Interview with Adam Daigian, VP Growth & Revenue Operations, Hustle

10 min read

Adam Daigian started his career amidst the growth hacker hype, and has been in the growth niche ever since. I was curious to see how Adam utilises his growth hacker mentality in his current role as VP Growth & Rev Ops at Hustle. Adam shared some growth hacker gems with me, from viewing your sales team as a customer and monitoring their internal NPS to measuring ‘altitude’ metrics. After chatting about Adam’s diverse career, the interview began…

Rory Brown (RB): Can you tell us about Adam Daigian and your career to date?

Adam Daigian (AD): Absolutely. When I entered the professional market, it was the beginning stages of growth emerging as a discipline. This is when Growth hacker became a buzzword and bigger companies were building entire growth teams. I had a diverse background with different hands-on skills: marketing, product, analysis, design etc and so I naturally fell into growth as discipline. At the end of the day, I was always drawn towards the various levers that companies have at their disposal to drive growth, whether that be marketing, sales, product, data or technology. And I certainly never skimped on the technology side and taught myself how to code.

So that really propelled me into this growth niche and I’ve been working as a growth leader in a cross-functional capacity focused on driving revenue ever since.

RB: Let’s talk about sales and marketing and the passing of leads between the two functions. What have you seen in terms of good strategies or processes which have ensured that the data flow continues and therefore the business gets the visibility it needs?

AD: Data is arguably the most powerful tool that startups have at their disposal today to inform strategy and make the right decisions.  

There is this steady stream of data throughout the customer lifecycle. What happens in the very first interaction between a company and a prospect can ultimately inform what happens in the post-sales environment.

I think it is important that rev ops either own, or have a really strong partnership, with the BI function, certainly as it relates to to-to-market. But first and foremost, you have got to be thoughtful about your KPIs. Every function within a revenue organization needs to have a set of KPIs that tie the organisation together. 

I think about defining and building out metrics in a couple of different ways. One is sort of a timeline perspective and that is the metrics to look at daily, weekly, monthly, quarterly and yearly. Those are important because they’re your pulse check.

Then I think about metrics from an altitude perspective, or a dashboard tree, and that is L0 to L3. L0 being the executive view, L1 being the VP and Director level view, L2 being the director to manager level view, and L3 being manager to IC level view. These are the metrics that help you run your business at all levels.

Every function within a revenue organisation, marketing, inside sales, sales, post-sales ought to have these dashboards with metrics that tie the client lifecycle together and give full visibility at all levels.

An under-played opportunity for metrics that drive continuous growth are the hand-off metrics.

At Hustle, we started scoring opportunities. We use BANT at the opportunity creation stage which is what you would typically consider a sales qualified opportunity. Inside Sales would give opportunities a BANT score of 1 – 5 and that would allow us to calibrate in the handoff and down the opportunity funnel. When an SDR consistently gave an opportunity a score of 5 and an AE would give it a score of 3 you know you have work to do around calibrating opportunity qualification. 

So I like to think about metrics in these three different categories. And then I’d add a fourth category which is ongoing insights from various funnels that forecasting and continued revenue growth, such as timeline metrics or product usage insights that can inform expansion opportunities.

The more data that you have at your disposal the better, as long as it’s well structured.

RB: So, let’s dig into one or two of these four areas then. Let’s start with time-based metrics. What might be some examples you could give of the time-based metrics that you are looking at, the pulse-based metrics, and why they are so important?

AD: One of my favourite time-based metrics is what I call the SAO frontier. This would be the average timeframe between sales qualified lead and sales accepted opportunity. What is the average timeline for accepting sales qualified leads into the opportunity funnel?  What does that curve, or frontier, look like? If I understand that curve well, then for any given time-frame outside of a cohort of opportunities, say one month out from a cohort, I know what percentage of my pipeline has matured for that particular cohort and how much additional pipeline value I should expect to be added into the opportunity funnel as that cohort continues to mature. As an example, if I know that a cohort is 80% mature one month out, almost everything that is going to become a sales accepted opportunity will have become a sales accepted opportunity already and I can expect 20% of additional pipeline value to be added over time.

