What is Operational Analytics? (with Examples and Use Cases)
Data Operations

What is Operational Analytics? (with Examples and Use Cases)

Aarushi singh
Aarushi singh

Companies gather more data than ever from multiple sources, so combining datasets is an ambitious and expensive undertaking.

There are challenges related to data collection, storage, access, and management. But what really matters is how well companies are able to execute based on their data.

Operational analytics allows teams to leverage disparate assets and fragmented data to build something more accurate and effective that drives growth. Teams are able to create winning strategies to stay ahead of the competition and deliver a seamless user experience.

We’ll walk you through:

  • The basics of operational analytics
  • How different teams leverage it to optimize their pipelines
  • What makes it different from business intelligence analytics
  • The different use cases of operational analytics

What is operational analytics?

Operational analytics is a category of business analytics that enables continuous monitoring of data and discovery of insights to help teams make better decisions on the fly.

In simple words, it processes real-time signals from various parts of a business to offer instant feedback. It lets you sync information directly from your data warehouse into front-end tools—Salesforce, Marketo, HubSpot, etc.— that your team uses every day.

This allows teams to accurately track data across various platforms and tools, streamline everyday business operations, improve efficiency, and enhance cross-functional team collaboration.

Why invest in operational analytics?

It’s important to use your data to stay on top of potential opportunities. You don’t want to miss out on additional profitability, higher customer lifetime value (LTV), lower customer acquisition costs (CAC), or a better app adoption rate.

Operational analytics helps you assess your existing business processes to discover such gaps, derive advanced analytics, and act upon them.

No matter how much data you collect, if the outcome isn’t valued at the outset, the lofty aspirations of “data-driven” decisions and a large amount of time will likely be shelved before you get started. By implementing operational analytics across your teams, you get access to a unified dataset and can use it to drive forward KPIs and achieve your goals.

Unlike traditional business analytics, operational analytics relies on near real-time, accurate insights with low latency data sync. This enables even non-technical teams, such as sales, marketing, customer success, etc. to leverage this operational data to deliver better customer experience and optimize existing data workflows.

When you think about operational analytics, think of cross-functional and small, tactical business decisions that take place every single day. It brings all kinds of data types together across various channels to give you insight into exactly what’s happening within each conversation, platform, and team.

This way, you can aggregate and process real-time information—like user behavior patterns, demographic details, app usage information, etc,—and act upon these advanced analytics.

3 Real-Life examples of operational analytics

Sales teams use operational analytics to close more deals

A common example of operational analytics is within the sales teams where sales reps are tasked with multiple decisions every day to help create a winning sales strategy.

By leveraging operational analytics, sales teams can measure key metrics, find hidden opportunities, identify inefficiencies, and implement strategic sales best practices based on robust, real-time data.

Let’s say, for example, that sales reps need to personalize their messaging. To do this, they need to scan different tools like Slack, Gmail, Salesforce, etc. Next they track down key information such as previous interactions with the brand, past behavior, interests, demographics, etc.

Operational analytics automatically feeds customer data from different channels to a centralized platform like a CRM. This way, the sales team can focus on delivering a delightful customer experience instead of having to work manually to sort and manage operational data.

Marketing team uses operational analytics to improve customer experiences

Operational analytics allow marketing teams to gather important information on how customers perceive their brand, how potential buyers interact with their brand, and what exactly they are looking for.

A real-life example of a marketing team using operational analytics is cart abandonment. When a user abandons their cart midway through a purchase cycle they are prompted with personalized emails or push notifications that entice them to complete their purchase.

This is possible because operational analytics syncs customer data in real-time. This allows marketing teams to improve customer experiences by tailoring their messaging based on the user’s actions or the brand’s objectives.

The bottom line is: operational analytics takes the guesswork out of the data. Marketers tap into the full functionality of their existing tools and drive better results by segmentation and personalization.

Product teams use operational analytics to derive better insights

Data is critical for product teams. It often means the difference between building features that meet customer expectations and watching poor tool adoption rates.

