Operational analytics: A mini guide for data managers and analysts

Data provides invaluable performance insights about your customers. However, analyzing metrics and KPIs only goes so far if you don’t take action on the information. Operational analytics helps put your data to practical use in real time. Learn how this can help you achieve your company benchmarks in a shorter time.

What is operational analytics?

Operational analytics’ meaning is not to be confused with traditional analytics. The latter pertains to data analysis. A customer data tool collects information from different data silos, integrates them, and compiles them into a report. This provides performance measurements like ROI, bounce rate, page views, etc. 

This is useful information, but by itself, it doesn’t improve high-value benchmarks like customer acquisition and revenue. 

This is where operational analytics comes in; this is the “taking action” part of data analysis. Operational analytics syncs your data warehouse with the business planning tools used by the sales, marketing, and HR team. In this manner, these teams have immediate access to the data without having to go through the IT department. It helps teams put the data into action through automation and AI-assisted systems.

Operational analytics example

Let’s say your website has a high bounce rate. First-time visitors tend to leave the site after a short stay without navigating to other pages. Your goal is to reduce bounce and increase site engagement. With an operational analytics tool, you can automate the system to send a pop-up message encouraging the visitor to explore the site further. 

For instance, you might set the pop-up to appear for departing visitors who meet these conditions:

• Have not browsed beyond the page they landed on
• Stayed on the site for less than 60 seconds
• Scrolled halfway or less on the site

The pop-up ad may include a trial offer, a product recommendation, or a chat window to speak with a live sales rep.

Why use operational analytics?

Operational analytics enables decision-makers to take constructive action with the data in front of them. In this sense, the data is no longer just a performance report relegated to charts or numbers on an Excel sheet. Here are a few advantages of operational analytics:

1. Make decisions in real time

Companies may analyze their analytics in the form of monthly or quarterly reports. However, so much opportunity and time is lost when the analysis is far and between. With real-time operational analytics, you can take actionable steps through automation—right as the data enters your data warehouse.

2. Improve the customer experience

Provide a more tailored and data-driven experience based on customer behavioral profiles and site interaction. The automated actions by the AI are immediate and not after the fact, leading to better conversions.

3. Increase productivity 

Identify bottlenecks in your workflow and take corrective action through an automated solution. For example, if, through analytics, you identify a longer-than-average order processing time, you can propose solutions to automate a portion of the process or strip out unneeded steps altogether. 

Operational analytics for different teams

Different teams will find different applications for operational analytics. Here’s how individual departments can make use of the system:

Marketing team

Marketing teams can tailor their ads depending on customer response. Through A/B testing and metric analysis, marketers can determine in real time the subject lines, meta titles, and calls to action that have the highest click rates and adjust their campaigns accordingly.

When testing email subject lines, for example, input the varying subject lines into the system, and the operational analytics AI will swap them out based on performance. Through machine learning, the system can detect which subject line is performing better among varying customer demographics by gender, geolocation, income level, etc.

Sales team

Sales teams can leverage operational analytics for better conversions and to boost customer lifetime value. To combat the cart abandonment rate, for example, sales reps can employ automated email reminders for customers to complete their transactions.

Operational analytics is also useful in freemium services. In freemium games, for instance, the AI can send limited-time discounts on in-game currency when players perform specific actions or achieve special milestones, such as leveling up or completing a stage.

Customer success team

The customer success team is similar to the sales team but is more oriented toward customer retention. Operational analytics can detect actions that may indicate a probable churn. In a SaaS company, customers may be at risk of churning if they fail to log in for a set number of days or switch off auto-billing. Retention measures can automatically kick in when the AI detects these churn behaviors. Automated actions may include invitations to discounted premium memberships or an offer for a live one-on-one consultation.

Invest in the right operational analytics tools

Operational analytics is only as effective as the tools you implement. For real-time data reporting and automated action, make Cloud Connect your primary data integration system. Sync your data warehouse with your frontline tools—all with the click of a button. Get started with Lytic’s Cloud Connect today.