Looking at those metrics  – within the opportunity funnel too – is extremely important for ongoing forecasting. From a forecasting perspective, understanding what percentage of deals you can expect to have been won within a month out, two months out, three months out time frame, gives you much stronger fidelity around forecasting, certainly for the SMB and mid-market segments which typically don’t have exceptionally long sales cycles.

RB: So you are saying if we can work out on average the time it takes to go a marketing qualified lead to an opportunity and the percentage, we can start to give the business visibility of what is likely to happen because we know all the metrics after that point.

AD: Yes, absolutely. 

RB: From a high-level perspective, how do you ensure that there is alignment between sales and marketing? And then in the actual heat of the battle itself, what could be done to make that process a little bit more seamless?

AD: I think the problem typically emerges when people don’t take the time to communicate and set expectations appropriately. The great thing about SaaS startups and acquisition is that there are not a lot of mysteries out there anymore. We are really in the optimisation phase when it comes to customer acquisition for SaaS. The playbooks are well documented and what you have to do is focus on truly executing.

It’s important to really align on definitions. For example, what should an MQL really look like? It can’t just be a generic definition. Then based on that definition, what is the appropriate conversion rate from MQL to SQL? Generate an SLA between the marketing organization and the inside sales organization around what that conversion rate ought to be

As a marketer, I have found myself generating what looks like a bunch of great pipeline but then close rate is very poor. Then at the end of the day, we both end up looking bad. Nothing is clear and you end up in this finger-pointing game. Maybe that pipeline wasn’t so great after all. But how do we know if we don’t have a tightly defined ICP that defines what an MQL should be?

RB: That is very nicely put. What has your experience of SLAs been? What do you normally see in these types of agreements?

AD: Well, it is tough. There are pretty common SLAs which include commitments to touch a lead in a certain timeframe. Then there are tougher SLAs which can involve committing to a conversion rate. 

In my experience, it is really important that across the revenue function the different leaders are aligned on what ‘good’ looks like or what ‘excellent’ looks like. If you don’t make these kinds of agreements, you don’t have the clarity around the metrics, then it’s not clear to the leadership, or even the team, what’s really going on with the acquisition funnel.

RB: Nice. The next bit I really want to dig into is what you describe as Altitude and L0 to L3. Perhaps you could break that down a little bit for us.

AD: Yes, sure. The way I look at it is, executives need a birds eye view of how functions are performing across the company and how that impacts the businesses’ unit economics. This is what I would call the L0 view. But then they also care about the details of the functions that roll up into them. This is what I would consider the L1 view.

The L1 view is for the Director to VP who cares about how their function is doing, down to the individual teams or initiatives.  L1 level needs to be able to give end to end performance visibility within a function and how it’s performance impacts adjacent organizations. It needs to be flexible enough so that it can be leveraged for digging in with executives down to demonstrating to team members how their function is doing and how their work impacts those metrics. And example of an L1 dashboard would be Sales or Marketing as a function.

The L2 level is really about team management, for anyone from a VP down to the manager level. An example of that dashboard might be Inside Sales for instance. This dashboard would be the place that a Director of Inside Sales would go to see the performance of the inside sales organization from a holistic perspective. It’s where you’d find metrics like SAO frontier. If SAO frontier was extremely critical at the Sales function level, you might find it on that dashboard as well, but maybe at the L2 level it’s decomposed by market for instance.

Lastly L3 dashboards are typically at the manager or initiative level. In the case of Hustle where an events was a primary channel and was allocated a healthy portion of my marketing budget, they definitely had an L3 dashboard. And that way they can understand how they are doing on a monthly basis, how are they doing with our campaigns running and how are the leads they’re driving flowing across the Marketing and Sales funnels.

I can’t take credit for creating this structure of dashboard levels. It was created by a colleague of mine, Abhi Sivasailam, who I’ve worked closely with on growth and data initiatives for a long time. He created this paradigm. I’ve adopted and implemented it and it works exceptionally well.

RB: You talk about pro-actively producing insights which people might not necessarily have requested. Where do you start and how do you know where to focus?

AD: That is a really good question. Typically, I think the best way to do it is to drill down into a segment, sort of a neatly defined funnel – a lead funnel or an opportunity funnel or a product funnel – and start with a set of business questions. For leads for instance: Are we working the right volume of prospects across the right markets? How do different tiers – like company tiers or markets like SMB or mid-market or enterprise – perform across the entire funnel when we decompose thos funnel? Because sometimes drilling into funnels at that level can actually show nuances that you just don’t see when you are looking at Lead to Close or even MQL to close – or even SAO to close.

