growing 100% Y/Y (fast pace) but it could still be short of targets because expectations are even higher. * Pipeline generation and creation is about how much pipeline is being created. Focus is on future quarters, with longer timeframe ranging from Q+1 to Q+4 or even Q+6 depending on length of the sales cycle. Typical dimensions that pipeline generation are tracked against are: * Creator type - who's sourcing the pipeline? Is it a BDR, an account executive, etc.? This matters because oftentimes, different sources of pipeline will have different rates of conversion. * Pipeline composition - what's the product mix of the pipeline? What's the deal type mix? Is it a lot of upsell? Cross-sell? Are we building enough pipeline across the portfolio? * Close quarter - when is this pipeline expected to close? Are we building a lot of pipeline for the near-term and medium-term future? * Pipeline coverage is the amount of pipeline in flight. Focus is typically on immediate next quarter. The KPIs ops and sales will focus on are coverage ratios - the ratio of pipeline in a given timeframe vs. the sales targets for that timeframe. E.g., if we have $100m of pipeline against a $25m target for Q2, then we'd have a 4x ratio. This is typically the main early indicator of future success, but there are many other ways you could investigate this, including looking at mature vs. total pipe coverage. Looking at historical conversion rates, and in-quarter create and close rates, etc. to understand what is "sufficient" coverage. 2. Within retention, the main categories of KPIs are attrition, renewals, and usage/adoption. 1. Attrition - this is a fun one. How much revenue did you lose from existing customers. Typically this is measured against targets. Very straightforward to understand and typically broken up into full attritions and down sells. 2. Renewal Rates - very similar but rather than absolute $'s lost you look at %'s. Two flavors of this that I've typically seen. Renewal rates against RBOB (renewal book of business) and Retention rates against BOB (book of business). 1. RBOB Renewal % - of the $'s that were up for renewal this year (or quarter), how much of it did you renew? 2. BOB Retention % - of your total annual recurring revenue, how much of it did you retain? 3. Product Usage/Adoption - are customers getting the most out of their existing products? What's their usage % (# licenses utilized, # activations, etc..)?

The worst KPIs I've seen are those that encourage the wrong behavior or provide a false sense of security that the business is doing better than it really is. Take pipeline coverage, for instance. Pipeline coverage is a fundamental KPI. There's no sales ops team in the world that doesn't measure pipeline. But the best teams know that when identifying the right KPIs, quality is just as important as quantity. Having a large quantity of pipeline means nothing if there's no thought behind how the quality of that pipeline is assessed. Sure, it might be great on paper to have a 5x coverage or a 10x coverage against next quarter's target, but how do you know you can "trust" that pipeline? The KPI is only meaningful if you have confidence that it is real, i.e., if you have systems to pressure test and accurately benchmark that metric.

I'll be the first to say it. It's really hard. There's no perfect answers and attempts to quantify this kind of uncertainty can sometimes like a reach, and at worst, feel like complete BS. But this is a really important question. How do you determine what "good" looks like when you're doing something you haven't done before? 1. I think the first step is admitting that there's no single "right" answer. If you let go of thinking that there's one perfect solution, you allow yourself to move past unrealistic expectations and get started on the work. You have to be ok with knowing it's going to be directional. But having a direction is way better than not knowing which way to go at all. As the saying often goes, done is better than perfect. There's a good chance that your KPIs will evolve as your thinking matures and you learn more about the market you're entering. 2. The second step is to really understand the art of assumption making. Realistic goals start with realistic assumptions. So if your assumptions are reasonable, chances are your goals will be too. Now, what's considered reasonable might be very different for different people, but remind yourself that goal here is to narrow down your range of possible outcomes into something that feels actionable. 3. The next step is to determine whether a "tops-down" or a "bottoms-up" analysis makes more sense. For the sake of example, let's say the KPI you've decided to focus on is # of new customers to acquire. 1. One top-down analysis could start with market sizing, where you quantify the TAM (total addressable market) based on population, your target demographic, and other salient and available data points, then identify, based on that TAM, what % would be reasonable to target. Your final answer might look something like: "We should target acquiring X # of customers which represents Y% of the TAM." 2. A bottoms-up analysis might involve looking at internal data and seeing if there have been similar historical precedents. What's been the customer acquisition rate from other markets the company has entered? Do these markets offer similar-enough comparison points? If it's not a straightforward 1:1 comparison, is there some type of aggregation you could do? 4. The final step, and arguably the most important, is to simply get started, learn, and iterate. Once you've acknowledge that the KPI is there to set the direction, the real work begins when you start moving in that direction. You'll quickly get a sense of whether your assumptions were right, or whether they need to be updated. The final note I'll add to all this - and this is sometimes the dose of realism that we get dealt - is, sometimes the goal is not to be realistic. Sometimes, the question is "what's required?", not "what's realistic?". If the CEO challenges you to grow revenue by X% or if you need to hit X target in order to keep the business afloat, you have no other choice but to figure out how to make it work. Humans are curious, in that way. We're capable of doing things that we can't even imagine yet or that we think are impossible.