What Is Revenue Analytics and Why Every Business Needs It

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You have all the campaign data and performance assessments, but how much value does your marketing data truly give you? The fact is that those reports on their own don’t deliver precise insights on what steps you should take next. This argument justifies why we need to talk about what is revenue analytics.

Revenue analytics looks at the entire spectrum of business data, converting it into tactical, predictive information. The importance of analytics is why McKinsey pinpoints it as a shared trait across business outperformers.

This blog takes a deep dive into revenue analytics, and it unpacks why every business needs it.

What Is Revenue Analytics?

Marketing attribution is essential for measuring campaign success; however, it doesn’t necessarily tell you what marketing levers to pull to create the results you want.

On the other hand, revenue analytics shows the revenue generated by various marketing and sales activities. It’s granular and precise, breaking revenue generation down by timeframe, campaigns, products, and individual activities.

Revenue analytics furthers your knowledge by demonstrating which efforts contributed to revenue growth. Your insights then become predictive and prescriptive.

You can accurately determine what activities to take to ramp up revenue growth. And justify marketing budgets.

Why Your Business Needs to Analyze Revenue

It should be evident that working with accurate information on which activities generate revenue will translate into outsized growth and results. This idea shows up consistently in market research.

Companies that integrate analytics into their marketing and sales operations are more likely to have higher than average growth rates.

Outliers operate with tactical precision, whereas other companies rely on informed guesswork, general market data, or subjective business intuition.

It’s one thing to purchase market insight on what customers want. However, cultivating your own revenue-oriented business intelligence is invaluable. And top growth leaders use this as the foundation for high-performing growth engine programs and decision-making.

What are your sales and marketing strategies based on? Are you working on creativity, ingenuity, skills, and talent alone? Or do you know exactly which offers, products, and messages make your customers pull out their wallets?

You already have the resource needed to put this into action. It’s the data generated by the tools, apps, and platforms you use. And if data is the oil of the digital age, then analytics is the engine you need to turn it into usable energy.

Getting Started with Revenue Analytics

So, how can you increase your understanding of what is revenue analytics, and how can you jumpstart your program? Let’s take a few recommendations from McKinsey; their analysts outlined a practical three-step framework:

1. Start With Your Existing Data

Your first priority is putting what you already have to use and working out any kinks in your fledgling system. So, keep things clean and simple at first.

Initial data sources to source from can include your:

  • CRM
  • Marketing automation tools
  • eCommerce/shopping platform
  • Sales engagement tools
  • AdTech
  • Back-office apps
  • Workflow tools
  • Customer success tools

Your core set of tools contains plenty of metrics and data to work with. Just get used to consistently collecting and using data.

2. Form New Understandings

Use your data to generate real-time insights, focusing on the data directly related to your marketing goals, sales targets, and any other critical business performance metric.

Use these insights in strategy sessions and for on-the-fly decision-making. Start evaluating the analytics you have:

  • How accurate are the results?
  • Did anything lead you off-target?
  • What do you need to refine?

3. Generate Predictions and Automate

Once you get used to working with real-time insight, you can incorporate predictive revenue analytics.

This is where things get really interesting. Your analytics should be accurate enough to inform your future direction, taking a prescriptive turn that creates added value.

After testing this out manually, you can link your analytics back into your business tools and trigger automated responses.

The Challenge? Eliminating Data Silos

So, what’s the catch? If understanding what is revenue analytics is so powerful, why isn’t everyone employing it? The main issue is accessing the information in the first place.

Transporting data between disparate apps and systems isn’t as easy as it sounds. Marketers often struggle to pull together all the existing business data to feed into their chosen analytics tool. This leaves them working with limited or partial information.

Data silos occur when information is locked into individual systems, apps, or tools. A few common causes include:

  • Tech limitations: Proprietary, closed, incompatible, or non-interoperable software.
  • Non-standardized data:  Syntactic and/or semantic information challenges.
  • Organizational culture: Overly competitive, rigid, or insular teams and departments.
  • Data hygiene is a secondary issue: Any information that doesn’t transfer cleanly is impractical for regular use.

Without a way to connect a tool or its information to the rest of your tech stack, data becomes accessible only to the individual or group of people using it.

The resulting information silos can be felt between teams and departments, eventually impairing their ability to work efficiently and effectively.

Siloes inhibit productivity, growth, and efficiency. They contribute to mismanagement, redundant work, and resource waste. Team members in organizations where information is blocked off can never understand the big picture or work cohesively together.

Are you or your team members missing critical information? Distributed digital workspaces make this the default scenario unless you’ve found a way to link your tools together.

The typical digitized workplace functions like this:

  • Marketing teams tend to generate data related to customer behaviors and interests.
  • Sales teams find out how well marketing efforts have worked, what areas are a success, what matters, and what needs to be revised.
  • Customer support teams get another view of customer complaints and service gaps.

Many organizations operate with little to no accuracy. However, fragmented views of the customer and business operations aren’t the only problem. There’s also the issue of only working with static reports.

Reports from sales, marketing, and customer success apps are helpful for periodic evaluations or for plugging into more advanced analytics tools. But these reports provide snapshots of historical insight and don’t work as a fluid part of the organization.

This is essential to keep in mind because business intelligence is an asset that works best when it’s an integral and dynamic part of operations. Apps should be able to trade information and respond to each other as needed.

Leveraging Data for Revenue Analytics

Let’s revisit the original question: What is revenue analytics? It’s going one step further than marketing attribution or performance reports with predictive and prescriptive insights that can guide your next steps.

Revenue analytics should influence your strategic plans and spontaneous decision-making. You should be able to gauge future states and pinpoint which activities should be done next.

But as we’ve seen, putting revenue analytics to use can be tricky. Implementing this takes a bi-directional information flow via a structured connection that interprets, categorizes, and cleans data in real-time.

So, how to pull this off? Right now, you have three options:

  • Wait for the software industry to become fully interoperable
  • Build your own API connections for the links you want to make
  • Use a third-party tool that connects apps, then cleans and standardizes the data

What’s your best option? Unless you have a staff of dedicated IT specialists, you need an advanced third-party tool. Often times, even if you have a  team, they aren’t always able to meet the specific needs of the marketing team.

Free Up Your Data with a Smart No-Code Solution

There has been some movement to standardize data structures and make interoperability feasible. Even so, these connections still require programming and information organization, followed by intensive data hygiene work.

Building API connections is fine for organizations that only need a couple of connections or can afford to have dedicated programmers handle this work. Otherwise, a third-party tool is your best option for accessing all the data.

Vertify’s RevOptics offers automated, predictive analytics based on your customers’ and prospects’ data. It is the single solution you will ever need to overcome your data challenges and improve sales and marketing alignment. Vertify makes freeing up data simple and cost-effective for any team. If you want to get a unified look at your internal revenue analytics, request a demo now to see how Vertify’s RevOptics can liberate your data.

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About Wayne Lopez

CPO at Vertify