It is about picking a funnel and thinking about what are the top five, ten, fifteen business questions I have about the funnel that aren’t necessarily captured in my Go To dashboards (unless you do exploratory analysis on top of those dashboards, which you certainly can do). 

In a way, the analysis is the easy part. The hard part is actually packaging that information up and distributing it across the team in a way that they can act on it, at the individual level.

RB: While we are on that, insights is a word we hear all the time. Insights are promised left, right and centre. But the process of turning those insights to action is where some businesses fall short. 

What have you seen there in terms of ways that you can present insights, or maybe a structure, that you use that would encourage that process of turning insights to action?

AD: That is another really good question. It is really a journey. And that journey starts with the question – how data mature is my organization? And the answer is usually not very. 

So first things first, you have to work towards making your organisation more data mature. Then you need to find ways to incentivise the right behaviour. One of the ways that I like to do that is to change commission plans and commission structures. The base structure should be changed as infrequently as possible. I really don’t want to be changing commission on anything besides an annual basis, especially in an organization where sales cycles can be that long.

But what I do like to do is change commission spiffs quarter over quarter to incentivise behavior changes like velocity or concentration on a certain ICP. I think that is a great way to actually get the insights that you have driven to be acted on. Last, but certainly not least, is to really acknowledge people for closing the loop: An insight was generated, this person acted on this insight, this was the result, and congratulations to them.

RB: Really great stuff, thank you for that. In your experience, how do product and rev ops support each other?

AD: I think it really depends on the organisation that you are in. 

I’ve worked for companies who have had both a sales motion and a free trial or self-onboarding motion and those are very blurry lines between the sales motion and the product motion.

So it is important that you have an exceptionally tight handshake between your product and Salesforce (or whichever CRM you are using). And that is just in the pre-sales realm. In the post-sales realm, it is equally important that your product is driving insights to the post-sales team.

This customer has exceeded their limit; this customer is well below their limit. You can’t really have a customer health score without ingesting product usage data.

There has to be a foundational handshake between your revenue ops systems and your product, and that usually looks like a direct integration to Salesforce plus consumption of data, probably data from your data warehouse.  

That’s one way I see it. The other way is really about the feedback loop between sales and product, and that is really a tough one to get right. It is important that you collect as much product and market data as you can, and segment it at the right levels of altitude, whether it is just clean close loss reasons or whether there are clear feature requests by market segments.

It does depend a lot on the company you are at – the size of your organisation etc. So it is a really tough one to get right because I think product organizations and revenue organizations think very differently and sometimes the conduit there isn’t always through marketing.  

What I see frequently is product thinks they are building the right thing for the market, sales doesn’t think they are building the right thing for the market and the truth is somewhere in between.

In those cases, I always fall back on the data. If the data is clean, it is hard to argue with it and I think if you are not supplying your partners, the product team with the appropriate data from, the go-to-market team, then you are not doing your job too. 

RB: One major question which I have been asking people is how do you measure success in sales rev ops?

AD: This is a really tough one. There are a lot of things that sales ops does that has direct impact on  revenue and some of them don’t.

I think there are a couple of different ways to look at it. One, you absolutely have to consider your internal customers’ NPS. If your sales organisation hates your revenue ops organisation, it is painful for everybody. 

It is hard to maintain a solid internal customer NPS because your internal customers are usually pretty tough and not always particularly understanding of your road map or competing priorities. But again, it is important that you deliver on expectations  and you maintain a strong relationship with your internal customers.

Clearly, whatever the core KPIs within a revenue organisation are, rev ops has to play an important role in and be a driver of lifting those metrics.

And last but not least, you can look for efficiencies elsewhere too because revenue ops is also connected to other functions, like product, marketing, finance etc. So there are opportunities to measure success by looking into efficiencies that you drive for these other organisations.

RB: You are the first person who has described your own sales team or your own stakeholders as a customer, which I think is very interesting.

Want to get more insights from sales ops leaders? Check out our other posts in the sales ops interview series.

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