Using operational analytics, product teams get radical transparency into how their customers use their products and derive insights to better understand how they can further improve their customers’ experience. This way, product teams build roadmaps based on actionable user data and focus on the features they want to build next.

As customer journeys get more complex, product teams can leverage operational analytics to get fresh, accurate data that allows them to make data-driven decisions faster.

For example, when you’re aiming for app adoption or user acquisition, you want to offer your users a frictionless experience during their onboarding. Analyzing how users sign up, when they drop off during the signup process, or how they interact with your tool once they sign up—all of it can help product managers decide what functionalities/pages need to be optimized or created next.

What are the benefits of operational analytics?

  • Leverages a mix of artificial intelligence, business intelligence, and machine learning to provide the most accurate data
  • Collects and utilizes a large amount of data that may go underutilized within your decision-making process
  • Increases collaboration and communication between operations, engineers, management, marketing, sales, and C-level decision-makers
  • Streamlines and standardize internal business processes for teams with multiple stakeholders and needs
  • Enables teams to capitalize on existing tech stack and drive higher results instead of adding more tools to create complex workflows
  • Identify events in real-time and notify relevant stakeholders of necessary action that may otherwise go unnoticed
  • Helps teams quantify how existing systems can be used more efficiently and effectively
  • Enables teams to optimize their current and future data collection processes

5 Use cases of operational analytics across different verticals

Operational analytics in customer support

One of the most common use cases of operational analytics is in customer support. Employees have to collect, sort, analyze, and take action on multiple customer support tickets, complaints, and feedback.

Operational analytics allows teams to identify what needs to be addressed urgently, and prioritize tickets automatically based on different product metrics.

Operational analytics for dynamic pricing

Operational analytics leverages data to understand what influences the customers’ buying decisions and integrates this information to meet the company’s pricing goals.

A common example of operational analytics is found within SaaS companies that use a pay-per-use model. Organizations can get information like user id, service area, product usage information, and more to generate insights. This enables teams to analyze how users interact with the brand on a more granular level, segment its customer database, and decide on its pricing strategy.

Operational analytics for predictive maintenance

Operational analytics tells you a lot of things: what’s happening with your business in real-time, any errors or potential vulnerabilities in the system, and more. This is especially useful for streaming platforms like Netflix, Amazon Prime, Hulu, Twitch, etc as they want to discover maintenance issues as quickly as possible.

Another use case is in the energy industry. Devices such as oil drills, wind turbines, etc. are placed in remote locations and companies need to constantly track the health and make adjustments to these devices. Operational analytics helps stream information about the device, analyze it, and send automated alerts to notify about any issues.

Operational analytics for personalization

Delivering the right message at the right time is crucial for a better, more relevant user experience. Moreover, it helps companies entice users to take appropriate actions to meet their objectives.

With operational analytics, organizations can see exactly how consumers interact with their brand, measure key metrics, and KPIs, and use the data to provide users with incentives such as discounts, redeemable points, or exclusive offers for in-app purchases. Similarly, you could optimize your ad spend based on which audience bucket is more likely to interact with your ad.

Operational analytics for automation

Many companies leverage operational analytics to send product usage information to the product team, high-intent leads and transactions to the sales team, and cart abandonment or cross-sell opportunities to the marketing team. Using operational analytics helps keep teams up-to-date on conversations and issues which further helps teams collaborate more effectively.

How to get started with operational analytics

When you have an array of platforms with massive amounts of data, it’s easy to get lost in the noise. Operational analytics helps teams to focus on the meaningful metrics that matter by giving them a real-time view of what’s happening and identifying a strategic path to take.

Operational analytics doesn’t require you to redesign your entire tech stack. Rather, take small steps and build efficient processes that help your team continually leverage your data to improve operations without getting overwhelmed by it.

Want to learn more about how you can implement operational analytics in your organization?  Schedule some time with our data experts to help you walk through how you can implement operational analytics effectively.

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