How to create insights from data via Revenue Operations? Explained

To decrease the cost of your sales and increase sales in general, you need to make strategic decisions. These strategic decisions are driven by insights.

To come up with insights you will need to collect and analyze data, create reports and eventually come up with insights.

Before diving into all of this, let’s establish some definitions.

Data is unanalyzed information about an object. Through looking for observations and patterns (‘analyzing’) one will find insights: actionable opportunities coming from transforming data and making connections between data.

If your business is a Lego house, the data bricks would be data points. The individual brick has a certain value, but by combining the bricks in a certain pattern they will have an impact on your house (e.g. your ability to construct the house). That combined value, the house, is more worth than the individual data, the bricks.

Reporting, is a more ambiguous term and is often (wrongly) used as synonym for getting ‘insights’ while in most cases it’s just showcasing or visualizing different data points.

If you would have 100 bricks in 5 colors (data), a report could showcase how many bricks you have of each color, but it wouldn’t tell you how to build a Lego house.

Too often business meetings (Quarterly Business Reviews, Team Meetings) focus on reporting, which lacks the patterns impacting your business. They bucket data and provide some level of detail about the data you have available, while not providing the bigger picture of how this data impacts the business.

A good business owner or leader will focus on insights and not on reporting. Most leaders know this, but do not manage to get their team aligned with this vision.

Over the course of the past years, I spent an immeasurable amount of time in meetings full of reporting and while reporting still provides important insight in your business, it doesn’t provide the actionable opportunities.

Insights are conclusions coming from data and reporting, impacting your ability to make strategic decisions around opportunities. Through collecting data you will be able to identify patterns. These patterns will tell you what influences your business negatively (cost) and positively (revenue).

So why are insights so underutilized? The answer lies in different pitfalls but it’s mainly related to our most expensive resource: time.

Without the intention to offend anyone, one of the pitfalls around data, reporting and insights is the lack of education of people involved in the provision of them. The lack of education is mainly related to:

• Definitions
• Statistics

The first issue is related to definitions. Many of us don’t really know the true meaning of data, reporting, insights, analysis, observations etc. which leads to these terms being used interchangeably.

On top of that, we just like to use them to give (emotional) strength to the argument we are making. We are all victims (and offenders) of this. It just sounds so much more acceptable when you for example hear ‘data proves humans can’t fly’ while you don’t need data as proof but just simple physics.

Getting alignment in definitions and nomenclature is important in order to decrease time wasted.

The second gap we have is our limited education about statistics (and the wrong use of terms).

Take a 30 seconds break and do the following self-test:

Most likely one of the following words came to mind:

• Causation
• Correlation
• Sample Size
• Significance
• Distribution

A subsequent self-test would be to instantly give a definition of these terms and instantly check how accurate your definition was.

This guide is not meant as a basic course on statistics, but if you had any of the terms wrong and used it recently in a conversation, you actually told them a potential lie. Again, not here to offend anyone.

The problem is that we overuse these terms even when we do not know 100% what these terms mean. Too often, so-called ‘correlations’ and ‘causations’ are found without the necessary proof of it being a correlation.

If you have 100 employees leaving your company and you find all of them are leaving for a better paid job, it doesn’t instantly mean there is causation, correlation and significance present between ‘a better job offer’ and ‘them leaving’.

First of all, it depends on the size of your population if the sample size is even relevant. Secondly, it can be that they all left because you are just a bad employer and it’s just a coincidence they all left for a better paying job. One last time: no offense intended.

What’s the point?

The point is that you need to make the time to educate employees and especially leadership on definitions, models, analysis and coming up with relevant insights.

Most of your commercial leadership were individual contributors before: Sales Development Reps, Partner Managers, Accounts Managers, Marketeers…

For years these individuals got trained to ‘sell an idea’ and ‘drive sales at all cost’. Now they become leaders and they are asked to provide the most factual insights (and drop their sales and marketing packaging) while potentially the last time they used statistics before becoming a leader was in school. That ask is doomed without the right rewiring.

You need time to refresh their knowledge and educate them.

A second category of data, reporting and insight pitfalls are:

• Lack of hypothesis: we don’t have a critical assumption
• Overconfident assumptions: we want our assumption to be true
• Data bias: we choose data to make our insight true

All of these are again, heavily influenced by the time we have available.

The case of ‘lack of hypothesis’ is when we do not take the time to come up with a hypothesis.

Let’s go back to our Lego example: we have Lego bricks (data), we have a manual (patterns) and we know we are building a Lego house (business opportunities). In the case you miss one of these objects, you need the time to think and figure out what is missing.

The same goes for your business: if you have data and you want to find business opportunities, you need to find patterns between the data and business objectives. In order to do that, you need to make an hypothesis and test this hypothesis. This requires time.

