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Revenue Operations
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Hiring a Revenue Operations Professional: Data Analyst

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Welcome to the third article in a multi-part series that will help you find the perfect fit for your revenue operations team. Because revenue operations often spans technical system administration, data analytics, enablement, process management, and project management, we’ll break down the different functions to cover them in more detail.

While it is possible to find someone who can pull off all of these functions, they are the employee equivalent of a sparkling unicorn. We feel obligated to point out that placing the burden of managing all of these components on one human will not set them up for success. 

How Experienced Should My New Hire Be?

The level of experience you require should depend on how complicated your technical stack is—by complicated, we mean the number of non-integrated systems and how different their data language is—and the number of responsibilities that will fall on the analyst.

Unfortunately, the most complicated environments tend to be startups that either don’t have the necessary budget or priorities to hire a seasoned professional. 

Why are these environments so complicated? We’re so glad you asked! 

It’s because they don’t have the necessary budget or priorities to purchase tools that integrate or the infrastructure needed to make those tools talk nicely to one another.

This typically impacts marketing more severely than sales or customer success. 

Sales does most of their work in a CRM, and a great deal of effort will go into analyzing the data, determining its accuracy, and trying to find ways to get the sales team to fill in the gaps with better data entry or automation. But most reporting can be achieved in Salesforce with the help of a gifted CRM administrator. Adding on forecasting tools and commission software will usually get you the final mile. 

Customer success is generally more complex than sales, but the number of systems they span are limited compared to marketing. You may need to crunch numbers in more spreadsheets, buy some connectors that feed real-time data, or look at a database solution. But more often than not, you can do most of what you need in your CS system.

According to, on average, marketers use a staggering 91 cloud-based applications.

Ideally, a senior analyst should be hired to support marketing. They have the most difficult time proving their contribution to the business because their tech stack is scary, and they produce many activities before a sale closes. In other words, the buyer journey is not linear—one campaign interaction rarely leads to one sale, particularly for big-ticket items and B2B sales. 

Senior analysts should also be hired if you need to determine the infrastructure necessary to meet current reporting requirements and you want them to anticipate what you will need to do today to meet future business requirements. If an analyst has 5+ years of experience, they’ve probably seen a good range of reporting requirements. This person is also better suited to setting data definitions and socializing new reporting methodology with the rest of the organization, which is absolutely critical if you want to establish trust with your finance team.

What Kind of Experience Should Candidates Have?

As with so many things in revenue options, the answer is:

It depends.

We would argue that a talented data analyst can adapt to new departments, but they will initially require more context as they become familiar with the business unit’s idiosyncrasies. Personally, I would rather have someone on my team who knows how to ask the right questions to get to the bottom of what someone is really looking to solve than hire an analyst who spends the majority of their time making the report look visually appealing (not that there isn’t value in aesthetics!).

If I use SQL Server and need someone to support my customer success organization, I would absolutely consider hiring a financial analyst with four years of experience in Microsoft SQL Server who was keen to learn more about customer success KPIs. I would not be as excited if a candidate had six years of customer success reporting experience and preferred to run reports in Salesforce because they aren’t super comfortable in Excel.

Your requirements become more straight-forward if you have a mature build-it-yourself data infrastructure. You’ll need to hire someone who knows the languages and data architecture common to those environments. A very junior analyst may have experience with Excel and the user interface of more complex tools. Still, unless you hit the recruiting lottery, they probably won’t be able to support a build-your-own customer data platform on their own.

Hiring an Advanced Data Analyst

The Technical Requirements

Carefully consider your technical stack before writing up a job description for a data analyst. Suppose the business knows they need someone to figure out reporting from the ground up. In that case, you’ll need someone well versed in tools similar to what the department(s) they will support use, data visualization tools, and preferably some query language experience. If you already have your infrastructure in place, your requirements are decided for you.

Let’s pretend we have Salesforce, Marketo, and a bunch of other marketing tools. We also have an executive team that is frustrated by the lack of metrics coming out of marketing. Add on the complicating factor that sales and marketing tend to point fingers at one another when the quarter doesn’t do well.

In this case, a shortlist of requirements may look like this:

  • 3-5 years as a data analyst supporting marketing or sales organizations
  • Experienced user of Tableau, PowerBI, or equivalent data visualization software
  • Able to map data schemas and explain how data flows into external reporting systems
  • Deployed marketing automation platform reports and/or Salesforce reports and dashboards
  • Has successfully rolled out attribution reporting and engagement scoring
  • Is familiar with rhythm of the business reporting and development of KPIs and OKRs
  • Is comfortable reviewing data with an executive team
  • Very experienced at gathering requirements and socializing reporting methodologies across functional teams
  • Bonus: Experience with a query language
  • Bonus: A certification (or two) in any major data platform

If you can afford to have a data scientist on your team, kudos. For the rest of us, there are many analysts out there with less formal schooling that still can make a big impact on your team.

The Questions

Q: Describe the demand generation waterfall and whether you recommend using it for our organization.

Why it’s asked: A demand generation waterfall spans both marketing and sales, helping the teams record buyer journey milestones. It also helps determine conversion rates throughout the funnel, which are great indicators for sticking points in the buyer journey. For example, if the conversion between marketing qualified lead and sales accepted opportunity is low, sales and marketing probably aren’t aligned on what a qualified lead looks like.

