Defining the Future of Data-Driven Revenue Operations
We recently hosted a fantastic RevOps meetup at our office to hear from today’s rising professionals on a few top of mind topics, and share peer learnings on where the tech stack and role are at today, where the gaps are, and where things are headed. Most organizations struggle with how to manage their data and workflow, but so much of that ties into process and governance, not just technology. Our discussion dove into today’s challenges, and how modern (and less modern) companies are working to solve them.
We had three amazing speakers in Rosalyn Santa Elena & Seamus Ruiz-Earle, CRO and CEO at Carabiner Group, respectively, and also our own Aseem Chandra, Co-Founder & CEO at Immersa, a data automation platform for RevOps.
What are the high-level issues that the role is facing today?
The nature of the job points to tactics and not strategy – RevOps professionals need to act strategically in order to be considered strategic, and the function still needs work in order to evolve in that direction. The services that RevOps provide to the team need to be clearly defined and advertised (how are you enabling the business?) or the role won’t get a seat at the table, because it cannot be quantified
Data cleanliness is still a huge overarching issue – Software stacks need to be more cleanly integrated than they are today, as there are diminishing returns on efficiency if everything is poorly cobbled together. Furthermore, it’s hard to analyze data in one place across a variety of different technologies. Furthermore, it’s not just feeding in data from many different sources either, humans can be the biggest source of data corruption themselves. It’s very important that the same data format and protocols are being followed by different reps, for example
Governance consistency is not always there – What does a healthy pipeline mean from one person to the next? Where was the data being pulled from and how was it being used? Reporting is still all over the place
RevOps needs more ability to influence the economics of the funnel in real-time, not just once a year during the annual review
Data today is still not very actionable – There’s still a lot of work to be done getting data from a data lake and turning it into actions that a CSM or AE will take the next day
It’s not just about improving growth, it’s about making growth more profitable. That’s super important, especially today
It’s still challenging to scale processes, and the next level of automation is not really here yet
Problems at companies large and small often tend to be similar in this function, but larger companies that are making a shift from sales/marketing ops to RevOps need a strategic leader at the top to pull them together – this can be hard – or alternatively, the role often winds up becoming subsumed by finance. At large companies it can’t be one person leading the function either, it could perhaps be per business unit, ruled over by the COO
What is the “Data Problem”?
Data requires a holistic approach – Often, people gather a bunch of data, and then go on to do nothing with it, when the real goal should be to get the right data to the right people at the right time. The biggest problem with data today is not having a good data strategy, and not having good data governance
It’s very important to be cognizant of data sources – In WW2 they did an analysis of returning planes to see where they were shot the most, and then intended to armor those areas, until someone asked – “What about the planes that didn’t come back?” You can always control what goes in, but need to consider what things aren’t going in
Trust but verify – Where is your data coming from? How can you ensure that it’s the right data, good data, high quality/clean data? Part of this is ensuring that teams have a strict adherence to protocols and limited fields in which to enter data
More is not always better – Start with a data strategy, and focus on collecting the specific data that you need. And as mentioned before, your most corrupted data source can be your own people – emotion has to be taken out of it
Figure out which data points are the most critical and need the most governance then work your way outwards
Figure out which data sources are needed for what – People need to be aligned with how the data is being used, and they have to be guided on how to use it. A lot of communication is needed here to keep this alignment, and not just up front communication, but ongoing communication, especially as people turn over
Data interpretation is huge – A big difference between Salesforce and the modern data stack is that the modern stack has a defined data layer – you can’t cheat on your presentations and export data, then manipulate it. Even so, people will find ways to screenshot things, manually copy data, etc., and the easiest way to overcome miscommunications around how data is being used is to sit down in a room and say “What does pipeline mean to you, Rosalyn?” People need to be able to contextualize data in the context they are solving for, but also communicate that contextualization
How to deal with people coming and going – You need to get people onboard with the dictionary and governance model. You could do this via handbooks, account models, rules of engagement, an analytics committee, etc. However, the key is that this has to stay up to date, and must be constantly socialized. Maintaining discipline here is really hard
The Future of The Tech Stack
The tech stack often depends on the company’s stage and sector, but process comes before technology – At a high-level, minimal/bar bones is better. Technology should be layered on for achieving your goals over time
Have a plan when you choose a tool, don’t choose a tool and then develop a plan around it – Things need to be configured properly, the team needs to be enabled to use it, it has to stay updated. You don’t need Zuora with 10 people, just copy a template
Look at things thematically and categorically – Do you send a basic contract in 5 minutes or something super complex and custom that requires the involvement of legal, procurement, etc.
The tech stack used to be fairly simple with Salesforce, but now hundreds of companies have emerged that are defined for specific roles – There has been a proliferation of engagement apps on top of Salesforce and a fundamental shift from workflow-based apps to data-driven apps (anchored by Snowflake), and businesses have to make choices on whether or not they will just move all their data into Snowflake, or pull from many different systems – there is no one-size-fits-all answer. Startups today use about 35 different SaaS applications, and enterprise-scale companies use about 280. A lot of this is on account of the PLG movement making it very easy for individual users to sign up for things. This transformation is likely permanent – we are going to have to live with complexity, because we aren’t going back to the old days, so we have to figure out how to simplify all the noise and make things work
There are operational platforms (Salesforce) and system of record platforms (Snowflake) – It’s important to think about them this way because data in operational platforms is always ephemeral and changing – you can move it around, rip it out, no problem. But warehouse data is durable and reported over time with history. The modern data strategy is a loose coupling of systems not a tight coupling – it’s more agile and doesn’t require systems to rely on one another
When individual users bring tools into the RevOps stack they are often not strategic about integrating and leveraging those tools, if there isn’t some centralization around tech, this is what tends to lead to bloat
What are the gaps in the role today (tech or otherwise)?
There aren’t really any platforms that tell you as a rep what you should be doing next, it’s all a black box (Madkudu and Sisense are trying to solve for this). People need a “why” to help contextualize their next action – e.g. What brought someone to them? This is especially needed with junior personnel. Create trust in the data via closed loops
A modern revops team needs data engineers or direct access to these people and today a lot of teams are still only one person. Because teams are understaffed, automation is always hot
Most large enterprises are seriously lacking in technology, they are focused on digital transformation and are still a ways out from RevOps being a focus. Politics are often severe at these places too – no one wants to give up their budget
What does hiring look like in the space today?
There are way fewer qualified people on the market than you think – imposter syndrome in the industry is real. This is an opportunity for large organizations that have cash reserves to really pick up good talent and do a brain grab. 6 months ago there were 5,000 people on LinkedIn with RevOps titles and 11,000 openings, today there are 11,000 people on LinkedIn with RevOps titles and 35,000 openings – it’s going to take some time to create supply.
Furthermore, what organizations are looking for in the role is often quite different – some are really just looking for sales ops, some want a Salesforce or Marketo admin, it’s an immature role
The demand is ramping up more quickly in smaller companies but will eventually spread to larger ones – right now there’s a lot of focus on top of funnel/the PLG motion, but this may eventually spread to B2B enterprise sales
RevOps can be a slow role to ramp and learn – your job is essentially to know the customer better than anyone else at the company and that can take years and years to learn