The two shortcuts that are often observed are overconfident assumptions which are missing non-critical context or even worse the manipulation of data in order to prove outcome.

Extreme example of overconfident assumptions

We have a report of our existing business and we see that the more we meet with clients, the more our clients invest in our product. There is a causality between meeting clients and revenue. (See what I did here?)

What might be issues with this statement?

The first issue:

Saying there is a causation would mean that 1 extra customer engagement would lead to $x more in revenue. Now, what do you think would happen if I call my client every single hour of the day for a month? According to the statement above, that would positively impact my revenue, but I feel confident that data would show you otherwise.

The second issue:

What if we turn this report upside down, and we make the assumption: ‘clients spending more on our product, are more interested in talking to us’? This might be the case – which might imply that driving more customer engagement for low spending clients is actually a waste of resources.

Too often we are not critical about the assumptions we are making and excluding business and macro-economic context can be deadly for the investment decisions you make.

The other shortcut is data bias: we have an assumption and we want to prove the assumption is true. To do that, we tweak the data to be in our favor.

Extreme example of data bias

You believe ‘humanity is inherent evil’ and want to prove your insight by looking at the %-of people that committed a crime. A shortcut to prove your point is to send out a survey to ask inmates in a prison ‘if they ever committed a crime?’.

The problem here is that the sample (prisoners) is most likely not the right one for the insight related to your population (humanity).

In a business context the same mistake can be made and data bias is something that’s unfortunately omnipresent.

Once again, the resolution to many of these issues is time.

Time will allow individuals to be more critical about their assumption and to feel less inclined to alter data or omit crucial context.

If the lack of time is really the root of all evil, why don’t we solve it?

You should.

To solve the dilemma of time you need to move from reporting to revenue generating insights. The explicit blocker here is ‘reporting’.

The flow to revenue generating opportunities is traditionally:

Data < Reporting < Hypothesis < Analysis < Insights < Opportunities < Revenue

Most businesses are stuck or lose time in the second step of the process because:

• They accumulate too much useless data
• Data is dispersed across multiple platforms / software
• Most ‘analytical’ software focuses on reporting

1. From Collecting Data to Generating Revenue
Adapted from: © South Park S2E17
(Matt Stone & Trey Parker) 1998

Accumulating too much data is straightforward: gathering data for the sake of having data without a hypothesis that might use the data.

Dispersed data is an issue that arose with software springing up like mushrooms. When CRMs, SIPs, SEPs, Email Marketing, Enrichment and Scheduling software were created the founders realized they could make the cost of attrition higher by putting all of their customer data in a walled garden (which they observed worked amazingly for giants like Google, Apple, Meta, …).

The result is that all of your data is enclosed within the software you are using and data is not centralized. Your email engagement lives in Salesloft, your scheduling lives in Chili Piper, your meeting information lives in Gong, your email marketing lives in Klaviyo, your event registrations lives in Splash, your product data lives in your databases and your customer data is in your CRM.

This creates issues to understand the exact customer journey from start to finish and the process to integrate all of them (or even simply consolidate the data) is tedious. It’s one of the reasons big software companies decided to build all of this software in-house (which resulted in Google and Meta employees that never opened Salesforce or HubSpot before).

On top of that, most ‘analytical’ software (think Tableau, PowerBI, Salesforce reports) make it easier to get reporting but not extremely easy to get insights. And while the reporting is great for daily users, those who don’t open these tools often, don’t know how to get the information they need.

Because of this, teams decided to do business reviews, where weekly, monthly or quarterly defined metrics are presented in order to understand ‘how the business is doing’.

To create these reviews, time is a necessity – which is time that could be used to find insights.

The ‘insights’ team needs to copy/paste data points, tables and visualization from different software to consolidate in one snackable presentation to see the current state of business, which leads to mistakes, wrong decisions and the lack of time to provide expert advice around revenue generating opportunities.

As decreasing the cost of sales and increasing output is one of the core tasks of your (soon-to-be-hired) Revenue Operations Manager, the task of magically increasing the amount of time available fits their role and responsibilities.

Their expertise can help you to:

• Decrease the volume of unnecessary data
• Increase the availability of necessary data (covered before)
• Consolidate available data
• Facilitate faster and more accurate reporting

Difficulty: 1/5

A relatively easy responsibility of a Revenue Operations Manager is to work together with other departments (Sales, Sales Strategy and Operations) in order to define which data is useful to come up with insights around revenue generating opportunities and which data can be removed.

We all experienced annoyingly long sign ups forms wondering what the data is used for. If the answer is as easy as ‘it’s not used for anything’ there is no need to keep it.

