Defining the Future of Data-Driven Revenue Operations

ASEEM CHANDRA | JUNE 16, 2022

Event: Defining the Future of Data-Driven Revenue Operations
When: May 27, 2022
Where: Mayfield Meetup
Hosted By: Mayfield Fund, Carabiner Group, Immersa

As a SaaS company, it can be easy to always focus on the new, bright and shiny marketing or sales campaign. The ones who truly get it and thrive though are the ones who recognize the value of building a solid revenue operations team to influence the flow of leads to sales and find new opportunities to bring revenue into the business.

This and many others were the topics of the Forum on Revenue Operations held at Mayfield offices with three great speakers. 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.

Among the companies represented at this forum were revenue operations, sales, marketing and CS leadership across AppDynamics, AWS, Carabiner Group, DuploCloud, Gated, Houzz, Immersa, Impira, LeanData, Legion Technologies, Lob, NexHealth, Mayfield, Paystand, Qventus, Redis, salesDNA, Skilljar, Snowflake, Workato, Workspan.

The conversation between these great leaders with decades of product and rev ops experience revolved around 5 main topics.

High level issues facing the RevOps role today
What is the “data problem”
The future of the tech stack
Gaps in the RevOps role
How to hire for the Rev Ops role

High-level issues facing the RevOps role today

As RevOps continues to take hold in SaaS companies there are many issues facing the funding of the role today.

Tactics over strategy – many times the insights that RevOps gives are more direct and stats driven, leaving it to other professionals to come up with the why and how that information is important. 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. Leadership can help with this by enabling RevOps professionals to be decision makers and even be a harbor for building programs and passing them on to other orgs once they are ready to scale. As Revops team evolve to provide the insights based on the data in a business context, they will have more “access to the funnel” that they desire.
Data cleanliness – to provide good insights and lead strategic decisions, RevOps needs to make sure that the data is actually valid. The old adage of “garbage in, garbage out” is still alive and well with data. Software stacks across multiple SaaS applications need to be more cleanly integrated than they are today. Finding ways to bring the data into one “central hub to rule them all” and then ensuring the removal of duplicates and other garbage will ensure that the insights are valid. The data also needs to be actionable.
Documentation and Governance – its easy to do something once, but sometimes harder to document and write down how and why you did it so it can be repeated. Currently consistency is not always there when it comes to RevOps– 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. Creating consistent reporting that teams can count on will help to build credibility and influence over the funnel.
Scale and process – RevOps is not just about improving growth, it’s about making growth more profitable. That happens with scaling processes. RevOps leaders need to find ways to integrate other technologies to automate processes that require fewer skilled data analysts to analyze. This will free up the RevOps team to provide more strategic insights to lead decisions. This is especially crucial with large organizations where orgs can be more complicated.

What is the data problem?

Data is always an issue when it comes to operations roles. Here are three key tips that were talked about:

Have a holistic approach – The data cycle should be focused on having the right data available, at the right time, and delivered to the right people. Too often companies will do only one or even none of these very well. For a RevOps team to succeed it needs to be focused on each of the three areas of data and make sure it has a process to make the best out of all of them. Data governance and ownership can help to improve the holistic approach.
More the better, but make sure it’s good – The more the merrier when it comes to data. It will only improve the insights that come from it. However, it’s important to make sure that it’s coming from the right sources and is leading to the right outcomes. You may want to ask, 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. Answering these will help you to come up with a strong data strategy that will ensure good data coming in and good insights going out.
Interpret correctly – Governance and process that is owned by RevOps and broadly shared within the organization will ensure that a PQL is the same to sales as it is to product as it is to marketing. When definitions of what a metric is are broadly shared you can better control insights and reduce misinterpretations of data.

The Future of The Tech Stack

The tech stack for a SaaS business has drastically changed over the last decade.Once single CRM like Salesforce now has hundreds of potential integrated products. Here are two main things to consider with your emerging tech stack.

Have a plan – Developing a strategy with how and when you buy technology will scale down the endless possibilities you can research. Things need to be configured properly, the team needs to be enabled to use it, it has to stay updated. You don’t need to deploy billing software if you are billing 10 customers, just use a spreadsheet and an invoice 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.
Process and governance – It can be easy to get quickly bloated when it comes to products, especially in martech. Having a process for how products are tested and purchased as well as a central operator and approver will help to limit that. 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.

What are the gaps in the role today (tech or otherwise)?

There are a lot of gaps that we have already talked about above.

Process, planning
Strategic insight
Clean data

Some other ones to consider are 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 the insight to help lead their action – e.g. What brought someone to them? This is especially needed with junior personnel. Create trust in the data via closed loops.

Data engineers and scientists can be a huge help to closing the loop on data and providing the insights needed for the RevOps team. That paired with the right technology and data storage can be incredibly powerful.

Hiring is another huge gap with ops in general. It is a fairly new category even though people have been doing it for a long time. As it gets more defined and valued in organizations the talent will begin to flow in. Until then it might be worth identifying the skill sets you want (data background, finance background, etc.) and developing those people into rev ops leaders.

Summary

The future of RevOps is bright. When invested in with the right people, the right technology, and the right data it can be a driving force to building your business to the next level.