Example Answer: The demand generation funnel tracks a buyer as they enter awareness (usually this begins when a name is captured or web activity data is recorded) through to a sale. When deploying the demand generation waterfall, there are considerations to make, including whether or not there’s an intermediary team between marketing and sales (business development representatives or inside sales representatives) and accounting for people who exit and reenter the buyer journey. I’ve seen companies replicate contact records to keep track of new cycles. I’d much rather find a different way to track non-linear journeys by either using the campaign member object or creating a separate object that can track how many times someone bounces in and out of the sales cycle without duplicating them.

Q: Do you have any certifications?

Why it’s asked: This may not be a hard requirement. Suppose you have a business intelligence team supporting your department and need someone to pass requirements and manipulate the final data output. In that case, you probably only need someone who is very good at Tableau, Excel, and maybe a few other tools—not necessarily a query language.

Example Answer: While I don’t have my Microsoft Data Analyst Associate degree, I have taken several courses on T-SQL and am a Certified Analytics Professional through the University of Washington.

Q: Describe to me the difference between a left join, right join, and inner join.

Why it’s asked: If you have a relational database, it’s important your analyst understands how different joins impact data output.

Example Answer: A left join returns all the data in the left table and only data in the right table that overlaps. A right join returns all the data in the right table and only data in the left table that overlaps. An inner join only returns data that overlaps. You’d use the last kind of join if you only wanted opportunities that had a specific primary campaign, for example.

Q: If you had multiple disconnected systems but didn’t have the budget or approval to put a database or data lake in place, how would you go about pulling reports?

Why it’s asked: It’s important to know how someone will adapt if conditions aren’t what they prefer.

Example Answer: I suppose I would start with trying to choose a primary data location between the systems I do have. There are workflow tools like Zapier and Workato that could help solve some of the issues, depending on the type of data housed in these disconnected systems. For example, we could probably find parameters to port a direct mail campaign activity to a contact in Salesforce. For something as massive as web data, I would probably encourage the marketing team to put UTM tracking in place to get a better picture of form fills, pixel tracking in place for social media, and hope for the best. Putting that amount of data in a spreadsheet simply isn’t feasible.

Q: When you’ve supported customer success in the past, what kind of metrics did you measure?

Why it’s asked: It’s important to understand the amount of experience someone has with the department they’ll be supporting.

Example Answer: It depends on the organization we’re talking about. At company A, customer success was responsible for renewals and upgrades. In addition to your typical churn, retention, LTV stats, we also created a book of business model. Honestly, it wasn’t without its issues because management frequently had to shift around accounts throughout the quarter, so the territory “snapshot” for the book of business was always in flux. At company B, the customer success team didn’t participate in sales beyond passing leads. I helped with some spiff programs, but for the most part, I was focused on more traditional metrics for quarterly meetings. LTV, NPS, Churn, and MRR.  Monthly, we reviewed satisfaction scores, ticket volume, and other metrics summarized by representative.

Finding a Jr. Analyst

The Technical Requirements

Many analysts stumble onto their position accidentally… Or get bored with finance but want to continue working with data. If you’ve spotted someone who can spot patterns in data and has a good instinct for asking the right questions, you may have found your next analyst.

If someone is interested in getting into data analysis, start with salesforce reports and data hygiene. If they enjoy finding ways to more efficiently find problems and are proactive about finding formulas and fixes on YouTube or through Google Search, you’ve someone with aptitude.

Soft Skills

As with many jobs, soft skills can make or break a career. I’ve worked with analysts who don’t like dealing with people and decide to become database administrators or developers. They want an analyst between them and the end customer with the requirements already ironed out. We need these skilled positions, so I view this as a good thing.

For analysts who work with end-users to determine requirements and produce a dashboard or requested report, soft skills are critical. They have to be diplomatic about prioritizing, know how to dig under the surface to get to the deeper issues that need solving and be borderline obsessive about reporting issues as soon as they’re spotted, particularly if the issue is due to a mistake in their calculation. 

The Questions

Q: Name a time you made a mistake and explain how you handled it?

Why it’s asked: I want to know that my future employee believes that bad news must travel faster than good news, but it’s the truth.

Answer: Instead of calculating the quarterly churn rate, I reported the yearly number. Unfortunately, this was in the first quarter, so it looked like we had done an excellent job retaining customers. The truth was less than stellar. As soon as I found out, I spoke with my manager, and we worked on an email I sent out to get the correct information in front of the management team. We had a few people who weren’t happy, but it was better to tell them right away than to have a shock in Q4.

Q: How do you prioritize your work?

Why it’s asked: Younger employees tend to either take on too much or tell people no without explaining. Both are coachable, but I want to know that they’re comfortable asking questions and know to align priorities with company goals.

Answer: I prefer to meet with my manager weekly to review my priorities to make sure we’re on the same page. I don’t always know when a strategy has changed or people have deprioritized a product—getting an opportunity to sync cuts down on those gaps.

Q: Tell me about a time you had to tell someone requesting a change, “No.”

Why it’s asked: Emotional intelligence matters. I care more about how they handled the interaction with the person than why they said no.

Answer: A manager wanted to include website data in our attribution reporting. We didn’t have a way to connect to the platform in a way that was scalable. I told them we had to either invest in a tool to help us translate the data into something usable, use more diligent tracking methods on form fills, or use the reporting we had in place today. Ultimately, they decided not to dedicate more budget to solving the problem at that time.

The Profile of a Rockstar

People think of data analysts as rigid, right-brain thinkers. In reality, the most successful analysts are creative. They very seldom accept that something is not achievable and dig in until they find the result.

The rockstars have high emotional intelligence and strong ethics. If they find something wrong in the data, they know how to let the impacted team know and when to escalate the issue further up the food chain. Most importantly, they’re able to see the bigger picture and understand how what they’re doing supports the business as a whole.

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