If you don’t need to know the color of the eyes of your users, don’t ask for the information. Revenue Operations should define, in collaboration with other departments, which data is useless to gather and remove it from the sales process.

They should also safeguard the quality of necessary data in order to prevent the collection of garbage. If half of your leads are signing up with:

first name = ‘John’
last name = ‘Doe’
email = ‘email@gmail.com’

You might have work to do in order to build trust to receive real data and rethink the timing of asking your (potential) customer for their information.

Lastly, Revenue Operations should implement a systematic approach to maintaining data quality. Having a recurring plan to clean up stale data will prevent wasting time on making wrong decisions.

Difficulty: 4/5

Less easy is the task of consolidating available data. The consolidation of data requires:

• Removal of walled gardens
• Finding a unique identifier
• Transform data to be aligned

The removal of walled gardens is not something in control of your Revenue Operations Manager. Getting access to data that’s in a closed ecosystem, will heavily depend on the willingness of your software suppliers to make the data available and their ability to create a useful API* at scale.

Most of the RevOps software is still in a very infant state when it comes to data exchange. They built their product around the minimum of information they need to pull/push from/to your CRM, which makes their APIs still limited today to get all data useful for your business.

Even in a perfect world, where all APIs are mapped 100%, there would be limitations. It would require engineering resources to get all data into a central data lake, all records (mostly leads, contacts, accounts or opportunities) would need a unique identifier that is software agnostic (e.g. we need to be able to identify ‘Company X’ across all software) and all data would need to be transformed to be in the same format before it will become useful.

At this point in time, I don’t have the Holy Grail to solve this issue. The best advice is to keep data consolidation in mind when you procure new software. When you are still setting up your sales processes and the respective stack, you have the opportunity to choose software that’s aligned to your data needs.

* API = Application Programming Interface

Difficulty: 1/5

If your insights team needs to copy/paste data points, tables and visualizations in order to prepare for weekly business review presentations, you will be 100% rewarded for reading this article up until this point.

You can solve this waste of time instantly.

In order to stop this loss of time for your reporting team, we created SlideFill (which is 100% free). The software allows you and your sales, reporting, marketing and analytics teams to create presentations with parameters that will be automatically updated with your data from Google Sheets.

You will need to invest less than an hour in creating a template from an existing presentation, replacing data points with placeholders. Once this is done, you will be able to create presentations in seconds directly from your data without storing your data anywhere on our servers.

You can for example export Salesforce or Hubspot data as a Google Sheet and create a presentation from that data in 3 clicks.

This will save you hours to:

• Create data-driven presentations from scratch
• Automatically update recurring business reviews
• Customize presentations with audience specific data

No more copy/paste. No more mistakes. No more wasting time.

So… watch the catch?

There is none. As said at the beginning of this section, this part of the article is a bit more emotionally driven. Frustration drives opportunity. Over the course of the past years, I observed this was a common issue at multiple companies and we wanted to make this an issue of the past.

So… why is it free?

An ideological belief in serendipity. At this point in time, we don’t have bandwidth to commercialize, we want to create up-front value and we save hours ourselves. That’s a win.

Consider SlideFill a passionate gift and try the app here.

Based on the smart usage of qualitative data in combination with automation, you will be able to generate a unique newsletter based on your specific audience. The same magic can be used in order to come up with insights or create better customer experience.

Before you head to the conclusion of this article, you can find answers on frequently asked questions related to using insights in Revenue Operations.

To move from data and reporting to value generating opportunities you need insights. Your teams need to understand different concepts, notice potential pitfalls in advance and work towards qualitative data automation.

You want them to focus on three main areas:

Retrain your teams to make sure they understand different concepts related to data, reports, analysis and insights. Make team members focus on finding insights instead of wasting time on collecting and restructuring data.

Prevent pitfalls such as lack of understanding, bias, assumptions, lack of hypothesis and overly investing time in non value generating activities. Be critical about statements and avoid making the wrong decisions.

Work towards a decrease in terms of volume of unnecessary data. Focus on data consolidation in order to make it easier to streamline data from different tech stack and increase your teams’ ability to make strategic decisions. Use Revenue Operations to facilitate faster and more accurate reporting to open up more time to find insights.

This expert article is part of a free eBook and 7-part series addressing multiple aspects of how to do scaled business in today’s economy:

1. Lead Scoring and the impact on revenue
2. From reporting to revenue generating insights
3. Automation of revenue generating activities
4. Closing the service gap through data
5. Making marketing initiatives measurable
6. Customization of content at scale
7. Revenue Operations and generative AI

Or download the eBook for free

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Views are personal and not affiliated to employers.
No generative AI was used to write the article.
All examples are illustrative and fictional.